过程挖掘类论文

摘要

[1] A data warehouse for workflow logs (2002)

(Eder, Johann and Olivotto, Georg E. and Gruber, Wolfgang | )

Abstract: Workflow Logs provide a very valuable source of information about the actual execution of business processes in organizations. We propose to use data warehouse technology to exploit this information resources for organizational developments, monitoring and process improvements. We introduce a general data warehouse design for workflow warehouses and discuss the results from an industrial case study showing the validity of this approach.

摘要： 工作流日志提供了有关组织中业务流程的实际执行的非常有价值的信息源。我们建议使用数据仓库技术来利用此信息资源进行组织开发，监视和流程改进。我们介绍了用于工作流仓库的常规数据仓库设计，并讨论了工业案例研究的结果，这些结果表明了这种方法的有效性。

下载地址 | 返回目录 | [10.1007/3-540-45785-2_1]

[2] A systematic mapping study of process mining (2018)

(Maita, Ana Rocio Cardenas and Martins, Lucas Corr^ea and Lopez Paz | )

Abstract: This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced ‘discovery (71%) as the main type of process mining addressed and ‘categorical prediction (25%) as the main mining task solved. The most applied traditional technique is the ‘graph structure-based ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are ‘evolutionary computation (9%) and ‘decision tree (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.

摘要： 该研究系统地评估了2005年至2014年的过程挖掘场景。对705篇论文的分析表明，发现（71 ％）是过程挖掘的主要类型，而类别预测（25 ％）作为主要的挖掘任务得以解决，最常用的传统技术是基于图结构的技术（38 ％），特别是在计算智能和机器学习技术方面，我们得出的结论是几乎没有相关性对他们来说，应用最多的分别是进化计算（9 ％）和决策树（6 ％）。过程挖掘的挑战，例如在鲁棒性，简单性，准确性和泛化性之间的平衡，可以从更多使用此类技术中受益。

下载地址 | 返回目录 | [10.1080/17517575.2017.1402371]

[3] Advanced Information Systems Engineering (2009)

(Planas, Elena and Cabot, Jordi and Gomez, Cristina | )

Abstract: MDD and MDA approaches require capturing the behavior of UML models in sufficient detail so that the models can be automatically implemented/executed in the production environment. With this purpose, Action Semantics (AS) were added to the UML specification as the fundamental unit of behavior specification. Actions are the basis for defining the fine-grained behavior of operations, activity diagrams, interaction diagrams and state machines. Unfortunately, current proposals devoted to the verification of behavioral schemas tend to skip the analysis of the actions they may include. The main goal of this paper is to cover this gap by presenting several techniques aimed at verifying AS specifications. Our techniques are based on the static analysis of the dependencies between the different actions included in the behavioral schema. For incorrect specifications, our method returns a meaningful feedback that helps repairing the inconsistency. textcopyright 2009 Springer Berlin Heidelberg.

摘要： MDD和MDA方法需要足够详细地捕获UML模型的行为，以便可以在生产环境中自动实现/执行模型。为此，将动作语义（AS）作为行为规范的基本单位添加到UML规范中。动作是定义操作，活动图，交互图和状态机的细粒度行为的基础。不幸的是，当前致力于行为模式验证的建议往往会跳过对它们可能包含的动作的分析。本文的主要目的是通过提出几种旨在验证AS规范的技术来弥补这一空白。我们的技术基于对行为模式中包括的不同动作之间的依赖性的静态分析。对于不正确的规格，我们的方法将返回有意义的反馈，以帮助修复不一致问题。 textcopyright 2009年施普林格柏林海德堡。

下载地址 | 返回目录 | [10.1007/978-3-642-02144-2]

[4] Analogize process mining techniques in healthcare: Sepsis case study (2017)

(Kukreja, Guneet and Batra, Shalini | )

Abstract: Process mining is a bridge between performance and compliance or super glue between process and data oriented analysis. Healthcare organizations are facing the challenges to extract dynamic, complex and cross-functional processes. The issues related to improper data management, collaboration and coordination make the careflows convoluted to perceive. In this paper, we are examining the event log embracing the events of sepsis cases from a hospital. Sepsis is a life threatening condition typically caused by an infection. The events were recorded by the ERP system of the hospital. The research objective is to investigate this healthcare process for two stances: control flow and conformance perusal. Varied process mining techniques are used for exploration and collation of their operation with the event log. We have extracted the useful facts from event logs in form of MXML/XES format, importing in a PROM framework and scrutinize the pathways followed by distinct processes. The results of these stances provide new discernment that facilitates the refinement of the prevailing procedure.

摘要： 流程挖掘是性能和法规遵从性之间的桥梁，或者是流程和面向数据的分析之间的完美结合。医疗保健组织面临着提取动态，复杂和跨职能流程的挑战。与数据管理，协作和协调不当相关的问题使护理流程变得难以理解。在本文中，我们正在检查事件日志，其中包含医院败血症病例的事件。败血症是通常由感染引起的危及生命的状况。这些事件由医院的ERP系统记录。研究目的是从两个方面研究该医疗过程：控制流程和合规性研究。各种过程挖掘技术用于探索事件并与事件日志进行核对。我们从事件日志中以MXML / XES格式提取了有用的事实，将其导入PROM框架并仔细研究了不同过程所遵循的路径。这些立场的结果提供了新的认识，有助于简化现行程序。

下载地址 | 返回目录 | [10.1109/ISPCC.2017.8269727]

[5] Automated Discovery of Process Models from Event Logs: Review and Benchmark (2019)

(Augusto, Adriano and Conforti, Raffaele and Dumas, Marlon and La Rosa | )

Abstract: Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

摘要： 流程挖掘使分析师能够利用业务流程的历史执行日志来提取有关这些流程的实际性能的见解。自动化过程发现是研究最广泛的过程挖掘操作之一。自动化的流程发现方法将事件日志作为输入，并生成业务流程模型作为输出，该业务流程模型捕获事件日志中观察到或隐含的任务之间的控制流关系。在过去的二十年中，已经提出了各种自动过程发现方法，从而在结果模型的可伸缩性，准确性和复杂性之间取得了不同的权衡。但是，这些方法已经采用不同的数据集，实验设置，评估方法和基线以临时方式进行了评估，由于使用封闭的数据集，通常会得出无法比拟的结论，有时甚至无法再现结果。本文使用开源基准，提供了对自动化过程发现方法的系统评价和比较评估，涵盖了12个公开可用的现实事件日志，12个专有的现实事件日志和9个质量指标。结果突出表明了该领域的差距和未探索的折衷，包括某些方法缺乏可扩展性，并且相对于所使用的不同质量指标，它们的性能差异很大。

下载地址 | 返回目录 | [10.1109/TKDE.2018.2841877]

[6] Business Process Management (2013)

(Leopold, Henrik | )

Abstract: Business process management (BPM) represents one of the core concepts enabling companies to flexibly react to the constantly changing business environment. The actual relevance of business process management is, for instance, illustrated by the size of the BPM software market. A recent study of Global Industry Analysts forecasts that the global market for BPM software will reach a volume of over 5 billion US dollars by the year 2017 18. The importance of BPM in academia is demonstrated by its constant presence among top-ranked information system conferences 32, 2, 1, 271. In fact, this also highlights that BPM has become one of the core areas of information systems research. The range of addressed topics goes from general organizational aspects of BPM to specific technical issues concerning business process models. Due to the importance of business process models for documenting and redesigning the operations of companies, many researchers have focused on aspects of process model design and process model quality. Nevertheless, there are still many significant aspects that have not been addressed by prior research.

摘要： 业务流程管理（BPM）代表了核心概念之一，使公司能够对不断变化的业务环境做出灵活的反应。业务流程管理的实际相关性例如由BPM软件市场的规模来说明。 Global Industry Analysts的最新研究预测，到2017年，BPM软件的全球市场规模将超过50亿美元18。 BPM在学术界的重要性通过其在一流信息系统会议中的持续存在得以证明32，2，1，271。实际上，这也凸显了BPM已成为信息系统研究的核心领域之一。涉及的主题范围从BPM的一般组织方面到涉及业务流程模型的特定技术问题。由于业务流程模型对于记录和重新设计公司的运营非常重要，因此许多研究人员将重点放在流程模型设计和流程模型质量方面。尽管如此，仍有许多重要方面尚未被先前的研究解决。

下载地址 | 返回目录 | [10.1007/978-3-319-04175-9_1]

[7] Determining the number of trace clusters: A Stability-based approach (2016)

(De Koninck | )

Abstract: Given the complexity of real-life event logs, several trace clustering techniques have been proposed to partition an event log into subsets with a lower degree of variation. In general, these techniques assume that the number of clusters is known in advance. However, this will rarely be the case in practice. Therefore, this paper is the first to present an approach to determine the appropriate number of clusters in a trace clustering context. In order to fulfil this objective, a stability-based method for identifying the most appropriate number of trace clusters is proposed. The method involves the design of tailored resampling strategies and cluster similarity metrics. Regarding practical validation, our approach is tested on multiple real-life datasets to investigate the workings of the different components. Our results suggest that our method is successful in identifying the right number of trace clusters.

摘要： 鉴于现实事件日志的复杂性，已经提出了几种跟踪聚类技术，以将事件日志划分为具有较低变化程度的子集。通常，这些技术假定簇的数目是预先已知的。但是，实际情况很少如此。因此，本文是第一个提出在跟踪聚类环境中确定适当数量的聚类的方法。为了实现此目标，提出了一种基于稳定性的方法，用于识别最合适数量的跟踪群集。该方法涉及定制的重采样策略和聚类相似性度量的设计。关于实际验证，我们的方法在多个真实数据集上进行了测试，以调查不同组件的工作情况。我们的结果表明，我们的方法成功地确定了正确数量的跟踪簇。

下载地址 | 返回目录 | []

[8] Discovering block-structured process models from event logs containing infrequent behaviour (2014)

(Leemans, Sander J.J. and Fahland, Dirk and van der Aalst, Wil M.P. | )

Abstract: Given an event log describing observed behaviour, process discovery aims to find a process model that best describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm returns a sound model in all cases (free of deadlocks and other anomalies), handles infrequent behaviour well and finishes quickly. We present a technique able to cope with infrequent behaviour and large event logs, while ensuring soundness. The technique has been implemented in ProM and we compare the technique with existing approaches in terms of quality and performance. textcopyright Springer International Publishing Switzerland 2014.

摘要： 鉴于事件日志描述了观察到的行为，过程发现旨在找到一种最佳描述此行为的过程模型。已经提出了各种各样的过程发现算法。但是，没有一种现有算法在所有情况下都返回一个声音模型（没有死锁和其他异常），能够很好地处理不频繁的行为并快速完成。我们提出了一种能够在确保稳健性的同时应对不频繁的行为和大事件日志的技术。该技术已在ProM中实现，并将该技术与现有方法进行了比较质量和性能方面。 textcopyright瑞士Springer国际出版，2014年。

下载地址 | 返回目录 | [10.1007/978-3-319-06257-0_6]

[9] Discovering the Glue connecting activities: Exploiting monotonicity to learn places faster (2018)

(van der Aalst, Wil M.P. | )

Abstract: Process discovery, one of the key areas within process mining, aims to derive behavioral models from event data. Since event logs are inherently incomplete (containing merely example behaviors) and unbalanced, this is often challenging. Different target languages can be used to capture sequential, conditional, concurrent, and iterative behaviors. In this paper, we assume that a process model is merely a set of places (like in Petri nets). Given a particular behavior, a place can be fitting, underfed (tokens are missing), or overfed (tokens are remaining). We define a partial order on places based on their connections. Then we will show various monotonicity properties that can be exploited during process discovery. If a candidate place is underfed, then all lighter places are also underfed. If a candidate place is overfed, then all heavier places are also overfed. This allows us to prune the search space dramatically. Moreover, we can further reduce the search space by not allowing conflicting or redundant places. These more foundational insights can be used to develop fast process mining algorithms producing places with a guaranteed quality level.

摘要： 过程发现是过程挖掘中的关键领域之一，旨在从事件数据中得出行为模型。由于事件日志本质上是不完整的（仅包含示例行为）且不平衡，因此这通常具有挑战性。可以使用不同的目标语言来捕获顺序，条件，并发和迭代行为。在本文中，我们假设过程模型仅仅是一组位置（如在Petri网中）。给定特定的行为，地点可以是合适，供不应求（缺少令牌）或供过于求（保留令牌）。我们根据地点的联系来定义地点的部分顺序。然后，我们将展示在过程发现过程中可以利用的各种单调性。如果候选地点供餐不足，则所有较轻的地点也供餐不足。如果候选地点供餐过多，那么所有较重的地点也将供餐。这使我们可以大大减少搜索空间。此外，我们可以通过不允许出现冲突或多余的位置来进一步减少搜索空间。这些更基础的见解可用于开发快速过程挖掘算法，以产生具有保证质量水平的场所。

下载地址 | 返回目录 | [10.1007/978-3-319-90089-6_1]

[10] Fraud detection on event logs of goods and services procurement business process using Heuristics Miner algorithm (2017)

(Rahmawati, Dewi and Ainul Yaqin | )

Abstract: Event logs are history records that contain sequence data for the activity of a case that has been executed by an information system. Event logs can be valuable information with a technique called mining process. With this technique, cheating on the business processes of an enterprise can be detected early on. Thus, the company can commit further examination of business processes, especially the business process of procurement of goods and services to achieve business process is expected.8 In this study, management data of event log obtained from log data at each event transaction procurement and services. The event log data is then analyzed using a heuristic miner algorithm. Heuristics miner algorithm chosen because it has advantages that are not owned by Alpha++ algorithm that this algorithm can calculate the frequency relation between activities in the log to determine the causal dependency. Heuristic Miner can be used to determine the predominant process of thousands of logs and detect behaviors that are not common in a process.11 This study aims to detect anomalies on business processes that occur during the process of procurement of goods and services by calculating the fitness value of the event log into the system. Heuristic miner algorithm using the results obtained identification accuracy of 0.88%.

摘要： 事件日志是历史记录，其中包含由信息系统执行的案件活动的顺序数据。使用称为挖掘过程的技术，事件日志可以成为有价值的信息。使用此技术，可以及早发现对企业业务流程的作弊行为。因此，公司可以对业务流程进行进一步的检查，特别是期望实现商品和服务采购的业务流程。8在这项研究中，事件日志的管理数据是从每个事件事务采购和服务处的日志数据中获取的。然后使用启发式挖掘器算法分析事件日志数据。选择启发式矿工算法是因为它具有Alpha ++算法不具备的优势，该算法可以计算日志中活动之间的频率关系以确定因果关系。启发式Miner可用于确定成千上万条日志的主要流程，并检测流程中不常见的行为。11本研究旨在通过计算事件日志进入系统的适用性值，来检测商品和服务采购过程中发生的业务流程异常。使用该结果的启发式挖掘器算法获得的识别精度为0.88 ％。

下载地址 | 返回目录 | [10.1109/ICTS.2016.7910307]

[11] Genetic process mining (2005)

(Van Der Aalst | )

Abstract: The topic of process mining has attracted the attention of both researchers and tool vendors in the Business Process Management (BPM) space. The goal of process mining is to discover process models from event logs, i.e., events logged by some information system are used to extract information about activities and their causal relations. Several algorithms have been proposed for process mining. Many of these algorithms cannot deal with concurrency. Other typical problems are the presence of duplicate activities, hidden activities, non-free-choice constructs, etc. In addition, real-life logs contain noise (e.g., exceptions or incorrectly logged events) and are typically incomplete (i.e., the event logs contain only a fragment of all possible behaviors). To tackle these problems we propose a completely new approach based on genetic algorithms. As can be expected, a genetic approach is able to deal with noise and incompleteness. However, it is not easy to represent processes properly in a genetic setting. In this paper, we show a genetic process mining approach using the so-called causal matrix as a representation for individuals. We elaborate on the relation between Petri nets and this representation and show that genetic algorithms can be used to discover Petri net models from event logs. textcopyright Springer-Verlag Berlin Heidelberg 2005.

摘要： 过程挖掘的主题在业务过程管理（BPM）领域吸引了研究人员和工具供应商的关注。流程挖掘的目的是从事件日志中发现流程模型，即，某些信息系统记录的事件用于提取有关活动及其因果关系的信息。已经提出了几种用于过程挖掘的算法。这些算法中有许多无法处理并发。其他典型问题是重复活动，隐藏活动，非自由选择构造等的存在。此外，现实生活中的日志包含噪音（例如，异常或错误记录的事件），并且通常不完整（即，事件日志）仅包含所有可能行为的一部分）。为了解决这些问题，我们提出了一种基于遗传算法的全新方法。可以预见，遗传方法能够处理噪声和不完整性。但是，在遗传背景下正确地表示过程并不容易。在本文中，我们展示了一种使用所谓因果矩阵作为个体表示的遗传过程挖掘方法。我们详细介绍了Petri网与这种表示之间的关系，并表明可以使用遗传算法从事件日志中发现Petri网模型。 textcopyright Springer-Verlag Berlin Heidelberg2005。

下载地址 | 返回目录 | [10.1007/11494744_5]

[12] Improving process discovery results by filtering outliers using conditional behavioural probabilities (2018)

(Sani, Mohammadreza Fani and van Zelst, Sebastiaan J. and van der Aalst, Wil M.P. | )

Abstract: Process discovery, one of the key challenges in process mining, aims at discovering process models from process execution data stored in event logs. Most discovery algorithms assume that all data in an event log conform to correct execution of the process, and hence, incorporate all behaviour in their resulting process model. However, in real event logs, noise and irrelevant infrequent behaviour are often present. Incorporating such behaviour results in complex, incomprehensible process models concealing the correct and/or relevant behaviour of the underlying process. In this paper, we propose a novel general purpose filtering method that exploits observed conditional probabilities between sequences of activities. The method has been implemented in both the ProM toolkit and the RapidProM framework. We evaluate our approach using real and synthetic event data. The results show that the proposed method accurately removes irrelevant behaviour and, indeed, improves process discovery results.

摘要： 过程发现是过程挖掘中的关键挑战之一，旨在从事件日志中存储的过程执行数据中发现过程模型。大多数发现算法均假定事件日志中的所有数据均符合流程的正确执行，因此会将所有行为纳入其生成的流程模型中。但是，在真实事件日志中，经常会出现噪音和无关紧要的行为。合并此类行为会导致复杂，难以理解的过程模型，从而掩盖了基础过程的正确和/或相关行为。在本文中，我们提出了一种新颖的通用过滤方法，该方法利用活动序列之间观察到的条件概率。该方法已在ProM工具包和RapidProM框架中实现。我们使用真实和综合事件数据评估我们的方法。结果表明，该方法能够准确地消除不相关的行为，并确实改善了过程发现的结果。

下载地址 | 返回目录 | [10.1007/978-3-319-74030-0_16]

[13] Learning hybrid process models from events process discovery without faking confidence (2017)

(van der Aalst, Wil M.P. and De Masellis | )

Abstract: Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a picture not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining vague when there is not enough evidence in the data or standard modeling constructs do not fit. Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.

摘要： 过程发现技术返回的过程模型既可以是正式的（精确地描述可能的行为），也可以是非正式的（仅仅是图片，不允许任何形式的形式推理）。正式模型能够将迹线（即事件序列）分类为合适或不合适。文献中描述的大多数过程挖掘方法都会产生这样的模型。这与超过25种可用的商业过程挖掘工具形成了鲜明的对比，这些工具仅发现非正式过程模型，这些模型在精确的可能踪迹集上故意含糊不清。供应商采用这种模型的主要原因有两个：可伸缩性和简单性。在本文中，我们建议将两个方面的优点结合起来：发现具有正式和非正式元素的混合过程模型。作为概念的证明，我们提出了一种基于混合Petri网的发现技术。这些模型不仅可以进行形式推理，还可以揭示主流形式模型无法捕获的信息。一种新的返回混合Petri网的发现算法已在ProM中实现，并已应用于多个现实事件日志中。结果清楚地表明，当数据中没有足够的证据或标准建模构造不适合时，保持模糊的优势。而且，该方法具有足够的可伸缩性，可以结合到工业强度过程挖掘工具中。

[下载地址](http://arxiv.org/abs/1703.06125 http://link.springer.com/10.1007/978-3-319-04175-9_1) | 返回目录 | [10.1007/978-3-319-65000-54]

[14] On the representational bias in process mining (2011)

(Van Der Aalst | )

Abstract: Process mining serves a bridge between data mining and business process modeling. The goal is to extract process related knowledge from event data stored in information systems. One of the most challenging process mining tasks is process discovery, i.e., the automatic construction of process models from raw event logs. Today there are dozens of process discovery techniques generating process models using different notations (Petri nets, EPCs, BPMN, heuristic nets, etc.). This paper focuses on the representational bias used by these techniques. We will show that the choice of target model is very important for the discovery process itself. The representational bias should not be driven by the desired graphical representation but by the characteristics of the underlying processes and process discovery techniques. Therefore, we analyze the role of the representational bias in process mining. textcopyright 2011 IEEE.

摘要： 流程挖掘在数据挖掘和业务流程建模之间架起了一座桥梁。目的是从信息系统中存储的事件数据中提取与过程相关的知识。流程发现是最具挑战性的流程挖掘任务之一，即从原始事件日志自动构建流程模型。如今，有数十种过程发现技术使用不同的表示法（Petri网络，EPC，BPMN，启发式网络等）生成过程模型。本文重点介绍了这些技术使用的代表性偏差。我们将证明目标模型的选择对于发现过程本身非常重要。表示偏差不应由所需的图形表示驱动，而应由基本过程和过程发现技术的特征驱动。因此，我们分析了代表性偏差在过程挖掘中的作用。 textcopyright 2011 IEEE。

下载地址 | 返回目录 | [10.1109/WETICE.2011.64]

[15] Pattern discovery using sequence data mining: Applications and studies (2011)

(Kumar, Pradeep and Radha Krishna | )

Abstract: Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios. Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches. textcopyright 2012 by IGI Global. All rights reserved.

摘要： 每天都会收集来自Web服务器日志，联机事务日志和性能度量的顺序数据。此顺序数据是有价值的信息源，因为它允许个人搜索特定的值或事件，并且还有助于分析某些事件或一组相关事件的频率。在科学，工程和商业场景的许多领域中，按顺序查找模式至关重要。使用序列数据挖掘进行模式发现：应用程序和研究提供了序列挖掘技术的全面视图，并介绍了研究人员和从业人员在序列数据中进行模式发现的最新研究和案例研究。这项研究确定了各种序列挖掘方法引入的行业应用。 textcopyright，2012年，IGI Global。版权所有。

下载地址 | 返回目录 | [10.4018/978-1-61350-056-9]

[16] Process Mining (2011)

(van der Aalst, Wil M. P. | )

Abstract: Over the last decade, process mining emerged as a new research field that focuses on the analysis of processes using event data. Classical data mining techniques such as classification, clustering, regression, association rule learning, and sequence/episode mining do not focus on business process models and are often only used to analyze a specific step in the overall process. Process mining focuses on end-to-end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques. Process models are used for analysis (e.g., simulation and verification) and enactment by BPM/WFM systems. Previously, process models were typically made by hand without using event data. However, activities executed by people, machines, and software leave trails in so-called event logs. Process mining techniques use such logs to discover, analyze, and improve business processes. Recently, the Task Force on Process Mining released the Process Mining Manifesto. This manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active involvement of end-users, tool vendors, consultants, analysts, and researchers illustrates the growing significance of process mining as a bridge between data mining and business process modeling. The practical relevance of process mining and the interesting scientific challenges make process mining one of the hot topics in Business Process Management (BPM). This article introduces process mining as a new research field and summarizes the guiding principles and challenges described in the manifesto. textcopyright 2012 ACM.

摘要： 在过去的十年中，过程挖掘作为一个新的研究领域出现了，该领域专注于使用事件数据进行过程分析。经典的数据挖掘技术（例如分类，聚类，回归，关联规则学习和序列/片段挖掘）并不关注业务流程模型，而通常仅用于分析整个流程中的特定步骤。流程挖掘专注于端到端流程，并且由于事件数据的可用性和新的流程发现以及一致性检查技术的日益普及而成为可能。流程模型用于BPM / WFM系统的分析（例如，仿真和验证）和制定。以前，流程模型通常是在不使用事件数据的情况下手动完成的。但是，由人员，机器和软件执行的活动会在所谓的事件日志中留下痕迹。流程挖掘技术使用此类日志来发现，分析和改进业务流程。最近，流程挖掘工作组发布了流程挖掘宣言。该宣言得到了53个组织的支持，并且有77名过程采矿专家对此做出了贡献。最终用户，工具供应商，顾问，分析师和研究人员的积极参与说明了过程挖掘作为数据挖掘和业务过程建模之间的桥梁的重要性日益提高。流程挖掘的实际相关性和有趣的科学挑战使流程挖掘成为业务流程管理（BPM）的热门主题之一。本文介绍了过程挖掘作为一个新的研究领域，并总结了宣言中描述的指导原则和挑战。 textcopyright 2012 ACM。

下载地址 | 返回目录 | [10.1007/978-3-642-19345-3]

[17] Process mining and the black swan: An empirical analysis of the influence of unobserved behavior on the quality of mined process models (2018)

(Rehse, Jana Rebecca and Fettke, Peter and Loos, Peter | )

Abstract: In this paper, we present the epistomological problem of induction, illustrated by the metaphor of the black swan, and its relevance for Process Mining. The quality of mined models is typically measured in terms of four dimensions, namely fitness, precision, simplicity, and generalization. Both precision and generalization rely on the definition of unobserved behavior, i.e. traces not contained in the log. This paper is intended to analyze the influence of unobserved behavior, the potential black swan, has on the quality of mined models. We conduct an empirical analysis to investigate the relation between a system, its observed and unobserved behavior and the mined models. The results show that the unobserved behavior, mainly determined by the nature of the unknown system, can have a significant impact on the quality assessment of mined models, hence eliciting the need to explicate and discuss the assumptions underlying the notions of unobserved behavior in more depth.

摘要： 在本文中，我们介绍了感应的流行病学问题，如黑天鹅的隐喻所说明的，及其与过程采矿的相关性。挖掘模型的质量通常从四个维度来衡量，即适应性，精度，简单性和泛化性。精度和泛化都依赖于不可观察的行为的定义，即，日志中未包含的跟踪。本文旨在分析未观察到的行为，潜在的黑天鹅对挖掘模型质量的影响。我们进行了一项实证分析，以研究系统，其观察到的和未观察到的行为与挖掘的模型之间的关系。结果表明，未观察到的行为（主要由未知系统的性质决定）可能会对挖掘模型的质量评估产生重大影响，因此需要更深入地阐述和讨论未观察到的行为的概念所基于的假设。

下载地址 | 返回目录 | [10.1007/978-3-319-74030-0_19]

[18] Process mining techniques and applications – A systematic mapping study (2019)

(Garcia, Cleiton dos Santos and Meincheim, Alex and Faria Junior | )

Abstract: Process mining is a growing and promising study area focused on understanding processes and to help capture the more significant findings during real execution rather than, those methods that, only observed idealized process model. The objective of this article is to map the active research topics of process mining and their main publishers by country, periodicals, and conferences. We also extract the reported application studies and classify these by exploration domains or industry segments that are taking advantage of this technique. The applied research method was systematic mapping, which began with 3713 articles. After applying the exclusion criteria, 1278 articles were selected for review. In this article, an overview regarding process mining is presented, the main research topics are identified, followed by identification of the most applied process mining algorithms, and finally application domains among different business segments are reported on. It is possible to observe that the most active research topics are associated with the process discovery algorithms, followed by conformance checking, and architecture and tools improvements. In application domains, the segments with major case studies are healthcare followed by information and communication technology, manufacturing, education, finance, and logistics.

摘要： 过程挖掘是一个不断发展且充满希望的研究领域，致力于了解过程并帮助在实际执行过程中捕获更重要的发现，而不是仅观察理想过程模型的那些方法。本文的目的是按国家，期刊和会议来概述过程挖掘及其主要发行者的活跃研究主题。我们还将提取已报告的应用研究，并根据利用该技术的勘探领域或行业细分将其分类。应用的研究方法是系统映射，从3713篇文章开始。应用排除标准后，选择了1278篇文章进行审查。在本文中，将对流程挖掘进行概述，确定主要的研究主题，然后确定最常用的流程挖掘算法，最后报告不同业务领域之间的应用领域。可以观察到，最活跃的研究主题与过程发现算法相关，然后进行一致性检查以及体系结构和工具改进。在应用领域，主要案例研究的领域是医疗保健，其次是信息和通信技术，制造，教育，金融和物流。

下载地址 | 返回目录 | [10.1016/j.eswa.2019.05.003]

[19] Process mining techniques and applications – A systematic mapping study (2019)

(Garcia, Cleiton dos Santos and Meincheim, Alex and Faria Junior | )

下载地址 | 返回目录 | [10.1016/j.eswa.2019.05.003]

[20] Process simulation and pattern discovery through alpha and heuristic algorithms (2015)

(Premchaiswadi, Wichian and Porouhan, Parham | )

Abstract: The paper is divided into two main parts. In the first part of the study, we applied two process mining discovery techniques (i.e., alpha and heuristic algorithms) on an event log previously collected from an information system during an Academic Writing (English) training course at a private university in Thailand. The event log was initially consisted of 330 process instances (i.e., number of participants) and 3,326 events (i.e., number of actions/tasks) in total. Using alpha algorithm enabled us to reconstruct causality in form of a Petri-net graph/model. By using heuristic algorithm we could derive XOR and AND connectors in form of a C-net. The results showed 86.36% of the applicants/participants managed to achieve the Academic Writing (English) certificate successfully, while 6.36% of them failed to achieve any certificate after a maximum number of 3 attempts to repeat the training course. Surprisingly, 7.28% of the participants neither achieved an accredited certificate nor failed the course by dropping out before ending the course training process. In the second part of the study, we used performance analysis with Petri net technique (as a process mining conformance checking approach) in order to further analyze the points of noncompliant behavior (i.e., so-called bottlenecks or points of noncompliant behavior) for every case in the collected course training log. Based on the results, we could eventually detect the existing discrepancies of the event log leading to +24 missed tokens and -24 remained tokens altogether.

摘要： 本文分为两个主要部分。在研究的第一部分中，我们在泰国一所私立大学的学术写作（英语）培训课程中从信息系统收集的事件日志上应用了两种过程挖掘发现技术（即alpha和启发式算法）。事件日志最初由330个流程实例（即，参与者数量）和3326个事件（即，动作/任务数量）组成。使用alpha算法使我们能够以Petri网图/模型的形式重构因果关系。通过使用启发式算法，我们可以得出C-net形式的XOR和AND连接器。结果显示，有86.36 ％的申请人/参与者成功获得了学术写作（英语）证书，而6.36 ％的申请人/参与者在最多重复3次尝试后仍未获得任何证书。训练课程。令人惊讶的是，有7.28 ％的参与者既未获得认可证书，也没有通过退出课程培训过程而退学而导致课程失败。在研究的第二部分中，我们使用Petri网技术（作为过程挖掘一致性检查方法）进行了性能分析，以便进一步分析每个方面的不合规行为的要点（即所谓的瓶颈或不合规行为的要点）。收集的课程培训日志中的案例。根据结果，我们最终可以检测到事件日志的现有差异，导致+24个丢失的令牌和-24个剩余的令牌。

下载地址 | 返回目录 | [10.1109/ICTKE.2015.7368472]

[21] Towards improving the representational bias of process mining (2012)

(Van Der Aalst | )

Abstract: Process mining techniques are able to extract knowledge from event logs commonly available in todays information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. Process discovery-discovering a process model from example behavior recorded in an event log-is one of the most challenging tasks in process mining. A variety of process discovery techniques have been proposed. Most techniques suffer from the problem that often the discovered model is internally inconsistent (i.e., the model has deadlocks, livelocks or other behavioral anomalies). This suggests that the search space should be limited to sound models. In this paper, we propose a tree representation that ensures soundness. We evaluate the impact of the search space reduction by implementing a simple genetic algorithm that discovers such process trees. Although the result can be translated to conventional languages, we ensure the internal consistency of the resulting model while mining, thus reducing the search space and allowing for more efficient algorithms. textcopyright 2012 IFIP International Federation for Information Processing.

摘要： 进程挖掘技术能够从当今信息系统中通常可用的事件日志中提取知识。这些技术提供了在各种应用程序域中发现，监视和改进进程的新方法。进程发现-从示例行为中发现进程模型记录在事件日志中是过程挖掘中最具挑战性的任务之一，已经提出了多种过程发现技术，大多数技术都存在以下问题：发现的模型经常内部不一致（即，模型具有死锁，活锁）或其他行为异常），这表明搜索空间应仅限于声音模型。本文中，我们提出了一种树状表示形式，以确保声音的合理性。我们通过实现一种简单的遗传算法来评估搜索空间缩减的影响尽管结果可以转换为常规语言，但我们确保内部一致性f在挖掘时生成的模型中，从而减少了搜索空间并允许使用更有效的算法。 textcopyright 2012年IFIP国际信息处理联合会。

下载地址 | 返回目录 | [10.1007/978-3-642-34044-4_3]

[22] A Framework for Benchmarking BPMN 2.0 Workflow Management Systems (2015)

(Motahari-Nezhad, Hamid R. and Recker, Jan and Weidlich, Matthias | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: This book constitutes the proceedings of the 13th International Conference on Business Process Management, BPM 2015, held in Innsbruck, Austria, in August/September 2015. The 21 regular papers, 7 short papers and 2 inductrial papers included in this volume were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on runtime process management, process modeling, process modeling discovery, business process models and analytics, BPM in industry, process compliance and deviations, energing and practical areas of BPM, and process monitoring.

摘要： 本书构成了2015年8月/ 9月在奥地利因斯布鲁克举行的2015年BPM第13届国际业务流程管理会议的会议记录。本卷中的21篇常规论文，7篇简短论文和2篇诱导性论文均经过仔细研究审查并从125个提交中选择。这些文件分为几个主题，分别涉及运行时流程管理，流程建模，流程建模发现，业务流程模型和分析，行业中的BPM，流程合规性和偏差，BPM的实用性和实用性以及流程监控。

下载地址 | 返回目录 | [10.1007/978-3-319-23063-4]

[23] A Framework for Benchmarking BPMN 2.0 Workflow Management Systems (2015)

下载地址 | 返回目录 | [10.1007/978-3-319-23063-4]

[24] A Framework for Benchmarking BPMN 2.0 Workflow Management Systems (2015)

下载地址 | 返回目录 | [10.1007/978-3-319-23063-4]

[25] A family of case studies on business process mining using MARBLE (2012)

(Perez-Castillo, Ricardo and Cruz-Lemus, Jose A. and De Guzman | Journal of Systems and Software)

Abstract: Business processes, most of which are automated by information systems, have become a key asset in organizations. Unfortunately, uncontrolled maintenance implies that information systems age overtime until they need to be modernized. During software modernization, ageing systems cannot be entirely discarded because they gradually embed meaningful business knowledge, which is not present in any other artifact. This paper presents a technique for recovering business processes from legacy systems in order to preserve that knowledge. The technique statically analyzes source code and generates a code model, which is later transformed by pattern matching into a business process model. This technique has been validated over a two-year period in several industrial modernization projects. This paper reports the results of a family of case studies that were performed to empirically validate the technique using analysis and meta-analysis techniques. The family of case studies demonstrates that the technique is feasible in terms of effectiveness and efficiency. textcopyright 2012 Elsevier Inc. All rights reserved.

摘要： 大多数业务流程已由信息系统自动化，已成为组织中的重要资产。不幸的是，不受控制的维护意味着信息系统会老化，直到需要对其进行现代化。在软件现代化过程中，老化的系统无法完全丢弃，因为它们逐渐嵌入了有意义的业务知识，而其他任何工件中都没有。本文提出了一种从旧系统中恢复业务流程以保留该知识的技术。该技术静态分析源代码并生成代码模型，然后通过模式匹配将其转换为业务流程模型。这项技术已在两年的多个工业现代化项目中得到验证。本文报告了一系列案例研究的结果，这些案例旨在使用分析和荟萃分析技术以经验方式验证该技术。一系列案例研究表明，该技术在有效性和效率方面是可行的。 textcopyright 2012 Elsevier Inc.保留所有权利。

下载地址 | 返回目录 | [10.1016/j.jss.2012.01.022]

[26] A genetic algorithm for discovering process trees (2012)

(Buijs, J. C.A.M. and Van Dongen | 2012 IEEE Congress on Evolutionary Computation, CEC 2012)

Abstract: Existing process discovery approaches have problems dealing with competing quality dimensions (fitness, simplicity, generalization, and precision) and may produce anomalous process models (e.g., deadlocking models). In this paper we propose a new genetic process mining algorithm that discovers process models from event logs. The tree representation ensures the soundness of the model. Moreover, as experiments show, it is possible to balance the different quality dimensions. Our genetic process mining algorithm is the first algorithm where the search process can be guided by preferences of the user while ensuring correctness. textcopyright 2012 IEEE.

摘要： 现有的过程发现方法在处理竞争质量维度（适应性，简单性，泛化性和精度）时会遇到问题，并且可能会产生异常的过程模型（例如，死锁模型）。在本文中，我们提出了一种新的遗传过程挖掘算法，该算法从事件日志中发现过程模型。树表示确保模型的健全性。而且，如实验所示，可以平衡不同的质量尺寸。我们的遗传过程挖掘算法是第一个算法，在确保正确性的同时，可以根据用户的偏好来指导搜索过程。 textcopyright 2012 IEEE。

下载地址 | 返回目录 | [10.1109/CEC.2012.6256458]

[27] A genetic algorithm for process discovery guided by completeness, precision and simplicity (2014)

(Vazquez-Barreiros, Borja and Mucientes, Manuel and Lama, Manuel | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: Several process discovery algorithms have been presented in the last years. These approaches look for complete, precise and simple models. Nevertheless, none of the current proposals obtains a good integration between the three objectives and, therefore, the mined models have differences with the real models. In this paper we present a genetic algorithm (ProDiGen) with a hierarchical fitness function that takes into account completeness, precision and simplicity. Moreover, ProDiGen uses crossover and mutation operators that focus the search on those parts of the model that generate errors during the processing of the log. The proposal has been validated with 21 different logs. Furthermore, we have compared our approach with two of the state of the art algorithms. textcopyright 2014 Springer International Publishing Switzerland.

摘要： 最近几年已经提出了几种过程发现算法。这些方法寻找完整，精确和简单的模型。然而，当前的提议都没有在三个目标之间取得良好的整合，因此，挖掘的模型与实际模型存在差异。在本文中，我们提出了一种遗传算法（ProDiGen），它具有考虑了完整性，准确性和简单性的分层适应度函数。此外，ProDiGen使用交叉和变异运算符将搜索集中在模型的那些在日志处理期间产生错误的部分。该提案已通过21种不同的日志验证。此外，我们已将我们的方法与两种最先进的算法进行了比较。 textcopyright 2014年Springer国际出版瑞士。

下载地址 | 返回目录 | [10.1007/978-3-319-10172-9_8]

[28] A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs (2012)

(De Weerdt | Information Systems)

Abstract: Process mining is the research domain that is dedicated to the a posteriori analysis of business process executions. The techniques developed within this research area are specifically designed to provide profound insight by exploiting the untapped reservoir of knowledge that resides within event logs of information systems. Process discovery is one specific subdomain of process mining that entails the discovery of control-flow models from such event logs. Assessing the quality of discovered process models is an essential element, both for conducting process mining research as well as for the use of process mining in practice. In this paper, a multi-dimensional quality assessment is presented in order to comprehensively evaluate process discovery techniques. In contrast to previous studies, the major contribution of this paper is the use of eight real-life event logs. For instance, we show that evaluation based on real-life event logs significantly differs from the traditional approach to assess process discovery techniques using artificial event logs. In addition, we provide an extensive overview of available process discovery techniques and we describe how discovered process models can be assessed regarding both accuracy and comprehensibility. The results of our study indicate that the HeuristicsMiner algorithm is especially suited in a real-life setting. However, it is also shown that, particularly for highly complex event logs, knowledge discovery from such data sets can become a major problem for traditional process discovery techniques. textcopyright 2012 Elsevier Ltd. All rights reserved.

摘要： 流程挖掘是致力于对业务流程执行进行事后分析的研究领域。该研究领域内开发的技术经过专门设计，可通过利用驻留在信息系统事件日志中的未开发的知识资源来提供深刻的见解。流程发现是流程挖掘的一个特定子域，它要求从此类事件日志中发现控制流模型。评估发现的过程模型的质量是进行过程挖掘研究以及在实践中使用过程挖掘的基本要素。在本文中，为了全面评估过程发现技术，提出了多维质量评估。与以前的研究相比，本文的主要贡献是使用了8个现实事件日志。例如，我们表明，基于真实事件日志的评估与使用人工事件日志评估流程发现技术的传统方法大不相同。此外，我们对可用的过程发现技术进行了广泛的概述，并描述了如何就准确性和可理解性评估发现的过程模型。我们的研究结果表明，启发式Miner算法特别适合于现实生活中的环境。但是，还显示出，特别是对于高度复杂的事件日志，从此类数据集中发现知识可能成为传统过程发现技术的主要问题。 textcopyright 2012 Elsevier Ltd.保留所有权利。

下载地址 | 返回目录 | [10.1016/j.is.2012.02.004]

[29] A systematic review on security in Process-Aware Information Systems - Constitution, challenges, and future directions (2014)

(Leitner, Maria and Rinderle-Ma, Stefanie | Information and Software Technology)

Abstract: Context Security in Process-Aware Information Systems (PAIS) has gained increased attention in current research and practice. However, a common understanding and agreement on security is still missing. In addition, the proliferation of literature makes it cumbersome to overlook and determine state of the art and further to identify research challenges and gaps. In summary, a comprehensive and systematic overview of state of the art in research and practice in the area of security in PAIS is missing. Objective This paper investigates research on security in PAIS and aims at establishing a common understanding of terminology in this context. Further it investigates which security controls are currently applied in PAIS. Method A systematic literature review is conducted in order to classify and define security and security controls in PAIS. From initially 424 papers, we selected in total 275 publications that related to security and PAIS between 1993 and 2012. Furthermore, we analyzed and categorized the papers using a systematic mapping approach which resulted into 5 categories and 12 security controls. Results In literature, security in PAIS often centers on specific (security) aspects such as security policies, security requirements, authorization and access control mechanisms, or inter-organizational scenarios. In addition, we identified 12 security controls in the area of security concepts, authorization and access control, applications, verification, and failure handling in PAIS. Based on the results, open research challenges and gaps are identified and discussed with respect to possible solutions. Conclusion This survey provides a comprehensive review of current security practice in PAIS and shows that security in PAIS is a challenging interdisciplinary research field that assembles research methods and principles from security and PAIS. We show that state of the art provides a rich set of methods such as access control models but still several open research challenges remain. textcopyright 2013 Elsevier B.V. All rights reserved.

摘要： 过程感知信息系统（PAIS）中的上下文安全性在当前的研究和实践中得到了越来越多的关注。但是，仍然缺乏关于安全性的共识和共识。此外，文学的激增使人们难以忽视和确定现有技术水平，并进一步确定研究挑战和差距。总之，缺少对PAIS安全领域研究和实践的最新技术的全面而系统的概述。目的本文研究PAIS中的安全性研究，旨在在此背景下建立对术语的共识。此外，它还研究了PAIS中当前应用了哪些安全控制。方法为了对PAIS中的安全性和安全控制进行分类和定义，进行了系统的文献综述。在1993年至2012年之间，我们从最初的424篇论文中，总共选择了275篇与安全和PAIS相关的出版物。此外，我们使用系统的映射方法对这些论文进行了分析和分类，得出5类和12种安全控件。结果在文献中，PAIS中的安全性通常集中在特定的（安全性）方面，例如安全性策略，安全性要求，授权和访问控制机制或组织间方案。此外，我们在PAIS的安全概念，授权和访问控制，应用程序，验证和故障处理领域中确定了12种安全控制。根据结果，确定并讨论了可能存在的解决方案方面的开放研究挑战和差距。结束语该调查对PAIS中的当前安全实践进行了全面回顾，并显示出PAIS中的安全性是一个充满挑战的跨学科研究领域，其汇集了来自安全性和PAIS的研究方法和原理。我们表明，现有技术提供了丰富的方法集，例如访问控制模型，但仍然存在一些开放的研究挑战。 textcopyright 2013 Elsevier B.V.保留所有权利。

下载地址 | 返回目录 | [10.1016/j.infsof.2013.12.004]

[30] Action logger: Enabling process mining for robotic process automation (2019)

(Leno, Volodymyr and Polyvyanyy, Artem and La Rosa | CEUR Workshop Proceedings)

Abstract: This paper presents a tool, called Action Logger, for recording user interface (UI) logs, i.e., logs of user interactions with information systems. By generating output suitable for process mining, the tool aims to introduce process mining methods, techniques, and tools for supporting Robotic Process Automation (RPA) activities, e.g., robot discovery and implementation. Action Logger offers unique capabilities, including logging relevant user actions at a granularity level suitable for RPA, data-awareness, and context-independence.

摘要： 本文介绍了一种称为Action Logger的工具，用于记录用户界面（UI）日志，即用户与信息系统交互的日志。通过生成适合过程挖掘的输出，该工具旨在介绍过程挖掘方法，技术和工具以支持机器人过程自动化（RPA）活动，例如机器人发现和实施。动作记录器提供了独特的功能，包括以适合RPA，数据意识和上下文独立性的粒度级别记录相关的用户动作。

下载地址 | 返回目录 | []

[31] Algorithms for anomaly detection of traces in logs of process aware information systems (2013)

(Bezerra, Fabio and Wainer, Jacques | Information Systems)

Abstract: This paper discusses four algorithms for detecting anomalies in logs of process aware systems. One of the algorithms only marks as potential anomalies traces that are infrequent in the log. The other three algorithms: threshold, iterative and sampling are based on mining a process model from the log, or a subset of it. The algorithms were evaluated on a set of 1500 artificial logs, with different profiles on the number of anomalous traces and the number of times each anomalous traces was present in the log. The sampling algorithm proved to be the most effective solution. We also applied the algorithm to a real log, and compared the resulting detected anomalous traces with the ones detected by a different procedure that relies on manual choices. textcopyright 2012 Elsevier Ltd. All rights reserved.

摘要： 本文讨论了四种用于检测过程感知系统日志中异常的算法。其中一种算法仅将日志中很少出现的潜在异常痕迹标记为。其他三种算法：阈值，迭代和采样基于从日志或其子集中挖掘过程模型。在一组1500条人工日志上对算法进行了评估，并对异常迹线的数量和每个异常迹线在日志中的出现次数进行了不同的配置。采样算法被证明是最有效的解决方案。我们还将算法应用于真实日志，并将检测到的异常迹线与通过依赖于手动选择的其他过程检测到的异常迹线进行比较。 textcopyright 2012 Elsevier Ltd.保留所有权利。

下载地址 | 返回目录 | [10.1016/j.is.2012.04.004]

[32] An efficient method for mining frequent sequential patterns using multi-Core processors (2017)

(Huynh, Bao and Vo, Bay and Snasel, Vaclav | Applied Intelligence)

Abstract: The problem of mining frequent sequential patterns (FSPs) has attracted a great deal of research attention. Although there are many efficient algorithms for mining FSPs, the mining time is still high, especially for large or dense datasets. Parallel processing has been widely applied to improve processing speed for various problems. Some parallel algorithms have been proposed, but most of them have problems related to synchronization and load balancing. Based on a multi-core processor architecture, this paper proposes a load-balancing parallel approach called Parallel Dynamic Bit Vector Sequential Pattern Mining (pDBV-SPM) for mining FSPs from huge datasets using the dynamic bit vector data structure for fast determining support values. In the pDBV-SPM approach, the support count is sorted in ascending order before the set of frequent 1-sequences is partitioned into parts, each of which is assigned to a task on a processor so that most of the nodes in the leftmost branches will be infrequent and thus pruned during the search; this strategy helps to better balance the search tree. Experiments are conducted to verify the effectiveness of pDBV-SPM. The experimental results show that the proposed algorithm outperforms PIB-PRISM for mining FSPs in terms of mining time and memory usage.

摘要： 挖掘频繁序列模式（FSP）的问题已引起了大量研究关注。尽管有许多用于挖掘FSP的有效算法，但挖掘时间仍然很高，尤其是对于大型或密集数据集。并行处理已被广泛应用于提高各种问题的处理速度。已经提出了一些并行算法，但是大多数并行算法都存在与同步和负载平衡有关的问题。本文基于多核处理器体系结构，提出了一种负载均衡并行方法，称为并行动态位向量顺序模式挖掘（pDBV-SPM），用于使用动态位向量数据结构从大型数据集中挖掘FSP，以快速确定支持值。在pDBV-SPM方法中，将支持计数按升序排序，然后将这组频繁的1序列划分为多个部分，每个部分都分配给处理器上的任务，以便最左侧分支中的大多数节点将很少，因此在搜索过程中被修剪；此策略有助于更好地平衡搜索树。进行实验以验证pDBV-SPM的有效性。实验结果表明，该算法在挖掘时间和内存使用方面均优于PIB-PRISM。

下载地址 | 返回目录 | [10.1007/s10489-016-0859-y]

[33] An improved simulated annealing algorithm for process mining (2009)

(Dianfang, Gao and Qiang, Liu | Proceedings of the 2009 13th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2009)

Abstract: The target of process mining is to automatically extract process models from event logs related to actual business process executions. One of the most important fields concerned is control flow mining, i.e., ordering of activities. However, the presence of complicated constructs, such as duplicate tasks, invisible tasks and non-free-choice structures, hinders us from correctly discovering the relations between activities. Therefore, an improved simulated annealing approach is proposed in this paper to tackle these problems. To verify the performance, experiments are conducted in the process minig framework. The result is expressed in terms of Petri net. textcopyright 2009 IEEE.

摘要： 流程挖掘的目标是从与实际业务流程执行相关的事件日志中自动提取流程模型。有关的最重要领域之一是控制流挖掘，即活动的排序。但是，复杂任务的存在，例如重复任务，看不见的任务和非自由选择的结构，阻碍了我们正确地发现活动之间的关系。因此，本文提出了一种改进的模拟退火方法来解决这些问题。为了验证性能，在过程最小框架中进行了实验。结果用Petri网表示。 textcopyright 2009 IEEE。

下载地址 | 返回目录 | [10.1109/CSCWD.2009.4968104]

[34] Analysis of Patient Treatment Procedures: The BPI Challenge Case Study (2011)

(Bose, R P Jagadeesh Chandra, Van der Aalst | Business Process Management Workshops (1) (Vol. 99, pp. 165-166).)

Abstract: A real-life event log, taken from a Dutch Academic Hospital, is analyzed using process mining techniques. The log contains events related to treatment and diagnosis steps for patients diagnosed with cancer. Given the heterogeneous nature of these cases, we rst demonstrate that it is possible to create more homogeneous subsets of cases (e.g. patients having a particular type of cancer that need to be treated urgently). Such preprocessing is crucial given the variation and variability found in the event log. The discovered homogeneous subsets are analyzed using stateof- the-art process mining approaches. In this paper, we report on the ndings discovered using enhanced fuzzy mining and trace alignment. A dedicated preprocessing ProM3 plug-in was developed for this challenge. The analysis was done using recent, but pre-existing, ProM plug-ins. As the evaluation shows, this approach is able to uncover many interesting ndings and could be used to improve the underlying care processes.

摘要： 来自荷兰学术医院的真实事件日志是使用过程挖掘技术进行分析的。日志包含与诊断为癌症的患者的治疗和诊断步骤有关的事件。考虑到这些病例的异质性，我们首先证明有可能创建更多同类病例（例如，患有特殊类型癌症的患者需要紧急治疗）。考虑到事件日志中的变化和可变性，这种预处理至关重要。使用最新的过程挖掘方法对发现的同类子集进行分析。在本文中，我们报告了使用增强的模糊挖掘和轨迹对齐发现的发现。为此，开发了专用的ProM3预处理插件。使用最近但已存在的ProM插件进行了分析。正如评估所表明的那样，这种方法能够发现许多有趣的发现，并可用于改善基本的护理过程。

下载地址 | 返回目录 | [10.4121/uuid]

[35] Automated Discovery of Workflow Models from Hospital Data (2001)

(Maruster, L and van der Aalst, W M P and Weijters, T and van den Bosch, A and Daelemans, W | Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2001))

Abstract: Workflow nets, a subclass of Petri nets, are known as attractive models for analysing complex business processes. In a hospital environment, for example, the processes show a complex and dynamic behavior, which is difficult to control; the workflow net which models such a complex process provides a good insight into it, and due to its formal representation offers techniques for improved control. We propose a method whose main advantage consists in discovering the workflow Petri nets automatically from process logs. We illustrate the functioning of our method on simulated hospital process logs, containing information about medical actions over time. The results of our experiments indicate that this method is able to discover processes whose underlying models are acyclic and sound WF nets, involving parallel, conditional and sequential constructs. We argue that solutions have to be found for cyclic and freechoice /non-free-choice workflow nets.

摘要： 工作流网是Petri网的子类，被称为用于分析复杂业务流程的有吸引力的模型。例如，在医院环境中，这些过程显示出复杂而动态的行为，难以控制。对这样一个复杂过程建模的工作流网提供了很好的洞察力，并且由于它的形式化表示，提供了改进控制的技术。我们提出一种方法，其主要优点在于从过程日志中自动发现工作流Petri网。我们在模拟的医院过程日志中说明了我们的方法的功能，其中包含有关随时间推移医疗行为的信息。我们的实验结果表明，该方法能够发现其基础模型是无环且健全的WF网络的过程，涉及并行，条件和顺序构造。我们认为必须为循环和自由选择/非自由选择工作流网络找到解决方案。

下载地址 | 返回目录 | []

[36] Automated discovery of structured process models: Discover structured vs. Discover and structure (2016)

(Augusto, Adriano and Conforti, Raffaele and Dumas, Marlon and Rosa, Marcello La and Bruno, Giorgio | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate blockstructured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our discover and structure approach outperforms traditional discover structured approaches with respect to a range of accuracy and complexity measures.

摘要： 本文解决了从事件日志中发现业务流程模型的问题。解决该问题的现有方法在发现的模型的准确性和可理解性之间进行了各种折衷。关于第二个标准，经验研究表明，与非结构化模型相比，块结构化过程模型通常更易于理解且更不易出错。因此，几种自动过程发现方法通过构造生成块结构模型。但是，这些方法将生成精确模型与确保其结构化的关注交织在一起，有时会牺牲前者以确保后者。在本文中，我们提出了一种将这两个问题分开的替代方法。我们首先应用一种众所周知的启发式方法，而不是直接发现结构化的过程模型，该方法会发现更准确但有时是非结构化（甚至不健全）的过程模型，然后将结果模型转换为结构化的模型。实验评估表明，就一系列准确性和复杂性衡量而言，我们的发现和结构方法优于传统的发现结构方法。

下载地址 | 返回目录 | [10.1007/978-3-319-46397-1_25]

[37] Avoiding over-fitting in ILP-based process discovery (2015)

(van Zelst, Sebastiaan J. and van Dongen, Boudewijn F. and van der Aalst, Wil M.P. | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: The aim of process discovery is to discover a process model based on business process execution data, recorded in an event log. One of several existing process discovery techniques is the ILP-based process discovery algorithm. The algorithm is able to unravel complex process structures and provides formal guarantees w.r.t. the model discovered, e.g., the algorithm guarantees that a discovered model describes all behavior present in the event log. Unfortunately the algorithm is unable to cope with exceptional behavior present in event logs. As a result, the application of ILP-based process discovery techniques in everyday process discovery practice is limited. This paper addresses this problem by proposing a filtering technique tailored towards ILP-based process discovery. The technique helps to produce process models that are less over-fitting w.r.t. the event log, more understandable, and more adequate in capturing the dominant behavior present in the event log. The technique is implemented in the ProM framework.

摘要： 发现过程的目的是基于记录在事件日志中的业务过程执行数据发现过程模型。现有的几种过程发现技术之一是基于ILP的过程发现算法。该算法能够解开复杂的过程结构，并提供正式的担保。所发现的模型，例如算法保证所发现的模型描述事件日志中存在的所有行为。不幸的是，该算法无法应对事件日志中出现的异常行为。结果，基于ILP的过程发现技术在日常过程发现实践中的应用受到限制。本文通过提出一种针对基于ILP的过程发现的过滤技术来解决此问题。这项技术有助于产生工艺模型，该模型不太适合w.r.t.事件日志，它更易于理解，并且更适合捕获事件日志中存在的主要行为。该技术在ProM框架中实现。

下载地址 | 返回目录 | [10.1007/978-3-319-23063-4_10]

[38] Behavioral process mining for unstructured processes (2016)

(Diamantini, Claudia and Genga, Laura and Potena, Domenico | Journal of Intelligent Information Systems)

Abstract: Real world applications provide many examples of unstructured processes, where process execution is mainly driven by contingent decisions taken by the actors, with the result that the process is rarely repeated exactly in the same way. In these cases, traditional Process Discovery techniques, aimed at extracting complete process models from event logs, reveal some limits. In fact, when applied to logs of unstructured processes, Process Discovery techniques usually return complex, spaghetti-like models, which usually provide limited support to analysts. As a remedy, in the present work we propose Behavioral Process Mining as an alternative approach to enlighten relevant subprocesses, representing meaningful collaboration work practices. The approach is based on the application of hierarchical graph clustering to the set of instance graphs generated by a process. We also describe a technique for building instance graphs from traces. We assess advantages and limits of the approach on a set of synthetic and real world experiments.

摘要： 现实世界的应用程序提供了许多非结构化流程的示例，其中流程的执行主要由参与者所做出的偶然决定来驱动，其结果是，流程很少以完全相同的方式重复进行。在这些情况下，旨在从事件日志中提取完整流程模型的传统流程发现技术揭示了一些限制。实际上，当将Process Discovery技术应用于非结构化流程的日志时，通常会返回复杂的类似意大利面条的模型，这些模型通常仅对分析人员提供有限的支持。作为一种补救措施，在本工作中，我们提出了行为过程挖掘作为一种替代方法来启发相关的子过程，代表有意义的协作工作实践。该方法基于将层次图聚类应用于流程生成的实例图集。我们还描述了一种从痕迹构建实例图的技术。我们在一组合成和现实世界的实验中评估该方法的优点和局限性。

下载地址 | 返回目录 | [10.1007/s10844-016-0394-7]

[39] Business Process Management Workshops (TAProViz 17) (2013)

(Teniente, Ernest and Weidlich, Matthias | )

Abstract: The Business Process Execution Language (BPEL) uses XML to specify the data used within a process and realizes data flow via $backslash$ n (globally) shared variables. Additionally, assign activities can be used to copy (parts of) variables to other variables using $backslash$ n techniques like XPath or XSLT. BPEL for Semantic Web Services (BPEL4SWS) employs SAWSDL to give meaning to data by referring $backslash$ n to ontological concepts and to enable a seamless mapping of XML data and its ontological representation. In this paper we $backslash$ n show how this ontological knowledge can be used to ease the definition of data flow in BPEL. We therefore extend BPEL and $backslash$ n introduce the concept of mediaton as a first class citizen. We give an example of data mediation in BPEL processes and show $backslash$ n how process modellers can benefit from the ontological knowledge when specifying data manipulation declaratively instead of $backslash$ n having to implement data manipulation each time a process is modelled.

摘要： 业务流程执行语言（BPEL）使用XML指定流程中使用的数据，并通过$ 反斜杠$ n（全局）共享变量实现数据流。此外，可以使用$ 反斜杠$ n技术（例如XPath或XSLT）将Assign活动用于将变量（的一部分）复制到其他变量。用于语义Web服务的BPEL（BPEL4SWS）使用SAWSDL通过将反斜杠$ n引用到本体概念来赋予数据含义，并实现XML数据及其本体表示的无缝映射。在本文中，反斜杠$ n显示了如何使用这种本体知识来简化BPEL中数据流的定义。因此，我们扩展了BPEL，反斜杠引入了mediaton作为头等公民的概念。我们以BPEL流程中的数据调解为例，并展示$ 反斜杠$ n声明式指定数据操作时，流程建模者如何从本体知识中受益，而不是每次都需要执行数据操纵时，$ 反斜杠$ n建模。

下载地址 | 返回目录 | [10.1007/978-3-319-74030-0]

[40] Business alignment: Using process mining as a tool for Delta analysis and conformance testing (2005)

(van der Aalst, W. M.P. | Requirements Engineering)

Abstract: Increasingly, business processes are being controlled and/or monitored by information systems. As a result, many business processes leave their foot-prints in transactional information systems, i.e., business events are recorded in so-called event logs. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. In this paper we focus on the potential use of process mining for measuring business alignment, i.e., comparing the real behavior of an information system or its users with the intended or expected behavior. We identify two ways to create and/or maintain the fit between business processes and supporting information systems: Delta analysis and conformance testing. Delta analysis compares the discovered model (i.e., an abstraction derived from the actual process) with some predefined processes model (e.g., the workflow model or reference model used to configure the system). Conformance testing attempts to quantify the fit between the event log and some predefined processes model. In this paper, we show that Delta analysis and conformance testing can be used to analyze business alignment as long as the actual events are logged and users have some control over the process. textcopyright Springer-Verlag London Limited 2005.

摘要： 越来越多地，业务流程由信息系统控制和/或监视。结果，许多业务流程在交易信息系统中留下了它们的足迹，即，业务事件被记录在所谓的事件日志中。流程挖掘旨在通过提供用于从事件日志中发现流程，控制，数据，组织和社会结构的技术和工具来改善这一点，即流程挖掘的基本思想是通过挖掘事件日志中的知识来诊断业务流程。在本文中，我们专注于流程挖掘在衡量业务一致性方面的潜在用途，即将信息系统或其用户的实际行为与预期或预期行为进行比较。我们确定了两种创建和/或维持业务流程与支持信息系统之间契合度的方法：增量分析和一致性测试。增量分析将发现的模型（即从实际过程中得出的抽象）与一些预定义的过程模型（例如用于配置系统的工作流模型或参考模型）进行比较。一致性测试试图量化事件日志和某些预定义流程模型之间的契合度。在本文中，我们表明，只要记录了实际事件并且用户对流程有一定的控制权，Delta分析和一致性测试就可以用于分析业务一致性。 textcopyright Springer-Verlag London Limited2005。

下载地址 | 返回目录 | [10.1007/s00766-005-0001-x]

[41] Business process mining based on simulated annealing (2008)

(Song, Wei and Liu, Shaozhuo and Liu, Qiang | Proceedings of the 9th International Conference for Young Computer Scientists, ICYCS 2008)

Abstract: In order to identify business processes effectively, historical data, such as event log, can be used as a base to retrieve abstract process model. The result of process mining can provide necessary information to deploy process-aware information systems. Process structure patterns disclosing the relationship among activities is one of the most important aspects. To retrieve the process model comprehensively and quickly, this paper propose a simulated annealing process mining approach to address this issue. Main contribution of the work includes:(1)Apply the simulated annealing approach under the setting of process mining. (2)Represent events as causal matrix. (3)Evaluate the mining result with a quantitative measurement, incorporate the ideas above into existing simulated annealing algorithm to form an integrated solution. We give experimental results which created by the ProM, a platform for business process mining, with the data it provides. textcopyright 2008 IEEE.

摘要： 为了有效地识别业务流程，历史数据（例如事件日志）可以用作检索抽象流程模型的基础。流程挖掘的结果可以为部署流程感知信息系统提供必要的信息。揭示活动之间关系的过程结构模式是最重要的方面之一。为了全面快速地检索过程模型，本文提出了一种模拟退火过程挖掘方法来解决该问题。这项工作的主要贡献包括：（1）在过程挖掘的背景下应用模拟退火方法。（2）将事件表示为因果矩阵。（3）通过定量测量评估采矿结果，将上述思想纳入现有的模拟退火算法中，形成一个综合解决方案。我们提供由ProM（用于业务流程挖掘的平台）创建的实验结果及其提供的数据。 textcopyright 2008 IEEE。

下载地址 | 返回目录 | [10.1109/ICYCS.2008.279]

[42] Business process mining: An industrial application (2007)

(van der Aalst, W. M.P. and Reijers, H. A. and Weijters, A. J.M.M. and van Dongen, B. F. and Alves de Medeiros | Information Systems)

Abstract: Contemporary information systems (e.g., WfM, ERP, CRM, SCM, and B2B systems) record business events in so-called event logs. Business process mining takes these logs to discover process, control, data, organizational, and social structures. Although many researchers are developing new and more powerful process mining techniques and software vendors are incorporating these in their software, few of the more advanced process mining techniques have been tested on real-life processes. This paper describes the application of process mining in one of the provincial offices of the Dutch National Public Works Department, responsible for the construction and maintenance of the road and water infrastructure. Using a variety of process mining techniques, we analyzed the processing of invoices sent by the various subcontractors and suppliers from three different perspectives: (1) the process perspective, (2) the organizational perspective, and (3) the case perspective. For this purpose, we used some of the tools developed in the context of the ProM framework. The goal of this paper is to demonstrate the applicability of process mining in general and our algorithms and tools in particular. textcopyright 2006 Elsevier B.V. All rights reserved.

摘要： 当代信息系统（例如WfM，ERP，CRM，SCM和B2B系统）将业务事件记录在所谓的事件日志中。业务流程挖掘使用这些日志来发现流程，控制，数据，组织和社会结构。尽管许多研究人员正在开发新的且功能更强大的过程挖掘技术，并且软件供应商已将这些技术集成到他们的软件中，但是很少有更高级的过程挖掘技术已在实际过程中经过测试。本文介绍了过程采矿在荷兰国家公共工程局的一个省级办公室中的应用，该办公室负责道路和水利基础设施的建设和维护。我们使用各种过程挖掘技术，从三个不同的角度分析了各个分包商和供应商发送的发票的处理：（1）过程角度，（2）组织角度和（3）案例角度。为此，我们使用了在ProM框架中开发的一些工具。本文的目的是总体上展示过程挖掘的适用性，尤其是我们的算法和工具。 textcopyright 2006 Elsevier B.V.保留所有权利。

下载地址 | 返回目录 | [10.1016/j.is.2006.05.003]

[43] Compliance monitoring in business processes: Functionalities, application, and tool-support (2015)

(Ly, Linh Thao and Maggi, Fabrizio Maria and Montali, Marco and Rinderle-Ma, Stefanie and Van Der Aalst | Information Systems)

Abstract: In recent years, monitoring the compliance of business processes with relevant regulations, constraints, and rules during runtime has evolved as major concern in literature and practice. Monitoring not only refers to continuously observing possible compliance violations, but also includes the ability to provide fine-grained feedback and to predict possible compliance violations in the future. The body of literature on business process compliance is large and approaches specifically addressing process monitoring are hard to identify. Moreover, proper means for the systematic comparison of these approaches are missing. Hence, it is unclear which approaches are suitable for particular scenarios. The goal of this paper is to define a framework for Compliance Monitoring Functionalities (CMF) that enables the systematic comparison of existing and new approaches for monitoring compliance rules over business processes during runtime. To define the scope of the framework, at first, related areas are identified and discussed. The CMFs are harvested based on a systematic literature review and five selected case studies. The appropriateness of the selection of CMFs is demonstrated in two ways: (a) a systematic comparison with pattern-based compliance approaches and (b) a classification of existing compliance monitoring approaches using the CMFs. Moreover, the application of the CMFs is showcased using three existing tools that are applied to two realistic data sets. Overall, the CMF framework provides powerful means to position existing and future compliance monitoring approaches.

摘要： 近年来，在运行时监视业务流程是否符合相关法规，约束和规则已成为文献和实践中的主要关注点。监视不仅是指不断观察可能的合规违规行为，还包括提供细粒度的反馈并预测将来可能发生的合规违规行为的能力。有关业务流程合规性的文献很多，并且难以确定专门解决流程监视的方法。而且，缺少对这些方法进行系统比较的适当手段。因此，尚不清楚哪种方法适用于特定场景。本文的目的是为合规性监视功能（CMF）定义一个框架，该框架可以对运行时期间业务流程中合规性规则的现有和新方法进行系统比较。为了定义框架的范围，首先要确定和讨论相关领域。 CMF是根据系统的文献综述和五个选定的案例研究收集的。选择CMF的适当性通过两种方式得到证明：（a）与基于模式的合规性方法进行系统比较，以及（b）使用CMF对现有合规性监测方法进行分类。此外，使用三个应用于两个实际数据集的现有工具展示了CMF的应用。总体而言，CMF框架提供了强有力的手段来定位现有和将来的合规性监视方法。

下载地址 | 返回目录 | [10.1016/j.is.2015.02.007]

[44] Computer-interpretable clinical guidelines: A methodological review (2013)

(Peleg, Mor | Journal of Biomedical Informatics)

Abstract: Clinical practice guidelines (CPGs) aim to improve the quality of care, reduce unjustified practice variations and reduce healthcare costs. In order for them to be effective, clinical guidelines need to be integrated with the care flow and provide patient-specific advice when and where needed. Hence, their formalization as computer-interpretable guidelines (CIGs) makes it possible to develop CIG-based decision-support systems (DSSs), which have a better chance of impacting clinician behavior than narrative guidelines. This paper reviews the literature on CIG-related methodologies since the inception of CIGs, while focusing and drawing themes for classifying CIG research from CIG-related publications in the Journal of Biomedical Informatics (JBI). The themes span the entire life-cycle of CIG development and include: knowledge acquisition and specification for improved CIG design, including (1) CIG modeling languages and (2) CIG acquisition and specification methodologies, (3) integration of CIGs with electronic health records (EHRs) and organizational workflow, (4) CIG validation and verification, (5) CIG execution engines and supportive tools, (6) exception handling in CIGs, (7) CIG maintenance, including analyzing clinicians compliance to CIG recommendations and CIG versioning and evolution, and finally (8) CIG sharing. I examine the temporal trends in CIG-related research and discuss additional themes that were not identified in JBI papers, including existing themes such as overcoming implementation barriers, modeling clinical goals, and temporal expressions, as well as futuristic themes, such as patient-centric CIGs and distributed CIGs. textcopyright 2013 Elsevier Inc.

摘要： 临床实践指南（CPG）旨在提高护理质量，减少不合理的实践变化并降低医疗保健成本。为使之有效，需要将临床指南与护理流程整合在一起，并在患者接受治疗时提供针对患者的建议因此，将其形式化为计算机可解释指南（CIG）使得开发基于CIG的决策支持系统（DSS）成为可能，该系统比叙事指南更有可能影响临床医生的行为。自CIG成立以来，就CIG相关方法论进行了研究，同时重点关注和绘制了主题，以从《生物医学信息学杂志》（JBI）的CIG相关出版物中对CIG研究进行分类，这些主题涵盖了CIG开发的整个生命周期，包括：知识改进的CIG设计的获取和规范，包括（1）CIG建模语言和（2）CIG获取和规范方法，（3）集成具有电子健康记录（EHR）和组织工作流程的CIG，（4）CIG验证和验证，（5）CIG执行引擎和支持工具，（6）CIG中的异常处理，（7）CIG维护，包括分析临床医生的依从性到CIG建议以及CIG版本和演变，最后（8）CIG共享。我研究了CIG相关研究的时间趋势，并讨论了JBI论文中未发现的其他主题，包括克服实施障碍，建模临床目标和时间表达等现有主题，以及以患者为中心的未来派主题CIG和分布式CIG。 textcopyright 2013 Elsevier Inc.

下载地址 | 返回目录 | [10.1016/j.jbi.2013.06.009]

[45] Connecting databases with process mining: a meta model and toolset (2019)

(Gonzalezxa0Lopezxa0dexa0Murillas | Software and Systems Modeling)

Abstract: Process mining techniques require event logs which, in many cases, are obtained from databases. Obtaining these event logs is not a trivial task and requires substantial domain knowledge. In addition, an extracted event log provides only a single view on the database. To change our view, e.g., to focus on another business process and generate another event log, it is necessary to go back to the source of data. This paper proposes a meta model to integrate both process and data perspectives, relating one to the other. It can be used to generate different views from the database at any moment in a highly flexible way. This approach decouples the data extraction from the application of analysis techniques, enabling the application of process mining in different contexts.

摘要： 过程挖掘技术需要事件日志，在许多情况下，这些事件日志是从数据库获取的。获得这些事件日志不是一件容易的事，并且需要大量的领域知识。此外，提取的事件日志仅提供数据库的单个视图。为了改变我们的观点，例如，专注于另一个业务流程并生成另一个事件日志，有必要回到数据源。本文提出了一个元模型，该模型将过程和数据角度都集成在一起，彼此相关。它可以随时以高度灵活的方式用于从数据库生成不同的视图。这种方法使数据提取与分析技术的应用脱钩，从而使过程挖掘可以在不同的上下文中应用。

下载地址 | 返回目录 | [10.1007/s10270-018-0664-7]

[46] Control-flow discovery from event streams (2014)

(Burattin, Andrea and Sperduti, Alessandro and Van Der Aalst | Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014)

Abstract: Process Mining represents an important research field that connects Business Process Modeling and Data Mining. One of the most prominent task of Process Mining is the discovery of a control-flow starting from event logs. This paper focuses on the important problem of control-flow discovery starting from a stream of event data. We propose to adapt Heuristics Miner, one of the most effective control-flow discovery algorithms, to the treatment of streams of event data. Two adaptations, based on Lossy Counting and Lossy Counting with Budget, as well as a sliding window based version of Heuristics Miner, are proposed and experimentally compared against both artificial and real streams. Experimental results show the effectiveness of control-flow discovery algorithms for streams on artificial and real datasets.

摘要： Process Mining代表了一个重要的研究领域，将业务流程建模和数据挖掘联系在一起。 Process Mining最突出的任务之一是发现从事件日志开始的控制流。本文重点关注从事件数据流开始的控制流发现的重要问题。我们建议使最有效的控制流发现算法之一启发式采矿器适应事件数据流的处理。提出了两种改进，分别基于有损计数和带预算的有损计数，以及基于滑动窗口的启发式矿工版本，并与人工流和真实流进行了实验比较。实验结果表明，针对人工和真实数据集的流，控制流发现算法的有效性。

[下载地址](http://arxiv.org/abs/1212.6383 http://dx.doi.org/10.1109/CEC.2014.6900341 http://www.scopus.com/inward/record.url?eid=2-s2.0-84908565982&partnerID=40&md5=85265779b57d296c301199f2a4f7fe29) | 返回目录 | [10.1109/CEC.2014.6900341]

[47] Decision mining in business processes (2006)

(Rozinat, A and van der Aalst, W. M. P. | BPM Center Report BPM-06-10, ldots)

Abstract: Many companies have adopted Process-aware Information Systems (PAIS) for supporting their business processes in some form. These systems typ- ically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. Proper analysis of PAIS execution logs can yield important knowledge and help organizations improve the quality of their ser- vices. Starting from a process model as it is possible to discover by conventional process mining algorithms we analyze how data attributes influence the choices made in the process based on past process executions. Decision mining, also re- ferred to as decision point analysis, aims at the detection of data dependencies that affect the routing of a case. In this paper we describe how machine learn- ing techniques can be leveraged for this purpose, and discuss further challenges related to this approach. To verify the presented ideas a Decision Miner has been implemented within the ProM framework.

摘要： 许多公司已采用流程感知信息系统（PAIS）以某种形式支持其业务流程。这些系统通常记录与实际业务流程执行相关的事件（例如，在事务日志或审计跟踪中）。对PAIS执行日志的正确分析可以产生重要的知识，并帮助组织提高服务质量。从可能通过常规过程挖掘算法发现的过程模型开始，我们基于过去的过程执行来分析数据属性如何影响过程中的选择。决策挖掘（也称为决策点分析）旨在检测影响案例路由的数据依存关系。在本文中，我们描述了如何将机器学习技术用于此目的，并讨论了与此方法有关的进一步挑战。为了验证提出的想法，已在ProM框架内实现了Decision Miner。

下载地址 | 返回目录 | []

[48] Detection and removal of infrequent behavior from event streams of business processes (2020)

(van Zelst, Sebastiaan J. and Fani Sani | Information Systems)

Abstract: Process mining aims at gaining insights into business processes by analyzing the event data that is generated and recorded during process execution. The vast majority of existing process mining techniques works offline, i.e. using static, historical data, stored in event logs. Recently, the notion of online process mining has emerged, in which techniques are applied on live event streams, i.e. as the process executions unfold. Analyzing event streams allows us to gain instant insights into business processes. However, most online process mining techniques assume the input stream to be completely free of noise and other anomalous behavior. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out infrequent behavior from live event streams. Our experiments show that we are able to effectively filter out events from the input stream and, as such, improve online process mining results.

摘要： 流程挖掘旨在通过分析流程执行期间生成和记录的事件数据来获得对业务流程的见解。现有的绝大多数过程挖掘技术都可以脱机工作，即使用存储在事件日志中的静态历史数据进行工作。最近，出现了在线过程挖掘的概念，其中将技术应用于实时事件流，即，随着过程执行的展开。分析事件流使我们能够立即洞悉业务流程。但是，大多数在线过程挖掘技术都假定输入流完全没有噪音和其他异常行为。因此，将这些技术应用于真实数据会导致质量下降。在本文中，我们提出了一种事件处理器，该处理器使我们能够从实时事件流中滤除不常见的行为。我们的实验表明，我们能够有效地从输入流中过滤掉事件，从而改善在线过程的挖掘结果。

下载地址 | 返回目录 | [10.1016/j.is.2019.101451]

[49] Detection and removal of infrequent behavior from event streams of business processes (2020)

(van Zelst, Sebastiaan J. and Fani Sani | Information Systems)

下载地址 | 返回目录 | [10.1016/j.is.2019.101451]

[50] Discovering Infrequent Behavioral Patterns in Process Models (2017)

(Chapela-Campa, David and Mucientes, Manuel and Lama, Manuel | Lecture Notes in Business Information Processing)

Abstract: Process mining has focused, among others, on the discov- ery of frequent behavior with the aim to understand what is mainly happening in a process. Little work has been done involving uncommon behavior, and mostly centered on the detection of anomalies or devia- tions. But infrequent behavior can be also important for the management of a process, as it can reveal, for instance, an uncommon wrong real- ization of a part of the process. In this paper, we present WoMine-i, a novel algorithm to retrieve infrequent behavioral patterns from a process model. Our approach searches in a process model extracting structures with sequences, selections, parallels and loops, which are infrequently executed in the logs. This proposal has been validated with a set of synthetic and real process models, and compared with state of the art techniques. Experiments show that WoMine-i can find all types of pat- terns, extracting information that cannot be mined with the state of the art techniques.

摘要： 过程挖掘特别关注频繁行为的发现，旨在了解过程中主要发生的事情。涉及罕见行为的工作很少，主要集中在异常或偏差的检测上。但是，不频繁的行为对于流程的管理也很重要，因为它可以揭示例如流程的一部分的罕见错误实现。在本文中，我们提出了WoMine-i，这是一种从过程模型中检索不常见行为模式的新颖算法。我们的方法在过程模型中进行搜索，以提取具有序列，选择，并行和循环的结构，这些结构很少在日志中执行。该建议书已通过一组综合和实际过程模型进行了验证，并与最新技术进行了比较。实验表明，WoMine-i可以找到所有类型的模式，并提取使用现有技术无法挖掘的信息。

下载地址 | 返回目录 | [10.1007/978-3-319-65000-5_19]

[51] Discovering and exploring state-based models for multi-perspective processes (2016)

(van Eck, Maikel L. and Sidorova, Natalia and van der Aalst, Wil M.P. | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: Process mining provides fact-based insights into process behaviour captured in event data. In this work we aim to discover models for processes where different facets, or perspectives, of the process can be identified. Instead of focussing on the events or activities that are executed in the context of a particular process, we concentrate on the states of the different perspectives and discover how they are related. We present a formalisation of these relations and an approach to discover state-based models highlighting them. The approach has been implemented using the process mining framework ProM and provides a highly interactive visualisation of the multi-perspective state-based models. This tool has been evaluated on the BPI Challenge 2012 data of a loan application process and on product user behaviour data gathered by Philips during the development of a smart baby bottle equipped with various sensors.

摘要： 过程挖掘为事件数据中捕获的过程行为提供了基于事实的见解。在这项工作中，我们旨在发现可以识别过程的不同方面或观点的过程模型。与其关注于在特定过程中执行的事件或活动，不如关注于不同视角的状态并发现它们之间的关系。我们提出了这些关系的形式化以及发现突出显示它们的基于状态的模型的方法。该方法已使用流程挖掘框架ProM实施，并提供了基于多角度状态的模型的高度交互式可视化。此工具已在贷款申请流程的BPI Challenge 2012数据以及飞利浦在开发配备各种传感器的智能婴儿奶瓶期间收集的产品用户行为数据上进行了评估。

下载地址 | 返回目录 | [10.1007/978-3-319-45348-4_9]

[52] Discovering block-structured process models from event logs - A constructive approach (2013)

(Leemans, Sander J.J. and Fahland, Dirk and Van Der Aalst | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: Process discovery is the problem of, given a log of observed behaviour, finding a process model that best describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm guarantees to return a fitting model (i.e., able to reproduce all observed behaviour) that is sound (free of deadlocks and other anomalies) in finite time. We present an extensible framework to discover from any given log a set of block-structured process models that are sound and fit the observed behaviour. In addition we characterise the minimal information required in the log to rediscover a particular process model. We then provide a polynomial-time algorithm for discovering a sound, fitting, block-structured model from any given log; we give sufficient conditions on the log for which our algorithm returns a model that is language-equivalent to the process model underlying the log, including unseen behaviour. The technique is implemented in a prototypical tool. textcopyright 2013 Springer-Verlag.

摘要： 过程发现是给定观察到的行为的记录的问题，它找到了一个最佳描述这种行为的过程模型。已经提出了各种各样的过程发现算法。但是，现有的算法不能保证返回拟合模型（即，能够重现所有观察到的行为）在有限时间内是健全的（没有死锁和其他异常）。此外，我们描述了日志中重新发现特定过程模型所需的最少信息，然后提供了多项式时间算法来从任何给定日志中发现声音，拟合，块结构模型；我们给出了充分的条件我们的算法为其返回的日志所使用的模型与该日志所基于的过程模型在语言上等效，包括看不见的行为。校准工具。 textcopyright 2013 Springer-Verlag。

下载地址 | 返回目录 | [10.1007/978-3-642-38697-8_17]

[53] Discovering models of software processes from event-based data (1998)

(Cook, Jonathan E. and Wolf, Alexander L. | ACM Transactions on Software Engineering and Methodology)

Abstract: Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be difficult, costly, and error prone. This presents a practical barrier to the adoption of process technologies, which would be lowered by automated assistance in creating formal models. To this end, we have developed a data analysis technique that we term process discovery. Under this technique, data describing process events are first captured from an on-going process and then used to generate a formal model of the behavior of that process. In this article we describe a Markov method that we developed specifically for process discovery, as well as describe two additional methods that we adopted from other domains and augmented for our purposes. The three methods range from the purely algorithmic to the purely statistical. We compare the methods and discuss their application in an industrial case study. textcopyright 1998 ACM.

摘要： 许多软件过程方法和工具以过程的正式模型为前提。不幸的是，为进行中的复杂过程开发正式模型可能很困难，成本很高并且容易出错。这为采用工艺技术带来了实际障碍，可以通过自动协助创建正式模型来降低工艺技术。为此，我们开发了一种称为过程发现的数据分析技术。在这种技术下，首先从正在进行的流程中获取描述流程事件的数据，然后将其用于生成该流程行为的正式模型。在本文中，我们描述了专门为过程发现而开发的Markov方法，并描述了我们从其他领域采用并出于我们的目的而扩展的两种其他方法。三种方法的范围从纯算法到纯统计。我们比较这些方法，并讨论它们在工业案例研究中的应用。 textcopyright 1998 ACM。

下载地址 | 返回目录 | [10.1145/287000.287001]

[54] Discovering more precise process models from event logs by filtering out chaotic activities (2019)

(Tax, Niek and Sidorova, Natalia and van der Aalst, Wil M.P. | Journal of Intelligent Information Systems)

Abstract: Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary points in time. We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques. The current modus operandi for filtering activities from event logs is to simply filter out infrequent activities. We show that frequency-based filtering of activities does not solve the problems that are caused by chaotic activities. Moreover, we propose a novel technique to filter out chaotic activities from event logs. We evaluate this technique on a collection of seventeen real-life event logs that originate from both the business process management domain and the smart home environment domain. As demonstrated, the developed activity filtering methods enable the discovery of process models that are more behaviorally specific compared to process models that are discovered using standard frequency-based filtering.

摘要： 流程发现与流程模型的自动生成有关，该流程模型根据该业务流程的执行数据来描述该业务流程。现实生活中的事件日志可能包含混乱的活动。这些活动与流程的状态无关，因此可以在任意时间点进行。我们表明，事件日志中此类混乱活动的存在严重影响可以使用过程发现技术发现的过程模型的质量。当前从事件日志中过滤活动的方式是简单地过滤掉不频繁的活动。我们表明，基于频率的活动过滤不能解决由混乱活动引起的问题。此外，我们提出了一种从事件日志中过滤掉混乱活动的新技术。我们在来自业务流程管理域和智能家居环境域的17个现实事件日志的集合上评估了该技术。如图所示，与使用标准基于频率的过滤发现的过程模型相比，开发的活动过滤方法能够发现行为更特定的过程模型。

下载地址 | 返回目录 | [10.1007/s10844-018-0507-6]

[55] Discovering process models from event multiset (2012)

(Wang, Dongyi and Ge, Jidong and Hu, Hao and Luo, Bin and Huang, Liguo | Expert Systems with Applications)

Abstract: The aim of process mining is to discover the process model from the event log which is recorded by the information system. Typical steps of process mining algorithm can be described as: (1) generating event traces from event log, (2) analyzing event traces and obtaining ordering relations of tasks, (3) generating process model with ordering relations of tasks. The first two steps could be very time consuming involving millions of events and thousands of event traces. This paper presents a novel algorithm ( $lambda$ -algorithm) which almost eliminates these two steps in generating event traces from event log and analyzing event traces so as to reduce the performance of process mining algorithm. Firstly, we retrieve the event multiset (input data of algorithm marked as MS) which records the frequency of each event but ignores their orders when extracted from event logs. The event in event multiset contains the information of post-activities. Secondly, we obtain ordering relations from event multiset. The ordering relations contain causal dependency, potential parallelism and non-potential parallelism. Finally, we discover a process models with ordering relations. The complexity of $lambda$ -algorithm is only bound up with the event classes (the set of events in event logs) that has significantly improved the performance of existing process mining algorithms and is expected to be more practical in real-world process mining based on event logs, as well as being able to detect SWF-nets, short-loops and most of implicit dependency (generated by non-free choice constructions). textcopyright 2012 Elsevier Ltd. All rights reserved.

摘要： 流程挖掘的目的是从信息系统记录的事件日志中发现流程模型。流程挖掘算法的典型步骤可以描述为：（1）从事件日志生成事件跟踪，（2）分析事件跟踪并获得任务的排序关系，（3）生成具有任务排序关系的流程模型。前两个步骤可能非常耗时，涉及数百万个事件和数千个事件跟踪。本文提出了一种新颖的算法（$ lambda $ -algorithm），该算法几乎消除了从事件日志生成事件跟踪和分析事件跟踪这两个步骤，从而降低了流程挖掘算法的性能。首先，我们检索事件多集（标记为MS的算法输入数据），该集记录了每个事件的发生频率，但从事件日志中提取时忽略了它们的顺序。事件多集中的事件包含活动后信息。其次，我们从事件多集获得排序关系。排序关系包含因果相关性，潜在并行性和非潜在并行性。最后，我们发现具有订购关系的过程模型。 $ lambda $-算法的复杂性仅与事件类（事件日志中的事件集）绑定在一起，后者显着提高了现有流程挖掘算法的性能，并有望在实际流程中更加实用根据事件日志进行挖掘，并能够检测SWF网络，短循环和大多数隐式依赖性（由非自由选择构造生成）。 textcopyright 2012 Elsevier Ltd.保留所有权利。

下载地址 | 返回目录 | [10.1016/j.eswa.2012.03.064]

[56] Discovering queues from event logs with varying levels of information (2016)

(Senderovich, Arik and Leemans, Sander J.J. and Harel, Shahar and Gal, Avigdor and Mandelbaum, Avishai and van der Aalst, Wil M.P. | Lecture Notes in Business Information Processing)

Abstract: Detecting and measuring resource queues is central to business process optimization. Queue mining techniques allow for the identification of bottlenecks and other process inefficiencies, based on event data. This work focuses on the discovery of resource queues. In particular, we investigate the impact of available information in an event log on the ability to accurately discover queue lengths, i.e. the number of cases waiting for an activity. Full queueing information, i.e. timestamps of enqueueing and exiting the queue, makes queue discovery trivial. However, often we see only the completions of activities. Therefore, we focus our analysis on logs with partial information, such as missing enqueueing times or missing both enqueueing and service start times. The proposed discovery algorithms handle concurrency and make use of statistical methods for discovering queues under this uncertainty. We evaluate the techniques using real-life event logs. A thorough analysis of the empirical results provides insights into the influence of information levels in the log on the accuracy of the measurements.

摘要： 检测和测量资源队列对于业务流程优化至关重要。队列挖掘技术可根据事件数据识别瓶颈和其他流程效率低下的问题。这项工作的重点是发现资源队列。特别是，我们调查了事件日志中可用信息对准确发现队列长度（即等待活动的案例数）的能力的影响。完整的排队信息，即排队和退出队列的时间戳，使队列发现变得微不足道。但是，通常我们只能看到活动的完成。因此，我们将分析的重点放在具有部分信息的日志上，例如缺少排队时间或缺少排队时间和服务启动时间。所提出的发现算法处理并发并利用统计方法在这种不确定性下发现队列。我们使用现实事件日志评估技术。对经验结果的透彻分析提供了对日志中信息水平对测量准确性的影响的见解。

下载地址 | 返回目录 | [10.1007/978-3-319-42887-1_13]

[57] Discovering workflow nets using integer linear programming (2018)

(van Zelst, S. J. and van Dongen, B. F. and Vanxa0der Aalst | Computing)

Abstract: Process mining is concerned with the analysis, understanding and improvement of business processes. Process discovery, i.e. discovering a process model based on an event log, is considered the most challenging process mining task. State-of-the-art process discovery algorithms only discover local control flow patterns and are unable to discover complex, non-local patterns. Region theory based techniques, i.e. an established class of process discovery techniques, do allow for discovering such patterns. However, applying region theory directly results in complex, overfitting models, which is less desirable. Moreover, region theory does not cope with guarantees provided by state-of-the-art process discovery algorithms, both w.r.t. structural and behavioural properties of the discovered process models. In this paper we present an ILP-based process discovery approach, based on region theory, that guarantees to discover relaxed sound workflow nets. Moreover, we devise a filtering algorithm, based on the internal working of the ILP-formulation, that is able to cope with the presence of infrequent, exceptional behaviour. We have extensively evaluated the technique using different event logs with different levels of exceptional behaviour. Our experiments show that the presented approach allows us to leverage the inherent shortcomings of existing region-based approaches. The techniques presented are implemented and readily available in the HybridILPMiner package in the open-source process mining tool-kits ProM (http://promtools.org) and RapidProM (http://rapidprom.org).

摘要： 流程挖掘与业务流程的分析，理解和改进有关。流程发现，即基于事件日志发现流程模型，被认为是最具挑战性的流程挖掘任务。最新的过程发现算法仅发现本地控制流模式，而无法发现复杂的非本地模式。基于区域理论的技术，即已建立的一类过程发现技术，确实允许发现这种模式。但是，直接应用区域理论会导致复杂的过拟合模型，这是不太理想的。此外，区域理论不能满足最新工艺发现算法所提供的保证。发现的过程模型的结构和行为特性。在本文中，我们提出一种基于区域理论的，基于ILP的过程发现方法，该方法可确保发现轻松的声音工作流网络。此外，我们基于ILP公式的内部工作设计了一种过滤算法，该算法能够应对偶发的异常行为的出现。我们已经使用具有不同级别异常行为的不同事件日志对技术进行了广泛的评估。我们的实验表明，提出的方法使我们能够利用现有的基于区域的方法的固有缺点。所介绍的技术已在开源过程挖掘工具包ProM（http://promtools.org）和RapidProM（http://rapidprom.org）的HybridILPMiner程序包中实现并易于使用。

下载地址 | 返回目录 | [10.1007/s00607-017-0582-5]

[58] Discovery of frequent episodes in event sequences (1997)

(Mannila, Heikki and Toivonen, Hannu and Verkamo, Inkeri | Data Mining and Knowledge Discovery)

Abstract: Sequences of events describing the behavior and actions of users or systems can be collected in several domains. An episode is a collection of events that occur relatively close to each other in a given partial order. We consider the problem of discovering frequently occurring episodes in a sequence. Once such episodes are known, one can produce rules for describing or predicting the behavior of the sequence. We give efficient algorithms for the discovery of all frequent episodes from a given class of episodes, and present detailed experimental results. The methods are in use in telecommunication alarm management. textcopyright 1997 Kluwer Academic Publishers.

摘要： 描述用户或系统的行为和动作的事件序列可以在几个域中收集。情节是事件的集合，这些事件以给定的部分顺序彼此相对靠近。我们考虑发现序列中频繁发生的情节的问题。一旦知道了这些情节，就可以产生描述或预测序列行为的规则。我们提供了用于从给定类别的情节中发现所有频繁情节的有效算法，并提供了详细的实验结果。该方法正在电信警报管理中使用。 textcopyright 1997年Kluwer学术出版社。

下载地址 | 返回目录 | [10.1023/A:1009748302351]

[59] Enhancing proceb mining results using domain knowledge (2015)

(Dixit, P. M. and Buijs, J. C.A.M. and Van Der Aalst | CEUR Workshop Proceedings)

Abstract: Proceb discovery algorithms typically aim at discovering proceb models from event logs. Most discovery algorithms discover the model based on an event log, without allowing the domain expert to inuence the discovery approach in any way. However, the user may have certain domain expertise which should be exploited to create a bet-Ter proceb model. In this paper, we addreb this ibue of incorporating domain knowledge to improve the discovered proceb model. We firstly present a modification algorithm to modify a discovered proceb model. Furthermore, we present a verification algorithm to verify the presence of user specified constraints in the model. The outcome of our approach is a Pareto front of proceb models based on the constraints specified by the domain expert and common quality dimensions of proceb mining.

摘要： Proceb发现算法通常旨在从事件日志中发现proceb模型。大多数发现算法都是基于事件日志来发现模型的，而不允许域专家以任何方式影响发现方法。但是，用户可能具有某些领域专业知识，应利用这些专业知识来创建bet-Ter proceb模型。在本文中，我们将结合领域知识来改进发现的proceb模型。我们首先提出一种修改算法来修改已发现的proceb模型。此外，我们提出了一种验证算法，以验证模型中用户指定约束的存在。我们的方法的结果是基于领域专家指定的约束和proceb挖掘的常见质量维度的proceb模型的Pareto前沿。

下载地址 | 返回目录 | []

[60] Event abstraction for process mining using supervised learning techniques (2018)

(Tax, Niek and Sidorova, Natalia and Haakma, Reinder and van der Aalst, Wil M.P. | Lecture Notes in Networks and Systems)

Abstract: Process mining techniques focus on extracting insight in processes from event logs. In many cases, events recorded in the event log are too fine-grained, causing process discovery algorithms to discover incomprehensible process models or process models that are not representative of the event log. We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity. This gives rise to the challenge to bridge the gap between an original low-level event log and a desired high-level perspective on this log, such that a more structured or more comprehensible process model can be discovered. We show that supervised learning can be leveraged for the event abstraction task when annotations with high-level interpretations of the low-level events are available for a subset of the sequences (i.e., traces). We present a method to generate feature vector representations of events based on XES extensions, and describe an approach to abstract events in an event log with Condition Random Fields using these event features. Furthermore, we propose a sequence-focused metric to evaluate supervised event abstraction results that fits closely to the tasks of process discovery and conformance checking. We conclude this paper by demonstrating the usefulness of supervised event abstraction for obtaining more structured and/or more comprehensible process models using both real life event data and synthetic event data.

摘要： 流程挖掘技术专注于从事件日志中提取流程的见解。在许多情况下，事件日志中记录的事件过于精细，导致流程发现算法发现无法理解的流程模型或不代表事件日志的流程模型。我们显示出，当流程发现算法只能从低级事件日志中发现不具代表性的流程模型时，在某些情况下，仍然可以通过首先将事件日志抽象到更高的粒度级别来发现流程中的结构。这就提出了挑战，要求弥合原始低级事件日志和该日志上所需的高级视角之间的差距，从而可以发现更结构化或更易于理解的过程模型。我们显示，当具有低层事件的高级解释的注释可用于序列的子集（即轨迹）时，监督学习可用于事件抽象任务。我们提出了一种基于XES扩展生成事件的特征矢量表示的方法，并描述了使用这些事件特征使用条件随机字段在事件日志中抽象事件的方法。此外，我们提出了一种以序列为中心的度量来评估受监督事件抽象结果，该结果非常适合于过程发现和一致性检查的任务。我们通过证明监督事件抽象在使用现实事件数据和综合事件数据获得更结构化和/或更易理解的过程模型方面的有用性来结束本文。

下载地址 | 返回目录 | [10.1007/978-3-319-56994-9_18]

[61] Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs (2017)

(Suriadi, S. and Andrews, R. and ter Hofstede, A. H.M. and Wynn, M. T. | Information Systems)

Abstract: Process-oriented data mining (process mining) uses algorithms and data (in the form of event logs) to construct models that aim to provide insights into organisational processes. The quality of the data (both form and content) presented to the modeling algorithms is critical to the success of the process mining exercise. Cleaning event logs to address quality issues prior to conducting a process mining analysis is a necessary, but generally tedious and ad hoc task. In this paper we describe a set of data quality issues, distilled from our experiences in conducting process mining analyses, commonly found in process mining event logs or encountered while preparing event logs from raw data sources. We show that patterns are used in a variety of domains as a means for describing commonly encountered problems and solutions. The main contributions of this article are in showing that a patterns-based approach is applicable to documenting commonly encountered event log quality issues, the formulation of a set of components for describing event log quality issues as patterns, and the description of a collection of 11 event log imperfection patterns distilled from our experiences in preparing event logs. We postulate that a systematic approach to using such a pattern repository to identify and repair event log quality issues benefits both the process of preparing an event log and the quality of the resulting event log. The relevance of the pattern-based approach is illustrated via application of the patterns in a case study and through an evaluation by researchers and practitioners in the field.

摘要： 面向过程的数据挖掘（过程挖掘）使用算法和数据（以事件日志的形式）构建旨在提供对组织过程的见解的模型。呈现给建模算法的数据质量（形式和内容）对于流程挖掘活动的成功至关重要。在进行过程挖掘分析之前，清理事件日志以解决质量问题是必要的，但通常是乏味且临时的任务。在本文中，我们描述了一组数据质量问题，这些问题是从我们进行过程挖掘分析的经验中提炼出来的，这些问题通常在过程挖掘事件日志中发现，或者在从原始数据源准备事件日志时遇到。我们表明，模式已在各种领域中用作描述常见问题和解决方案的手段。本文的主要贡献在于表明基于模式的方法适用于记录常见的事件日志质量问题，制定一组用于将事件日志质量问题描述为模式的组件以及对11种集合的描述从我们准备事件日志的经验中提炼出的事件日志缺陷模式。我们假设使用这种模式存储库来识别和修复事件日志质量问题的系统方法，既有益于准备事件日志的过程，也有利于所得事件日志的质量。通过案例研究中模式的应用以及该领域研究人员和实践者的评估，说明了基于模式的方法的相关性。

下载地址 | 返回目录 | [10.1016/j.is.2016.07.011]

[62] Event stream-based process discovery using abstract representations (2018)

(van Zelst, Sebastiaan J. and van Dongen, Boudewijn F. and van der Aalst, Wil M.P. | Knowledge and Information Systems)

Abstract: The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper, we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining toolkit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.

摘要： 过程发现的目的源自过程挖掘领域，是基于业务过程执行数据发现过程模型。大多数过程发现技术都依赖于事件日志作为输入。事件日志是捕获业务流程执行情况的历史数据的静态来源。在本文中，我们专注于依赖在线业务流程执行事件流的流程发现。从事件流中学习过程模型既带来挑战也带来机遇，即我们需要使用有限的内存（最好是恒定的时间）来处理无限量的数据。我们提出一种通用的体系结构，该体系结构允许在事件流的上下文中采用几种现有的过程发现技术。此外，我们提供了该体系结构的几个实例，以及过程挖掘工具包ProM（http://promtools.org）中的实现。使用这些实例，我们评估了基于流的流程发现的多个维度。评估表明，所提出的体系结构使我们能够将过程发现提升到流域。

下载地址 | 返回目录 | [10.1007/s10115-017-1060-2]

[63] Event-based detection of concurrency (1998)

(Cook, Jonathan E. and Wolf, Alexander L. | ACM SIGSOFT Software Engineering Notes)

Abstract: Understanding the behavior of a system is crucial in being able to modify, maintain, and improve the system. A particularly difficult aspect of some system behaviors is concurrency. While there are many techniques to specify intended concurrent behavior, there are few, if any, techniques to capture and model actual concurrent behavior. This paper presents a technique to discover patterns of concurrent behavior from traces of system events. The technique is based on a probabilistic analysis of the event traces. Using metrics for the number, frequency, and regularity of event occurrences, a determination is made of the likely concurrent behavior being manifested by the system. The technique is useful in a wide variety of software engineering tasks, including architecture discovery, reengineering, user interaction modeling, and software process improvement.

摘要： 了解系统的行为对于能够修改，维护和改进系统至关重要。某些系统行为的一个特别困难的方面是并发。尽管有很多技术可以指定预期的并发行为，但是，只有很少的技术可以捕获并建模实际的并发行为。本文提出了一种从系统事件的痕迹中发现并发行为模式的技术。该技术基于事件跟踪的概率分析。使用事件发生的数量，频率和规律性的度量，可以确定系统所表现出的可能的并发行为。该技术可用于多种软件工程任务，包括体系结构发现，再工程，用户交互建模和软件过程改进。

下载地址 | 返回目录 | [10.1145/291252.288214]

[64] Finding Structure in Unstructured Processes: The Case for Process Mining (2007)

(Van Der Aalst | Proceedings - 7th International Conference on Application of Concurrency to System Design, ACSD 2007)

Abstract: Today there are many process mining techniques that allow for the automatic construction of process models based on event logs. Unlike synthesis techniques (e.g., based on regions), process mining aims at the discovery of models (e.g., Petri nets) from incomplete information (i.e., only example behavior is given). The more mature process mining techniques perform well on structured processes. However, most of the existing techniques fail miserably when confronted with unstructured processes. This paper attempts to bring structure to the unstructured by using an integrated combination of abstraction and clustering techniques. The ultimate goal is to present process models that are understandable by analysts and that lead to improved system/process redesigns. textcopyright 2007 IEEE.

摘要： 今天，有许多过程挖掘技术可以基于事件日志自动构建过程模型。与综合技术（例如基于区域）不同，过程挖掘的目的是从不完整的信息（即仅给出示例行为）中发现模型（例如Petri网）。比较成熟的过程挖掘技术在结构化过程中表现良好。但是，大多数现有技术在遇到非结构化过程时都会惨遭失败。本文尝试通过将抽象技术和聚类技术结合在一起，将结构带入非结构化。最终目标是提出分析人员可以理解的流程模型，并导致改进的系统/流程重新设计。 textcopyright 2007 IEEE。

下载地址 | 返回目录 | [10.1109/ACSD.2007.50]

[65] Flexible heuristics miner (FHM) (2011)

(Weijters, A. J.M.M. and Ribeiro, J. T.S. | IEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDM 2011: 2011 IEEE Symposium on Computational Intelligence and Data Mining)

Abstract: One of the aims of process mining is to retrieve a process model from a given event log. However, current techniques have problems when mining processes that contain nontrivial constructs, processes that are low structured and/or dealing with the presence of noise in the event logs. To overcome these problems, a new process representation language is presented in combination with an accompanying process mining algorithm. The most significant property of the new representation language is in the way the semantics of splits and joins are represented; by using so-called split/join frequency tables. This results in easy to understand process models even in the case of non-trivial constructs, low structured domains and the presence of noise. This paper explains the new process representation language and how the mining algorithm works. The algorithm is implemented as a plug-in in the ProM framework. An illustrative example with noise and a real life log of a complex and low structured process are used to explicate the presented approach. textcopyright 2011 IEEE.

摘要： 过程挖掘的目的之一是从给定的事件日志中检索过程模型。然而，当挖掘包含非平凡构造的过程，结构化程度低和/或处理事件日志中存在噪声的过程时，当前技术存在问题。为了克服这些问题，提出了一种新的过程表示语言，并结合了一种随附的过程挖掘算法。新表示语言的最重要特性是表示拆分和联接的语义的方式。通过使用所谓的分离/联合频率表。即使在非平凡的构造，低结构域和存在噪音的情况下，这也导致易于理解的过程模型。本文介绍了新的过程表示语言以及挖掘算法的工作原理。该算法作为ProM框架中的插件实现。具有噪声和复杂且结构化程度较低的过程的真实日志的说明性示例用于说明所提出的方法。 textcopyright 2011 IEEE。

下载地址 | 返回目录 | [10.1109/CIDM.2011.5949453]

[66] Fuzzy Mining (2007)

(Van Der Aalst | Proceedings of the 5th International Conference on Business Process Management (BPM 2007) 24-28 September 2007)

Abstract: Process Mining is a technique for extracting process models from execution logs. This is particularly useful in situations where people have an idealized view of reality. Real-life processes turn out to be less structured than people tend to believe. Unfortunately, traditional process mining approaches have problems dealing with unstructured processes. The discovered models are often spaghetti-like, showing all details without distinguishing what is important and what is not. This paper proposes a new process mining approach to overcome this problem. The approach is configurable and allows for different faithfully simplified views of a particular process. To do this, the concept of a roadmap is used as a metaphor. Just like different roadmaps provide suitable abstractions of reality, process models should provide meaningful abstractions of operational processes encountered in domains ranging from healthcare and logistics to web services and public administration.

摘要： Process Mining是一种从执行日志中提取流程模型的技术。这在人们对现实有理想化看法的情况下尤其有用。事实证明，现实生活中的过程没有人们想象的那么结构化。不幸的是，传统的过程挖掘方法在处理非结构化过程方面存在问题。发现的模型通常是意大利面式的，显示所有细节而没有区分重要和不重要。本文提出了一种新的过程挖掘方法来克服这一问题。该方法是可配置的，并允许对特定过程进行不同的忠实简化视图。为此，将路线图的概念用作隐喻。就像不同的路线图提供合适的现实抽象一样，流程模型也应该提供从医疗保健和物流到Web服务和公共管理等领域所遇到的运营流程的有意义的抽象。

下载地址 | 返回目录 | [10.1007/978-3-540-75183-0]

[67] Genetic process mining: An experimental evaluation (2007)

(De Medeiros | Data Mining and Knowledge Discovery)

Abstract: One of the aims of process mining is to retrieve a process model from an event log. The discovered models can be used as objective starting points during the deployment of process-aware information systems (Dumas et al., eds., Process-Aware Information Systems: Bridging People and Software Through Process Technology. Wiley, New York, 2005) and/or as a feedback mechanism to check prescribed models against enacted ones. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. Most of the problems happen because many current techniques are based on local information in the event log. To overcome these problems, we try to use genetic algorithms to mine process models. The main motivation is to benefit from the global search performed by this kind of algorithms. The non-trivial constructs are tackled by choosing an internal representation that supports them. The problem of noise is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The main challenge in a genetic approach is the definition of a good fitness measure because it guides the global search performed by the genetic algorithm. This paper explains how the genetic algorithm works. Experiments with synthetic and real-life logs show that the fitness measure indeed leads to the mining of process models that are complete (can reproduce all the behavior in the log) and precise (do not allow for extra behavior that cannot be derived from the event log). The genetic algorithm is implemented as a plug-in in the ProM framework. textcopyright Springer Science+Business Media, LLC 2007.

摘要： 过程挖掘的目的之一是从事件日志中检索过程模型。发现的模型可以用作过程感知信息系统部署的客观起点（Dumas等编着，过程感知信息系统：通过过程技术桥接人与软件。Wiley，纽约，2005年）和/或作为一种反馈机制，以根据制定的模型检查规定的模型。但是，当开采包含非平凡构造的过程时和/或处理原木中的噪声时，当前的技术存在问题。发生大多数问题是因为许多当前技术都是基于事件日志中的本地信息。为了克服这些问题，我们尝试使用遗传算法来挖掘过程模型。主要动机是要受益于这种算法执行的全局搜索。通过选择支持它们的内部表示，可以解决非平凡的构造。遗传算法自然可以解决噪声问题，因为按照定义，这些算法对噪声具有鲁棒性。遗传方法的主要挑战是定义良好的适应性度量，因为它可以指导遗传算法执行的全局搜索。本文介绍了遗传算法的工作原理。使用合成日志和真实日志进行的实验表明，适应性度量确实导致了对过程模型的挖掘，这些过程模型是完整的（可以重现日志中的所有行为）并且是精确的（不允许发生无法从事件中得出的额外行为）日志）。遗传算法被实现为ProM框架中的插件。 textcopyright Springer Science + Business Media，LLC2007。

下载地址 | 返回目录 | [10.1007/s10618-006-0061-7]

[68] Heuristic mining revamped: An interactive, data-Aware, and conformance-Aware miner (2017)

(Mannhardt, Felix and De Leoni | CEUR Workshop Proceedings)

Abstract: Process discovery methods automatically infer process models based on events logs that are recorded by information systems. Several heuristic process discovery methods have been proposed to cope with less structured processes and the presence of noise in the event log. However, (1) a large parameter space needs to be explored, (2) several of the many available heuristics can be chosen from, (3) data attributes are not used for discovery, (4) discovered models are not visualized as described in literature, and (5) existing tools do not give reliable quality diagnostics for discovered models. We present the interactive Data-Aware Heuristics Miner (iDHM), a modular tool that attempts to address those five issues. The iDHM enables quick interactive exploration of the parameter space and several heuristics. It uses data attributes to improve the discovery procedure and provides built-in conformance checking to get direct feedback on the quality of the model. It is the first tool that visualizes models using the concise Causal Net (C-Net) notation. We provide a walk-Through of the iDHM by applying it to a large event log with hospital billing information.

摘要： 过程发现方法根据信息系统记录的事件日志自动推断过程模型。已经提出了几种启发式过程发现方法来应对结构化程度较低的过程以及事件日志中存在噪声的情况。但是，（1）需要探索较大的参数空间；（2）可以从许多可用的启发式方法中选择几种；（3）数据属性不用于发现；（4）所发现的模型不可视化，如文献，以及（5）现有工具无法为发现的模型提供可靠的质量诊断。我们提出了交互式数据感知启发式挖掘器（iDHM），这是一种模块化工具，旨在解决这五个问题。 iDHM支持对参数空间和几种启发式方法进行快速交互探索。它使用数据属性来改进发现过程，并提供内置的一致性检查以获取有关模型质量的直接反馈。它是第一个使用简洁的因果网（C-Net）表示法可视化模型的工具。我们通过将iDHM应用于带有医院帐单信息的大型事件日志中来提供iDHM的遍历。

下载地址 | 返回目录 | []

[69] Hierarchy process mining from multi-source logs (2017)

(Sarno, Riyanarto and Effendi, Yutika Amelia | Telkomnika (Telecommunication Computing Electronics and Control))

Abstract: Nowadays, large-scale business processes is growing rapidly; in this regards process mining is required to discover and enhance business processes in different departments of an organization. A process mining algorithm can generally discover the process model of an organization without considering the detailed process models of the departments, and the relationship among departments. The exchange of messages among departments can produce asynchronous activities among department process models. The event logs from departments can be considered as multi-source logs, which cause difficulties in mining the process model. Discovering process models from multi-source logs is still in the state of the art, therefore this paper proposes a hierarchy high-to-low process mining approach to discover the process model from a complex multi-source and heterogeneous event logs collected from distributed departments. The proposed method involves three steps; i.e. firstly a high level process model is developed; secondly a separate low level process model is discovered from multi-source logs; finally the Petri net refinement operation is used to integrate the discovered process models. The refinement operation replaced the abctract transitions of a high level process model with the corresponding low level process models. Multi-source event logs from several departments of a yarn manufacturing were used in the computational study, and the results showed that the proposed method combined with the modified time-based heuristics miner could discover a correct parallel process business model containing XOR, AND, and OR relations.

摘要： 如今，大型业务流程正在迅速发展；在这方面，需要进行流程挖掘以发现和增强组织不同部门中的业务流程。流程挖掘算法通常可以发现组织的流程模型，而无需考虑部门的详细流程模型以及部门之间的关系。部门之间的消息交换可以在部门流程模型之间产生异步活动。来自部门的事件日志可以视为多源日志，这在挖掘过程模型时会造成困难。从多源日志中发现过程模型仍处于最新状态，因此，本文提出了一种从高到低的分层过程挖掘方法，以从分布式部门收集的复杂的多源和异构事件日志中发现过程模型。。所提出的方法包括三个步骤。即，首先建立高级过程模型；其次，从多源日志中发现一个单独的低级流程模型。最后，使用Petri网精炼操作集成发现的过程模型。细化操作用相应的低级过程模型代替了高级过程模型的abctract转换。计算研究中使用了来自多个纱线生产部门的多源事件日志，结果表明，该方法与改进的基于时间的启发式挖掘器相结合，可以发现包含XOR，AND和OR关系。

下载地址 | 返回目录 | [10.12928/TELKOMNIKA.v15i4.6326]

[70] How to synthesize nets from languages - A survey (2007)

(Lorenz, Robert and Mauser, Sebastian and Juhas, Gabriel | Proceedings - Winter Simulation Conference)

Abstract: In this paper we present a survey on methods for the synthesis of Petri nets from behavioral descriptions given as languages. We consider place/transition Petri nets, elementary Petri nets and Petri nets with inhibitor arcs. For each net class we consider classical languages, step languages and partial languages as behavioral description. All methods are based on the notion of regions of languages. We identify two different types of regions and two different principles of computing from the set of regions of a language a finite Petri net generating this language. For finite or regular languages almost each combination of Petri net class, language type, region type and computation principle can be considered to compute such a net. Altogether, we present a framework for region-based synthesis of Petri nets from languages which integrates almost all known approaches and fills several remaining gaps in literature. textcopyright 2007 IEEE.

摘要： 在本文中，我们对从以语言给出的行为描述中合成Petri网的方法进行了调查。我们考虑放置/过渡Petri网，基本Petri网和带有抑制弧的Petri网。对于每个网络类，我们将古典语言，步骤语言和部分语言视为行为描述。所有方法都基于语言区域的概念。我们从一种语言的区域集（一种生成该语言的有限Petri网）中识别出两种不同类型的区域和两种不同的计算原理。对于有限或常规语言，几乎可以考虑使用Petri网类，语言类型，区域类型和计算原理的每种组合来计算这样的网。总而言之，我们提出了一种基于语言的基于区域的Petri网合成的框架，该框架集成了几乎所有已知的方法，并填补了文献中剩余的空白。 textcopyright 2007 IEEE。

下载地址 | 返回目录 | [10.1109/WSC.2007.4419657]

[71] Infrequent pattern mining in smart healthcare environment using data summarization (2018)

(Ahmed, Mohiuddin and Barkat Ullah | Journal of Supercomputing)

Abstract: A summarization technique creates a concise version of large amount of data (big data!) which reduces the computational cost of analysis and decision-making. There are interesting data patterns, such as rare anomalies, which are more infrequent in nature than other data instances. For example, in smart healthcare environment, the proportion of infrequent patterns is very low in the underlying cyber physical system (CPS). Existing summarization techniques overlook the issue of representing such interesting infrequent patterns in a summary. In this paper, a novel clustering-based technique is proposed which uses an information theoretic measure to identify the infrequent frequent patterns for inclusion in a summary. The experiments conducted on seven benchmark CPS datasets show substantially good results in terms of including the infrequent patterns in summaries than existing techniques.

摘要： 汇总技术可创建大量数据（大数据！）的简洁版本，从而降低了分析和决策的计算成本。有一些有趣的数据模式，例如罕见的异常，它们在本质上比其他数据实例更不常见。例如，在智能医疗环境中，底层网络物理系统（CPS）中的不频繁模式所占的比例非常低。现有的摘要技术忽略了在摘要中表示这种有趣的罕见模式的问题。在本文中，提出了一种基于聚类的新技术，该技术使用信息理论方法来识别要包含在摘要中的不频繁模式。在七个基准CPS数据集上进行的实验显示，与现有技术相比，在汇总中包括了不常见的模式方面，具有相当好的结果。

下载地址 | 返回目录 | [10.1007/s11227-018-2376-8]

[72] K-Means Clustering With Outlier Removal (2017)

(Gan, Guojun and Ng, Michael Kwok Po | Pattern Recognition Letters)

Abstract: Outlier detection is an important data analysis task in its own right and removing the outliers from clusters can improve the clustering accuracy. In this paper, we extend the k-means algorithm to provide data clustering and outlier detection simultaneously by introducing an additional cluster to the k-means algorithm to hold all outliers. We design an iterative procedure to optimize the objective function of the proposed algorithm and establish the convergence of the iterative procedure. Numerical experiments on both synthetic data and real data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

摘要： 异常值检测本身就是一项重要的数据分析任务，从聚类中删除异常值可以提高聚类的准确性。在本文中，我们通过将额外的聚类引入k-means算法来容纳所有异常值，从而扩展k-means算法以同时提供数据聚类和离群值检测。我们设计了一个迭代程序来优化所提出算法的目标函数，并建立了迭代程序的收敛性。通过对合成数据和真实数据进行数值实验，证明了该算法的有效性和有效性。

下载地址 | 返回目录 | [10.1016/j.patrec.2017.03.008]

[73] Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface (2011)

(Katoen, Joost Pieter and Konig, Barbara | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: Gossip protocols have been proposed as a robust and efficient method for disseminating information throughout large-scale networks. In this paper, we propose a compositional analysis technique to study formal probabilistic models of gossip protocols in the context of wireless sensor networks. We introduce a simple probabilistic timed process calculus for modelling wireless sensor networks. A simulation theory is developed to compare probabilistic protocols that have similar behaviour up to a certain probability. This theory is used to prove a number of algebraic laws which revealed to be very effective to evaluate the performances of gossip networks with and without communication collisions.

摘要： Gossip协议已被提出为一种在整个大型网络中传播信息的强大而有效的方法。在本文中，我们提出了一种成分分析技术，以研究在无线传感器网络环境中的八卦协议的正式概率模型。我们介绍了一种简单的概率定时过程演算，用于对无线传感器网络进行建模。开发了一种模拟理论来比较概率协议，这些协议具有相似的行为直到一定概率。该理论用于证明许多代数定律，这些定律对评估有或没有通信冲突的八卦网络的性能非常有效。

下载地址 | 返回目录 | [10.1007/978-3-642-23217-6]

[74] Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface (2011)

(Katoen, Joost Pieter and Konig, Barbara | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

下载地址 | 返回目录 | [10.1007/978-3-642-23217-6]

[75] Linking data and process perspectives for conformance analysis (2018)

(Alizadeh, Mahdi and Lu, Xixi and Fahland, Dirk and Zannone, Nicola and van der Aalst, Wil M.P. | Computers and Security)

Abstract: The detection of data breaches has become a major challenge for most organizations. The problem lies in the fact that organizations often lack proper mechanisms to control and monitor users activities and their data usage. Although several auditing approaches have been proposed to assess the compliance of actual executed behavior, existing approaches focus on either checking data accesses against security policies (data perspective) or checking user activities against the activities needed to conduct business processes (process perspective). Analyzing user behavior from these perspectives independently may not be sufficient to expose security incidents. In particular, security incidents may remain undetected or diagnosed incorrectly. This paper proposes a novel auditing approach that reconciles the data and process perspectives, thus enabling the identification of a large range of deviations. In particular, we analyze and classify deviations with respect to the intended purpose of data and the context in which data are used, and provide a novel algorithm to identify non-conforming user behavior. The approach has been implemented in the open source framework ProM and was evaluated through both controlled experiments and a case study using real-life event data. The results show that the approach is able to accurately identify deviations in both data usage and control-flow, while providing the purpose and context of the identified deviations.

摘要： 数据泄露的检测已成为大多数组织的主要挑战。问题在于组织经常缺乏适当的机制来控制和监视用户的活动及其数据使用情况。尽管已提出了几种审核方法来评估数据泄露。遵从实际执行行为的要求，现有方法侧重于根据安全策略检查数据访问（数据角度）或根据进行业务流程所需的活动检查用户活动（流程角度），仅从这些角度分析用户行为可能不足以揭露安全事件，尤其是安全事件可能仍未被发现或诊断不正确，本文提出了一种新颖的审计方法，该方法可以协调数据和流程的观点，从而能够识别大范围的偏差，尤其是，我们对偏差进行分析和分类关于d的预期目的数据以及使用数据的上下文，并提供了一种新颖的算法来识别不合格的用户行为。该方法已在开源框架ProM中实现，并通过受控实验和使用现实事件数据的案例研究进行了评估。结果表明，该方法能够准确地识别数据使用和控制流中的偏差，同时提供所识别偏差的目的和上下文。

下载地址 | 返回目录 | [10.1016/j.cose.2017.10.010]

[76] Log mining to re-construct system behavior: An exploratory study on a large telescope system (2019)

(Pettinato, Michele and Gil, Juan Pablo and Galeas, Patricio and Russo, Barbara | Information and Software Technology)

Abstract: Context: A large amount of information about system behavior is stored in logs that record system changes. Such information can be exploited to discover anomalies of a system and the operations that cause them. Given their large size, manual inspection of logs is hard and infeasible in a desired timeframe (e.g., real-time), especially for critical systems. Objective: This study proposes a semi-automated method for reconstructing sequences of tasks of a system, revealing system anomalies, and associating tasks and anomalies to code components. Method: The proposed approach uses unsupervised machine learning (Latent Dirichlet Allocation) to discover latent topics in messages of log events and introduces a novel technique based on pattern recognition to derive the semantic of such topics (topic labelling). The approach has been applied to the big data generated by the ALMA telescope system consisting of more than 2000 log events collected in about five hours of telescope operation. Results: With the application of our approach to such data, we were able to model the behavior of the telescope over 16 different observations. We found five different behavior models and three different types of errors. We use the models to interpret each error and discuss its cause. Conclusions: With this work, we have also been able to discuss some of the known challenges in log mining. The experience we gather has been then summarized in lessons learned.

摘要： 上下文：有关系统行为的大量信息存储在记录系统更改的日志中。可以利用此类信息来发现系统异常以及引起异常的操作。考虑到日志的大小，在所需的时间范围内（例如实时），手动进行日志检查非常困难且不可行，尤其是对于关键系统而言。目的：本研究提出了一种半自动化方法，用于重构系统任务序列，揭示系统异常以及将任务和异常与代码组件相关联。方法：所提出的方法使用无监督机器学习（潜在狄利克雷分配）来发现日志事件消息中的潜在主题，并引入了一种基于模式识别的新技术来导出此类主题的语义（主题标记）。该方法已应用于由ALMA望远镜系统生成的大数据，该数据由在大约五个小时的望远镜操作中收集到的2000多个日志事件组成。结果：通过将我们的方法应用于此类数据，我们能够对16种不同观测值的望远镜行为进行建模。我们发现了五个不同的行为模型和三种不同类型的错误。我们使用模型来解释每个错误并讨论其原因。结论：通过这项工作，我们还能够讨论日志挖掘中的一些已知挑战。然后，我们在总结的经验教训中总结了我们收集的经验。

下载地址 | 返回目录 | [10.1016/j.infsof.2019.06.011]

[77] Minimal infrequent pattern based approach for mining outliers in data streams (2015)

(Sweetlin Hemalatha | Expert Systems with Applications)

Abstract: Outlier detection is an important task in data mining which aims at detecting patterns that are unusual in a dataset. Though several techniques are proved to be useful in solving some outlier detection problems, there are certain issues yet to be resolved. Most of the existing methods compute distance of points in full dimensional space to detect outliers. But in high dimensional space, the concept of proximity may not be qualitatively meaningful due to the curse of dimensionality and incurs high computational cost. Moreover, the existing methods focus on discovering outliers but do not provide the interpretability of different subspaces that cause the abnormality. Frequent pattern mining based approaches resolve the aforementioned issues. Recently, infrequent pattern mining has attracted the attention of data mining research community which aims at discovering rare associations and researches in this area motivated to propose a new method to detect outliers in data streams. Infrequent patterns are more interesting than frequent patterns in some domains such as fraudulent credit transactions, anomaly detection, etc. In such applications, mining infrequent patterns facilitates detecting outliers. Minimal infrequent patterns are generators of family of infrequent patterns. In this paper, a novel method is presented to detect outliers by mining minimal infrequent patterns from data streams. Three measures namely Transaction Weighting Factor (TWF), Minimal Infrequent Deviation Factor (MIPDF) and Minimal Infrequent Pattern based Outlier Factor (MIFPOF) are defined. An algorithm called Minimal Infrequent Pattern based Outlier Detection (MIFPOD) method is proposed for detecting outliers in data streams based on mined minimal infrequent patterns. The effectiveness of the proposed method is demonstrated on synthetic dataset obtained from vital dataset collected from body sensors and a publicly available real dataset. The experimental results have shown that the proposed method outperforms the existing methods in detecting outliers.

摘要： 异常值检测是数据挖掘中的重要任务，其目的是检测数据集中异常的模式。尽管已证明几种技术可用于解决某些异常检测问题，但仍有一些问题尚待解决。现有的大多数方法都会计算整个维空间中的点的距离以检测离群值。但是在高维空间中，由于维数的诅咒，接近度的概念可能在质量上没有意义，并且会导致较高的计算成本。而且，现有方法着重于发现异常值，但是不提供引起异常的不同子空间的可解释性。基于频繁模式挖掘的方法解决了上述问题。近年来，不频繁的模式挖掘引起了数据挖掘研究界的关注，该领域旨在发现该领域中的稀有关联，并研究提出了一种新的方法来检测数据流中的异常值。在某些领域（例如欺诈性信用交易，异常检测等）中，不经常使用的模式比经常使用的模式更有趣。在此类应用程序中，挖掘不经常使用的模式有助于检测异常值。最小的不频繁模式是不频繁模式族的生成器。在本文中，提出了一种通过从数据流中挖掘最小的不频繁模式来检测异常值的新方法。定义了三个度量，即交易加权因子（TWF），最小不频繁偏差因子（MIPDF）和最小不频繁基于模式的离群因子（MIFPOF）。提出了一种基于最小不频繁模式的离群值检测算法（MIFPOD），该算法基于挖掘的最小不频繁模式来检测数据流中的离群值。在从人体传感器收集的生命数据集和可公开获得的真实数据集获得的合成数据集上证明了该方法的有效性。实验结果表明，该方法在检测异常值方面优于现有方法。

下载地址 | 返回目录 | [10.1016/j.eswa.2014.09.053]

[78] Mining frequent patterns in process models (2019)

(Chapela-Campa, David and Mucientes, Manuel and Lama, Manuel | Information Sciences)

Abstract: Process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from event logs and by offering techniques to discover, monitor and enhance real processes. In the discovery of process models, retrieving a complex one, i.e., a hardly readable process model, can hinder the extraction of information. Even in well-structured process models, there is information that cannot be obtained with the current techniques. In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model. Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs. This proposal has been validated with a set of process models, including some from BPI Challenges, and compared with the state of the art techniques. Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.

摘要： 过程挖掘已成为一种通过从事件日志中提取知识并提供发现，监视和增强实际过程的技术来分析组织行为的方法。在发现过程模型时，检索复杂的模型，即难以读取的过程模型，可能会阻碍信息的提取。即使在结构合理的过程模型中，也存在无法通过当前技术获得的信息。在本文中，我们提出了WoMine，这是一种从模型中检索频繁行为模式的算法。我们的方法在过程模型中进行搜索，以提取具有序列，选择，并行和循环的结构，这些结构经常在日志中执行。该建议书已通过一系列流程模型（包括来自BPI Challenges的一些流程模型）进行了验证，并与最新技术进行了比较。实验证明，WoMine可以找到所有类型的模式，并提取使用最新技术无法挖掘的信息。

下载地址 | 返回目录 | [10.1016/j.ins.2018.09.011]

[79] Mining infrequent patterns in data stream (2014)

(Lakshmi, R. and Hemalatha, C. Sweetlin and Vaidehi, V. | 2014 International Conference on Recent Trends in Information Technology, ICRTIT 2014)

Abstract: In recent years researches are focused towards mining infrequent patterns rather than frequent patterns. Mining infrequent pattern plays vital role in detecting any abnormal event. In this paper, an algorithm named Infrequent Pattern Miner for Data Streams (IPM-DS) is proposed for mining nonzero infrequent patterns from data streams. The proposed algorithm adopts the FP-growth based approach for generating all infrequent patterns. The proposed algorithm (IPM-DS) is evaluated using health data set collected from wearable physiological sensors that measure vital parameters such as Heart Rate (HR), Breathing Rate (BR), Oxygen Saturation (SPO2) and Blood pressure (BP) and also with two publically available data sets such as e-coli and Wine from UCI repository. The experimental results show that the proposed algorithm generates all possible infrequent patterns in less time.

摘要： 近年来，研究集中在挖掘不频繁的模式，而不是频繁的模式。罕见的挖掘模式在检测任何异常事件中起着至关重要的作用。在本文中，提出了一种用于数据流的不频繁模式挖掘器（IPM-DS）的算法，用于从数据流中挖掘非零不频繁模式。所提出的算法采用基于FP增长的方法来生成所有不频繁的模式。使用从可穿戴生理传感器收集的健康数据集评估提出的算法（IPM-DS），该传感器测量诸如心率（HR），呼吸率（BR），血氧饱和度（SPO2）和血压（BP）等重要参数包含两个公开可用的数据集，例如UCI存储库中的e-coli和Wine。实验结果表明，该算法在更短的时间内生成了所有可能的不频繁模式。

下载地址 | 返回目录 | [10.1109/ICRTIT.2014.6996199]

[80] Mining local process models (2016)

(Tax, Niek and Sidorova, Natalia and Haakma, Reinder and van der Aalst, Wil M.P. | Journal of Innovation in Digital Ecosystems)

Abstract: In this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as $backslash$ emphlocal process models. Local process model mining can be positioned in-between process discovery and episode / sequential pattern mining. The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining. However, we do not look at start-to-end models, which distinguishes our approach from process discovery and creates a link to episode / sequential pattern mining. We propose an incremental procedure for building local process models capturing frequent patterns based on so-called process trees. We propose five quality dimensions and corresponding metrics for local process models, given an event log. We show monotonicity properties for some quality dimensions, enabling a speedup of local process model discovery through pruning. We demonstrate through a real life case study that mining local patterns allows us to get insights in processes where regular start-to-end process discovery techniques are only able to learn unstructured, flower-like, models.

摘要： 在本文中，我们描述了一种在事件日志中发现频繁行为模式的方法。我们将这些模式表示为$ 反斜杠$ emph 本地流程模型。本地过程模型挖掘可以位于过程发现和情节/顺序模式挖掘之间。本文介绍的技术能够学习涉及顺序组成，并发，选择和循环的行为模式，例如在过程挖掘中。但是，我们不关注始端模型，后者将我们的方法与过程发现区分开来，并创建了指向情节/顺序模式挖掘的链接。我们提出了一种增量过程，用于基于所谓的过程树来构建捕获频繁模式的本地过程模型。给定事件日志，我们为本地流程模型提出了五个质量维度和相应的指标。我们显示了某些质量维度的单调性，从而可以通过修剪加快本地过程模型的发现。我们通过现实生活中的案例研究证明，挖掘局部模式可以使我们深入了解常规的从始至终的流程发现技术只能学习非结构化，类似花朵的模型的流程。

[下载地址](http://arxiv.org/abs/1606.06066 http://dx.doi.org/10.1016/j.jides.2016.11.001 http://link.springer.com/10.1007/11494744_5 http://www.scopus.com/inward/record.url?eid=2-s2.0-84944916586&partnerID=40&md5=be23d25182d9cff3bb2885a599febe4d http://journals.sa) | 返回目录 | [10.1016/j.jides.2016.11.001]

[81] Mining the Usability of Business Process Modeling Tools: Concept and Case Study (2015)

(Thaler, Tom and Maurer, Dirk and Angelis, Vittorio De and Fettke, Peter and Loos, Peter | Proceedings of the Industry Track at the 13th International Conference on Business Process Management 2015. Business Process Management (BPM-15), September 1-3, Innsbruck, Austria)

Abstract: Business process models are key artifacts in business process management. The technical support of the process of process modeling is important for the quality and the applicability of the resulting models. The quality of that technical support plays an important role in the selection of corresponding software products and is a crucial characteristic of differentiation. Nevertheless, only little knowledge on the tool-specific line of actions and the corresponding challenges in the daily work of modelers is available, which makes it hard to improve a modeling tool against customer requirements. In order to address that conflict, we develop a method based on process mining, allowing the continuous analysis of modeling tools and the applied processes of process modeling with regard to software usability aspects. The resulting method containing the phases user monitoring, trace clustering, usage model derivation, usage model analysis, recommendation derivation and implementation primarily aims at a target-oriented design and further development of business process modeling tools and is evaluated with the ARIS Designer by performing a user study. The results allow promising estimations for an application of the method in a broader context.

摘要： 业务流程模型是业务流程管理中的关键工件。过程建模过程的技术支持对于所得模型的质量和适用性至关重要。技术支持的质量在选择相应的软件产品中起着重要作用，并且是差异化的关键特征。但是，只有很少的工具特定操作知识以及建模人员日常工作中的相应挑战，这使他们很难根据客户需求改进建模工具。为了解决该冲突，我们开发了一种基于过程挖掘的方法，允许就软件可用性方面对建模工具和过程建模的应用过程进行连续分析。包含阶段用户监视，跟踪群集，使用模型推导，使用模型分析，推荐推导和实施的最终方法主要针对面向目标的设计和业务流程建模工具的进一步开发，并通过执行ARIS Designer进行评估。用户研究。结果为在更广泛的环境中应用该方法提供了有希望的估计。

下载地址 | 返回目录 | []

[82] Mining variable fragments from process event logs (2017)

(Pourmasoumi, Asef and Kahani, Mohsen and Bagheri, Ebrahim | Information Systems Frontiers)

Abstract: Many peer-organizations are now using process-aware information systems for managing their organizational processes. Most of these peer-organizations have shared processes, which include many commonalities and some degrees of variability. Analyzing and mining the commonalities of these processes can have many benefits from the reusability point of view. In this paper, we propose an approach for extracting common process fragments from a collection of event logs. To this end, we first analyze the process fragment literature from a theoretical point of view, based on which we present a new process fragment definition, called morphological fragments to support composability and flexibility. Then we propose a novel algorithm for extracting such morphological fragments directly from process event logs. This algorithm is capable of eliciting common fragments from a family of processes that may not have been executed within the same application/organization. We also propose supporting algorithms for detecting and categorizing morphological fragments for the purpose of reusability. Our empirical studies show that our approach is able to support reusability and flexibility in process fragment identification.

摘要： 许多同级组织现在正在使用过程感知信息系统来管理其组织过程。这些同级组织中的大多数组织都有共享的流程，其中包括许多共性和一定程度的可变性。从可重用性的角度来看，分析和挖掘这些过程的共性可以带来很多好处。在本文中，我们提出了一种从事件日志集合中提取常见流程片段的方法。为此，我们首先从理论角度分析过程片段文献，在此基础上，我们提出了一种新的过程片段定义，称为形态片段，以支持可组合性和灵活性。然后，我们提出了一种直接从过程事件日志中提取此类形态片段的新颖算法。此算法能够从同一应用程序/组织中可能尚未执行的一系列过程中引出常见片段。我们还提出了用于重用形态的检测和分类形态学片段的支持算法。我们的经验研究表明，我们的方法能够支持过程片段识别中的可重用性和灵活性。

下载地址 | 返回目录 | [10.1007/s10796-016-9662-x]

[83] Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering (2017)

(Deb, Anwesha Barai and Dey, Lopamudra | World Journal of Computer Application and Technology)

Abstract: An outlier in a pattern is dissimilar with rest of the pattern in a dataset. Outlier detection is an important issue in data mining. It has been used to detect and remove anomalous objects from data. Outliers occur due to mechanical faults, changes in system behavior, fraudulent behavior, and human errors. This paper describes the methodology or detecting and removing outlier in K-Means and Hierarchical clustering. First apply clustering algorithm K-means and Hierarchical clustering on a data set then find outliers from the each resulting clustering. In K-Means clustering outliers are found by distance based approach and cluster based approach. In case of hierarchical clustering, by using dendrogram outliers are found. The goal of the project is to detect the outlier and remove the outliers to make the clustering more reliable.

摘要： 模式中的异常值与数据集中的其余模式不同。离群检测是数据挖掘中的重要问题。它已用于检测和删除数据中的异常对象。由于机械故障，系统行为的变化，欺诈行为和人为错误而导致出现异常值。本文介绍了在K均值和层次聚类中的方法或检测和消除异常值。首先在数据集上应用聚类算法K均值和层次聚类，然后从每个结果聚类中找到离群值。在K-Means中，通过基于距离的方法和基于聚类的方法找到离群值。在分层聚类的情况下，通过使用树状图可以发现异常值。该项目的目标是检测异常值并消除异常值，以使聚类更加可靠。

下载地址 | 返回目录 | [10.13189/wjcat.2017.050202]

[84] PM2: A process mining project methodology (2015)

(Van Eck | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: Process mining aims to transform event data recorded in information systems into knowledge of an organisations business processes. The results of process mining analysis can be used to improve process performance or compliance to rules and regulations. However, applying process mining in practice is not trivial. In this paper we introduce PM2, a methodology to guide the execution of process mining projects. We successfully applied PM2 during a case study within IBM, a multinational technology corporation, where we identified potential process improvements for one of their purchasing processes.

摘要： 流程挖掘旨在将信息系统中记录的事件数据转换为组织业务流程的知识。流程挖掘分析的结果可用于提高流程性能或遵守规则和法规。但是，在实践中应用流程挖掘并不是在本文中，我们介绍了指导流程挖掘项目执行的方法PM2。我们在跨国技术公司IBM的案例研究中成功地应用了PM2，在那里我们确定了其中一项采购流程的潜在流程改进。

下载地址 | 返回目录 | [10.1007/978-3-319-19069-3_19]

[85] Preface: 9th International workshop on business process intelligence (BPI 2013) (2014)

(van der Aalst, Wil and de Medeiros, Ana Karla Alves and Benatallah, Boualem and Gaaloul, Walid and Greco, Gianluigi and Grigori, Daniela and Guzzo, Antonella and Leyer, Michael and Mendling, Jan and Pastor, Oscar and Popova, Viara and Reichert, Manfred and Rosemann, Michael and Rozinat, Anne and Sacca, Domenico and Soffer, Phina and Sperduti, Alessandro and Weigand, Hans and Weske, Mathias | Lecture Notes in Business Information Processing)

Abstract: Compliance specifications concisely describe selected aspects of what a business operation should adhere to. To enable automated techniques for compliance checking, it is important that these requirements are specified correctly and precisely, describing exactly the behavior intended. Although there are rigorous mathematical formalisms for representing compliance rules, these are often perceived to be difficult to use for business users. Regardless of notation, however, there are often subtle but important details in compliance requirements that need to be considered. The main challenge in compliance checking is to bridge the gap between informal description and a precise specification of all requirements. In this paper, we present an approach which aims to facilitate creating and understanding formal compliance requirements by providing configurable templates that capture these details as options for commonly-required compliance requirements. These options are configured interactively with end-users, using question trees and natural language. The approach is implemented in the Process Mining Toolkit ProM. textcopyright Springer International Publishing Switzerland 2014.

摘要： 合规性规范简要描述了业务运营应遵循的某些方面。为了启用用于合规性检查的自动化技术，正确，准确地指定这些要求并准确描述预期的行为非常重要。尽管存在严格的数学形式主义来表示合规性规则，但通常认为这些规则对商业用户而言很难使用。但是，无论使用什么符号，合规要求中通常都需要考虑一些细微但重要的细节。符合性检查的主要挑战是弥合非正式描述与所有要求的精确规范之间的鸿沟。在本文中，我们提出一种方法，旨在通过提供可配置的模板来促进创建和理解正式的合规性要求，该模板将这些详细信息捕获为常见要求的合规性要求的选项。这些选项是使用问题树和自然语言与最终用户交互配置的。该方法在Process Mining Toolkit ProM中实现。 textcopyright瑞士Springer国际出版社，2014年。

下载地址 | 返回目录 | [10.1007/978-3-319-06257-0]

[86] PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth (2001)

(Pei, J. and Han, J. and Mortazavi-Asl, B. and Pinto, H. and Chen, Q. and Dayal, U. and Hsu, M. C. | Proceedings - International Conference on Data Engineering)

Abstract: Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of Apriori which may substantially reduce the number of combinations to be examined. However Apriori still encounters problems when a sequence database is large and/or when sequential patterns to be mined are numerous and/or long. In this paper, we propose a novel sequential pattern mining method, called PrefixSpan (i.e., Prefix-projected Sequential pattern mining), which explores prefix-projection in sequential pattern mining. PrefixSpan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover, prefix-projection substantially reduces the size of projected databases and leads to efficient processing. Our performance study shows that PrefixSpan outperforms both the Apriori-based GSP algorithm and another recently proposed method, FreeSpan, in mining large sequence databases.

摘要： 顺序模式挖掘是一个具有广泛应用的重要数据挖掘问题。这是具有挑战性的，因为可能需要检查数量庞大的可能子序列模式。大多数先前开发的顺序模式挖掘方法都遵循Apriori的方法，该方法可以大大减少要检查的组合的数量。但是，当序列数据库很大和/或要挖掘的顺序模式很多和/或很长时，Apriori仍然会遇到问题。在本文中，我们提出了一种新颖的顺序模式挖掘方法，称为PrefixSpan（即前缀投影的顺序模式挖掘），它探索了顺序模式挖掘中的前缀投影。 PrefixSpan挖掘完整的模式集，但大大减少了候选子序列生成的工作量。此外，前缀投影大大减少了投影数据库的大小，并导致有效的处理。我们的性能研究表明，在挖掘大型序列数据库中，PrefixSpan优于基于Apriori的GSP算法和最近提出的另一种方法FreeSpan。

下载地址 | 返回目录 | [10.1109/icde.2001.914830]

[87] ProDiGen: Mining complete, precise and minimal structure process models with a genetic algorithm (2015)

(Vazquez-Barreiros, Borja and Mucientes, Manuel and Lama, Manuel | Information Sciences)

Abstract: Process discovery techniques automatically extract the real workflow of a process by analyzing the events that are collected and stored in log files. Although in the last years several process discovery algorithms have been presented, none of them guarantees to find complete, precise and simple models for all the given logs. In this paper we address the problem of process discovery through a genetic algorithm with a new fitness function that takes into account both completeness, precision and simplicity. ProDiGen (Process Discovery through a Genetic algorithm) includes new definitions for precision and simplicity, and specific crossover and mutation operators. The proposal has been validated with 39 process models and several noise levels, giving a total of 111 different logs. We have compared our approach with the state of the art algorithms; non-parametric statistical tests show that our algorithm outperforms the other approaches, and that the difference is statistically significant.

摘要： 流程发现技术通过分析收集并存储在日志文件中的事件，自动提取流程的实际工作流程。尽管最近几年提出了几种过程发现算法，但是它们都不能保证为所有给定的日志找到完整，准确和简单的模型。在本文中，我们通过具有新的适应度函数的遗传算法解决了过程发现的问题，该函数同时兼顾了完整性，准确性和简单性。 ProDiGen（通过遗传算法进行过程发现）包括精确度和简便性的新定义，以及特定的交叉和变异算子。该提案已通过39个过程模型和多个噪声级别进行了验证，总共提供111种不同的日志。我们已经将我们的方法与最先进的算法进行了比较。非参数统计测试表明，我们的算法优于其他方法，并且差异具有统计意义。

下载地址 | 返回目录 | [10.1016/j.ins.2014.09.057]

[88] Process Mining a Comparative Study (2014)

(GUPTA, ESMITA .P. | Ijarcce)

Abstract: The systems that support today s globally distributed and agile businesses are steadily growing in size and generating numerous events. Business Intelligence aims to support and improve decision making processes by providing methods and tools for analyzing the data. Process mining builds the bridge between Data Mining as a Business Intelligence approach and Business Process Management. Its primary objective is the discovery of process models based on available event log data. Many process mining algorithms have been proposed recently, there does not exist a widely-accepted benchmark to evaluate and compare these process mining algorithms. As a result, it can be difficult to choose a suitable process mining algorithm for a given enterprise or application domain. This paper proposes a solution to evaluate and compare these process mining algorithms efficiently, so that businesses can efficiently select the process mining algorithms that are most suitable for a given model set.

摘要： 支持当今全球分布和敏捷业务的系统规模正在稳步增长，并产生大量事件。商业智能旨在通过提供分析数据的方法和工具来支持和改进决策过程。数据挖掘作为一种商业智能方法和业务流程管理，其主要目的是基于可用的事件日志数据发现流程模型，最近提出了许多流程挖掘算法，目前还没有一个广泛接受的评估和比较基准因此，很难为给定的企业或应用领域选择合适的过程挖掘算法，本文提出了一种有效评估和比较这些过程挖掘算法的解决方案，以便企业可以有效地选择这些过程挖掘算法。最适合给定模型集的过程挖掘算法。

下载地址 | 返回目录 | [10.17148/ijarcce.2014.31154]

[89] Process Mining with the HeuristicsMiner Algorithm (2006)

(Weijters, A.J.M.M. and van der Aalst, W M P and de Medeiros;, A K Alves | Beta working papers)

Abstract: The basic idea of process mining is to extract knowledge from event logs recorded by an information system. Until recently, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing tech- niques and tools for discovering process, organizational, social, and per- formance information from event logs. Fuelled by the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), process mining has become a vivid research area 1, 2. In this paper we introduce the challenging process mining domain and discuss a heuristics driven process mining algorithm; the so-called HeuristicsMiner in detail. HeuristicsMiner is a practical applicable mining algorithm that can deal with noise, and can be used to express the main behavior (i.e. not all details and exceptions) registered in an event log. In the experimental section of this paper we introduce benchmark material (12.000 different event logs) and measurements by which the performance of process mining algorithms can be measured.

摘要： 过程挖掘的基本思想是从信息系统记录的事件日志中提取知识。直到最近，这些事件日志中的信息仍很少用于分析基础流程。流程挖掘旨在通过提供从事件日志中发现流程，组织，社会和绩效信息的技术和工具来改善这一状况。在事务信息系统中无处不在的事件日志的推动下（参见WFM，ERP，CRM，SCM和B2B系统），流程挖掘已成为一个生动的研究领域1、2。在本文中，我们介绍了具有挑战性的流程挖掘领域和讨论启发式驱动的过程挖掘算法；所谓的HeuristicsMiner进行了详细介绍。 HeuristicsMiner是一种实用的，适用于挖掘的算法，可以处理噪声，并且可以用来表示事件日志中注册的主要行为（即并非所有详细信息和异常）。在本文的实验部分，我们介绍了基准材料（12.000种不同的事件日志）和测量方法，可用来测量过程挖掘算法的性能。

下载地址 | 返回目录 | [10.1.1.324.1075]

[90] Process Mining with the HeuristicsMiner Algorithm (2006)

(Weijters, A.J.M.M. and van der Aalst, W M P and de Medeiros;, A K Alves | Beta working papers)

下载地址 | 返回目录 | []

[91] Process Mining (2005)

(Van Der Aalst | Process-Aware Information Systems: Bridging People and Software through Process Technology)

Abstract: The basic idea of process mining is to extract knowledge from event logs recorded by an information system. Until recently, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. Process mining is useful for several reasons. It can be used as a tool to find out how people and/or procedures really work, i.e., process discovery. However, it can also be used to do Delta analysis (e.g., comparing an SAP reference model with an actual event log) or performance analysis. This chapter introduces the concept of process mining and describes one of the techniques in more detail. textcopyright 2005 John Wiley & Sons, Inc.

摘要： 过程挖掘的基本思想是从信息系统记录的事件日志中提取知识。直到最近，这些事件日志中的信息仍很少用于分析基础流程。流程挖掘旨在通过提供从事件日志中发现流程，控制，数据，组织和社会结构的技术和工具来改善这一状况。流程挖掘之所以有用，有几个原因。它可以用作发现人员和/或程序如何真正工作的工具，即过程发现。但是，它也可以用于进行Delta分析（例如，将SAP参考模型与实际事件日志进行比较）或性能分析。本章介绍了流程挖掘的概念，并更详细地介绍了其中的一种技术。 textcopyright 2005 John Wiley ＆ Sons，Inc.

下载地址 | 返回目录 | [10.1002/0471741442.ch10]

[92] Process discovery using integer linear programming (2009)

(Van Der Werf | Fundamenta Informaticae)

Abstract: The research domain of process discovery aims at constructing a process model (e.g. a Petri net) which is an abstract representation of an execution log. Such a model should (1) be able to reproduce the log under consideration and (2) be independent of the number of cases in the log. In this paper, we present a process discovery algorithm where we use concepts taken from the language-based theory of regions, a well-known Petri net research area. We identify a number of shortcomings of this theory from the process discovery perspective, and we provide solutions based on integer linear programming.

摘要： 过程发现的研究领域旨在构建过程模型（例如Petri网），该模型是执行日志的抽象表示。这样的模型应该（1）能够重现所考虑的日志，并且（2）与日志中的案例数无关。在本文中，我们提出了一种过程发现算法，其中使用了来自基于语言的区域理论（著名的Petri网络研究领域）中的概念。我们从过程发现的角度确定了该理论的许多缺点，并提供了基于整数线性规划的解决方案。

下载地址 | 返回目录 | [10.3233/FI-2009-136]

[93] Process mining in healthcare: A literature review (2016)

(Rojas, Eric and Munoz-Gama, Jorge and Sepulveda, Marcos and Capurro, Daniel | Journal of Biomedical Informatics)

Abstract: Process Mining focuses on extracting knowledge from data generated and stored in corporate information systems in order to analyze executed processes. In the healthcare domain, process mining has been used in different case studies, with promising results. Accordingly, we have conducted a literature review of the usage of process mining in healthcare. The scope of this review covers 74 papers with associated case studies, all of which were analyzed according to eleven main aspects, including: process and data types; frequently posed questions; process mining techniques, perspectives and tools; methodologies; implementation and analysis strategies; geographical analysis; and medical fields. The most commonly used categories and emerging topics have been identified, as well as future trends, such as enhancing Hospital Information Systems to become process-aware. This review can: (i) provide a useful overview of the current work being undertaken in this field; (ii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches for their own applications; and (iii) highlight the use of process mining to improve healthcare processes.

摘要： Process Mining专注于从公司信息系统中生成和存储的数据中提取知识，以便分析执行的过程。在医疗保健领域，过程挖掘已用于不同的案例研究中，并取得了可喜的结果。因此，我们对过程挖掘在医疗保健中的用途进行了文献综述。本综述的范围涵盖74篇相关案例研究的论文，所有这些论文均根据11个主要方面进行了分析，包括：过程和数据类型；经常提出的问题；过程挖掘技术，观点和工具；方法；实施和分析策略；地理分析；和医学领域。确定了最常用的类别和新兴主题，以及未来的趋势，例如增强医院信息系统以使其具有流程意识。这项审查可以：（i）提供对该领域目前正在开展的工作的有益概述；（ii）帮助研究人员为自己的应用选择过程挖掘算法，技术，工具，方法论和方法；（iii）强调使用过程挖掘来改善医疗过程。

下载地址 | 返回目录 | [10.1016/j.jbi.2016.04.007]

[94] Process mining in healthcare: Analysis and modeling of processes in the emergency area (2015)

(Orellana Garcia | IEEE Latin America Transactions)

Abstract: Decision making requires a high performance in strategic processes. The process mining is responsible for generating knowledge and discover processes from event logs that are extracted from information systems, for finding errors, inconsistencies and vulnerabilities. To improve its performance, organizations are looking for a better process management approach, which as a first step requires precise modeling of these. In the health sector, an area largely unexplored by researchers in the field, such modeling is even more critical given the nature of this kind of organization. Obtaining these processes is not trivial in many cases, but it is a very complex task. This article aims to generate process models through the ProM tool for obtaining detailed, realistic and easily analyzable views, from records stored in information systems for health processes, particularly in a hospital (which are rich in information and generally tend to be overlooked).

摘要： 决策制定需要战略流程中的高性能。流程挖掘负责生成知识并从从信息系统中提取的事件日志中发现流程，以查找错误，不一致和漏洞。为了提高其性能，组织正在寻找一种更好的流程管理方法，第一步需要对它们进行精确建模。在卫生领域，该领域的研究人员尚未对该领域进行充分探索，鉴于这种组织的性质，这种建模更为关键。在许多情况下，获得这些过程并非易事，但这是一项非常复杂的任务。本文旨在通过ProM工具生成过程模型，以从存储在卫生过程信息系统中的记录中获取详细，现实且易于分析的视图，特别是在医院（信息丰富且通常被忽视）中。

下载地址 | 返回目录 | [10.1109/TLA.2015.7112022]

[95] Process mining in software systems: Discovering real-life business transactions and process models from distributed systems (2015)

(Leemans, Maikel and Van Der Aalst | 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems, MODELS 2015 - Proceedings)

Abstract: This paper presents a novel reverse engineering technique for obtaining real-life event logs from distributed systems. This allows us to analyze the operational processes of software systems under real-life conditions, and use process mining techniques to obtain precise and formal models. Hence, the work can be positioned in-between reverse engineering and process mining. We present a formal definition, implementation and an instrumentation strategy based the joinpoint-pointcut model. Two case studies are used to evaluate our approach. These concrete examples demonstrate the feasibility and usefulness of our approach.

摘要： 本文提出了一种新颖的逆向工程技术，用于从分布式系统中获取现实事件日志。这使我们能够在现实条件下分析软件系统的操作过程，并使用过程挖掘技术来获得精确而正式的模型。因此，可以将工作定位在逆向工程和过程挖掘之间。我们提出基于联接点切入点模型的正式定义，实现和检测策略。两个案例研究用于评估我们的方法。这些具体示例证明了我们方法的可行性和实用性。

下载地址 | 返回目录 | [10.1109/MODELS.2015.7338234]

[96] Process mining in the large: A tutorial (2014)

(van der Aalst, Wil M.P. | Lecture Notes in Business Information Processing)

Abstract: Recently, process mining emerged as a new scientific discipline on the interface between process models and event data. On the one hand, conventional Business Process Management (BPM) andWorkflow Management (WfM) approaches and tools are mostly model-driven with little consideration for event data. On the other hand, Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus on data without considering end-to-end process models. Process mining aims to bridge the gap between BPM and WfM on the one hand and DM, BI, and ML on the other hand. Here, the challenge is to turn torrents of event data (Big Data) into valuable insights related to process performance and compliance. Fortunately, process mining results can be used to identify and understand bottlenecks, inefficiencies, deviations, and risks. This tutorial paper introduces basic process mining techniques that can be used for process discovery and conformance checking. Moreover, some very general decomposition results are discussed. These allow for the decomposition and distribution of process discovery and conformance checking problems, thus enabling process mining in the large. textcopyright Springer International Publishing Switzerland 2014.

摘要： 最近，过程挖掘作为过程模型和事件数据之间的接口上的一门新科学学科而出现。一方面，常规的业务流程管理（BPM）和工作流管理（WfM）方法和工具主要是模型驱动的，很少考虑事件数据。另一方面，数据挖掘（DM），商业智能（BI）和机器学习（ML）专注于数据，而不考虑端到端流程模型。流程挖掘旨在一方面弥补BPM和WfM之间的差距，另一方面弥补DM，BI和ML之间的差距。在这里，挑战在于将大量事件数据（大数据）转变为与流程性能和合规性有关的有价值的见解。幸运的是，过程挖掘结果可用于识别和理解瓶颈，效率低下，偏差和风险。本教程文件介绍了可用于过程发现和一致性检查的基本过程挖掘技术。此外，讨论了一些非常普遍的分解结果。这些允许分解和分布过程发现和一致性检查问题，从而实现大规模的过程挖掘。 textcopyright瑞士Springer国际出版社，2014年。

下载地址 | 返回目录 | [10.1007/978-3-319-05461-2_2]

[97] Process mining put into context (2012)

(Van Der Aalst | IEEE Internet Computing)

Abstract: Process mining techniques help organizations discover and analyze business processes based on raw event data. The recently released Process Mining Manifesto presents guiding principles and challenges for process mining. Here, the authors summarize the manifestos main points and argue that analysts should take into account the context in which events occur when analyzing processes. textcopyright 2006 IEEE.

下载地址 | 返回目录 | [10.1109/MIC.2012.12]

[98] Process mining using BPMN: relating event logs and process models (2017)

(Kalenkova, Anna A. and van der Aalst, Wil M.P. and Lomazova, Irina A. and Rubin, Vladimir A. | Software and Systems Modeling)

Abstract: Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users, since the control flow perspective can be integrated with data and resource perspectives discovered from event logs. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. Thus, on the basis of these conversion algorithms low-level models (such as Petri nets, causal nets and process trees) discovered from event logs using existing approaches can be represented in terms of BPMN. Moreover, we establish behavioral relations between Petri nets and BPMN models and use them to adopt existing conformance checking and performance analysis techniques in order to visualize conformance and performance information within a BPMN diagram. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more complicated BPMN models in comparison with the initial models are identified.

摘要： 过程感知信息系统（PAIS）是依赖于过程的系统，这些过程涉及人力和软件资源以实现具体目标。需要开发用于PAIS中的建模，分析，改进和监视过程的方法。这些方法包括用于从事件日志中发现过程模型，查找日志和模型偏差以及分析过程性能特征的过程挖掘技术。代表性偏差（一种建模过程的方法）在过程挖掘中起着重要作用。 BPMN 2.0（业务流程模型和表示法）标准被广泛使用，并允许构建常规且易于理解的流程模型。除了平面控制流透视图之外，子流程，数据流和资源也可以集成在一个BPMN图中。这使得BPMN对于流程挖掘者和业务用户都非常有吸引力，因为控制流透视图可以与从事件日志中发现的数据和资源透视图集成在一起。在本文中，我们描述并证明了鲁棒的控制流转换算法，该算法为更高级的基于BPMN的发现和一致性检查算法提供了基础。因此，基于这些转换算法，可以使用BPMN表示使用现有方法从事件日志中发现的低级模型（例如Petri网络，因果网络和过程树）。此外，我们在Petri网和BPMN模型之间建立行为关系，并使用它们采用现有的一致性检查和性能分析技术，以便在BPMN图表中可视化一致性和性能信息。我们认为，本文介绍的结果可用于多种BPMN挖掘和一致性检查算法。我们还提供了在转换为BPMN结构之前和之后发现的流程的度量。确定了与初始模型相比，转换算法可以产生更紧凑或更复杂的BPMN模型的情况。

下载地址 | 返回目录 | [10.1007/s10270-015-0502-0]

[99] Process mining with token carried data (2016)

(Li, Chuanyi and Ge, Jidong and Huang, Liguo and Hu, Haiyang and Wu, Budan and Yang, Hongji and Hu, Hao and Luo, Bin | Information Sciences)

Abstract: Process mining is to discover, monitor and improve real processes by extracting the knowledge from logs which are available in todays information systems. The existing process mining algorithms are based on the event logs where only the executions of tasks are recorded. In order to reduce the pre-processing efforts and strengthen the mining ability of the existing process mining algorithms, we have proposed a novel perspective to employ the data carried by tokens recorded in token log which tracks the changes of process resources for process mining in this study. The feasibility of the token logs is proved and the results of pairwise t-tests show that there is no big difference between the efforts that are taken by the same workflow system to generate the token log and the event log. Besides, a process mining algorithm ( $tau$ ) based on the new log is proposed in this paper. With algorithm $tau$ , the mining efficiency as well as the mining capability is improved compared to the traditional event-log-based mining algorithms. We have also developed three plug-ins on top of the existing workflow engine, process modeling and mining platforms (YAWL, PIPE and ProM) for proving the feasibility of token log and realizing the token log generation and algorithm $tau$ .

摘要： 过程挖掘是通过从当今信息系统中可用的日志中提取知识来发现，监视和改善实际过程。现有的过程挖掘算法基于事件日志，其中仅记录任务的执行。通过减少预处理工作并增强现有流程挖掘算法的挖掘能力，我们提出了一种新颖的观点来利用令牌日志中记录的令牌携带的数据，该数据跟踪了本研究中用于流程挖掘的流程资源的变化。证明了令牌日志的可行性，并且成对t检验的结果表明，同一工作流系统生成令牌日志和事件日志所付出的努力之间没有太大差异。本文提出了基于新日志的$ tau $），使用算法$ tau $，与t相比，提高了挖掘效率和挖掘能力。基于传统事件日志的挖掘算法。我们还在现有工作流引擎，流程建模和挖掘平台（YAWL，PIPE和ProM）的基础上开发了三个插件，以证明令牌日志的可行性并实现令牌日志生成和算法$ tau $。

下载地址 | 返回目录 | [10.1016/j.ins.2015.08.050]

[100] Process mining: A research agenda (2004)

(Van der Aalst | Computers in Industry)

Abstract: Enterprise information systems support and control operational business processes ranging from simple internal back-office processes to complex interorganizational processes. Technologies such as workflow management (WFM), enterprise application integration (EAI), enterprise resource planning (ERP), and web services (WS) typically focus on the realization of IT support rather than monitoring the operational business processes. Process mining aims at extracting information from event logs to capture the business process as it is being executed. In this paper, we put the topic of process mining into context, discuss the main issues around process mining, and finally we introduce the papers in this special issue. textcopyright 2003 Elsevier B.V. All rights reserved.

摘要： 企业信息系统支持和控制从简单的内部后台流程到复杂的组织间流程的业务流程。工作流管理（WFM），企业应用程序集成（EAI），企业资源计划（ERP）和Web服务（WS）等技术通常专注于IT支持的实现，而不是监视运营业务流程。流程挖掘旨在从事件日志中提取信息，以捕获正在执行的业务流程。在本文中，我们将过程挖掘的主题放到了上下文中，讨论了过程挖掘的主要问题，最后在本期特刊中介绍了论文。 textcopyright 2003 Elsevier B.V.保留所有权利。

下载地址 | 返回目录 | [10.1016/j.compind.2003.10.001]

[101] Process mining: Data science in action (2016)

(Van der Aalst | Process Mining: Data Science in Action)

Abstract: This is the second edition of Wil van der Aalsts seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.

摘要： 这是Wil van der Aalst关于过程采矿的开创性著作的第二版，该书现在还在更广泛的数据科学和大数据方法的背景下讨论该领域。它包括一些补充和更新，例如关于感应采矿技术，对齐的概念，软件工具的显着扩展部分以及大型过程挖掘的全新篇章是独立的，同时涵盖了从过程发现到预测分析的整个过程挖掘范围。第一部分，第二部分对数据科学和流程挖掘进行了一般性介绍，为理解本书的其余部分提供了业务流程建模和数据挖掘的基础知识；接下来，第三部分着重于将流程发现作为最重要的流程挖掘任务，而第四部分超越了发现流程的控制流，突出了一致性检查以及组织和时间观点，第五部分提供了成功的指南。在实践中成功地应用流程挖掘，包括对广泛使用的开源工具ProM和几种商业产品的介绍。最后，第六部分退后一步，对所介绍的材料和关键的开放挑战进行了反思。总体而言，本书全面概述了过程挖掘的最新技术。它适用于业务流程分析师，业务顾问，流程经理，研究生和BPM研究人员。

下载地址 | 返回目录 | [10.1007/978-3-662-49851-4]

[102] Process mining: Discovering direct successors in process logs (2002)

(Maruster, Laura and Weijters, A. J.M.M.(Ton) and van der Aalst, W. M.P.(Wil) and van den Bosch, Antal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: Workflow management technology requires the existence of explicit process models, i.e. a completely specified workflow design needs to be developed in order to enact a given workflow process. Such a workflow design is time consuming and often subjective and incomplete. We propose a learning method that uses the workflow log, which contains information about the process as it is actually being executed. In our method we will use a logistic regression model to discover the direct connections between events of a realistic not complete workflow log with noise. Experimental results are used to show the usefulness and limitations of the presented method.

摘要： 工作流程管理技术需要存在明确的流程模型，即，为了制定给定的工作流程，需要开发完全指定的工作流程设计。这样的工作流设计是耗时的并且通常是主观的和不完整的。我们提出一种使用工作流日志的学习方法，其中包含有关实际执行过程的信息。在我们的方法中，我们将使用逻辑回归模型来发现现实的，不完整的带有噪声的工作流日志的事件之间的直接联系。实验结果表明了该方法的有效性和局限性。

下载地址 | 返回目录 | [10.1007/3-540-36182-0_37]

[103] Process mining: Extending the a algorithm to mine short loops (2004)

(de Medeiros, A K A and van Dongen, B F and van der Aalst, W M P and Weijters, A J M M | BETA Working Paper Series)

Abstract: The deployment ofWorkflow Management systems is a time- consuming and error-prone task. A possible solution is process min- ing, which automatically extracts workflow models from event-data logs. However, the current research in process mining still has problems in mining some common constructs in workflow models. Among these con- structs are short loops, which are loops of length one and two. For in- stance, the $alpha$ -algorithm was proven to mine sound Structured Workflow nets without short loops. In this paper, we present a new algorithm (the $alpha$ ±algorithm) that can handle short loops, and we prove that it correctly mines all sound Structured Workflow nets. The $alpha$ ±algorithm is based on the $alpha$ -algorithm and is implemented in the EMiT tool.

摘要： 工作流管理系统的部署是一项耗时且容易出错的任务。一种可能的解决方案是过程挖掘，该过程可自动从事件数据日志中提取工作流模型。但是，当前对过程挖掘的研究在挖掘工作流模型中的一些常见构造时仍然存在问题。在这些结构中，有短循环，即长度为1和2的循环。例如，$ alpha $-算法被证明可挖掘无短循环的合理的结构化工作流网络。在本文中，我们提出了一种可以处理短循环的新算法（$ alpha $ ±algorithm），并且证明了该算法可以正确挖掘所有合理的结构化工作流网络。 $ alpha $ ±算法基于$ alpha $-算法，并在EMiT工具中实现。

下载地址 | 返回目录 | []

[104] Process mining: Overview and outlook of Petri net discovery algorithms (2009)

(Van Dongen | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: Within the research domain of process mining, process discovery aims at constructing a process model as an abstract representation of an event log. The goal is to build a model (e.g., a Petri net) that provides insight into the behavior captured in the log. The theory of regions can be used to transform a state-based model or a set of words into a Petri net that exactly mimics the behavior given as input. Recently several papers appeared on the application of the theory of regions for process discovery. This paper provides an overview of different Petri net based discovery algorithms from both the area of process mining and the theory of regions. The overview encompasses five categories of algorithms, for which common assumptions and problems are indicated. Furthermore, based on the shortcomings of the algorithms in each category, a set of directions for future research in the process discovery area is discussed. textcopyright 2009 Springer.

摘要： 在过程挖掘的研究领域中，过程发现旨在构建过程模型作为事件日志的抽象表示。目标是建立模型（例如Petri网），以提供对日志中捕获的行为的洞察力。区域理论可用于将基于状态的模型或一组单词转换为精确模拟作为输入行为的Petri网。最近，出现了几篇关于区域理论在过程发现中的应用的论文。本文从过程挖掘领域和区域理论两个方面概述了基于不同Petri网的发现算法。概述包含五类算法，针对这些算法指出了常见的假设和问题。此外，基于每个类别中算法的缺点，讨论了过程发现领域中未来研究的一组方向。 textcopyright 2009年Springer。

下载地址 | 返回目录 | [10.1007/978-3-642-00899-3_13]

[105] Process simulation and pattern discovery through alpha and heuristic algorithms (2015)

(Premchaiswadi, Wichian and Porouhan, Parham | International Conference on ICT and Knowledge Engineering)

下载地址 | 返回目录 | [10.1109/ICTKE.2015.7368472]

[106] Quality dimensions in process discovery: The importance of fitness, precision, generalization and simplicity (2014)

(Buijs, J. C.A.M. and Van Dongen | International Journal of Cooperative Information Systems)

Abstract: Process discovery algorithms typically aim at discovering process models from event logs that best describe the recorded behavior. Often, the quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. replay fitness. At the same time, there are other measures that compare a model with recorded behavior in terms of the precision of the model and the extent to which the model generalizes the behavior in the log. Furthermore, many measures exist to express the complexity of a model irrespective of the log. In this paper, we first discuss several quality dimensions related to process discovery. We further show that existing process discovery algorithms typically consider at most two out of the four main quality dimensions: replay fitness, precision, generalization and simplicity. Moreover, existing approaches cannot steer the discovery process based on user-defined weights for the four quality dimensions. This paper presents the ETM algorithm which allows the user to seamlessly steer the discovery process based on preferences with respect to the four quality dimensions. We show that all dimensions are important for process discovery. However, it only makes sense to consider precision, generalization and simplicity if the replay fitness is acceptable. textcopyright 2014 World Scientific Publishing Company.

摘要： 过程发现算法通常旨在从事件日志中发现最能描述所记录行为的过程模型。通常，通过量化所得到的模型可以在多大程度上重现日志中的行为，即重放适应度，来测量过程发现算法的质量。同时，还有其他措施可以将模型与记录的行为进行比较，包括模型的精度以及模型在日志中概括行为的程度。此外，存在许多度量来表达模型的复杂性，而与对数无关。在本文中，我们首先讨论与过程发现相关的几个质量维度。我们进一步证明，现有的过程发现算法通常最多考虑以下四个主要质量维度中的两个：重放适应性，精度，泛化性和简单性。此外，现有方法无法基于用户定义的权重来指导四个质量维度的发现过程。本文提出了一种ETM算法，该算法允许用户基于相对于四个质量维度的偏好来无缝地引导发现过程。我们表明，所有维度对于流程发现都很重要。但是，只有重播适用性可以接受时，才需要考虑精度，概括性和简单性。 textcopyright 2014年世界科学出版公司。

下载地址 | 返回目录 | [10.1142/S0218843014400012]

[107] Queue mining for delay prediction in multi-class service processes (2015)

(Senderovich, Arik and Weidlich, Matthias and Gal, Avigdor and Mandelbaum, Avishai | Information Systems)

Abstract: Information systems have been widely adopted to support service processes in various domains, e.g., in the telecommunication, finance, and health sectors. Information recorded by systems during the operation of these processes provides an angle for operational process analysis, commonly referred to as process mining. In this work, we establish a queueing perspective in process mining to address the online delay prediction problem, which refers to the time that the execution of an activity for a running instance of a service process is delayed due to queueing effects. We present predictors that treat queues as first-class citizens and either enhance existing regression-based techniques for process mining or are directly grounded in queueing theory. In particular, our predictors target multi-class service processes, in which requests are classified by a type that influences their processing. Further, we introduce queue mining techniques that derive the predictors from event logs recorded by an information system during process execution. Our evaluation based on large real-world datasets, from the telecommunications and financial sectors, shows that our techniques yield accurate online predictions of case delay and drastically improve over predictors neglecting the queueing perspective.

摘要： 信息系统已被广泛采用，以支持电信，金融和卫生部门等各个领域的服务流程。系统在这些过程的操作过程中记录的信息为操作过程分析（通常称为过程挖掘）提供了一个角度。在这项工作中，我们建立了进程挖掘中的排队透视图，以解决在线延迟预测问题，该问题指的是由于排队效应而导致服务进程的运行实例的活动执行延迟的时间。我们提供了将队列视为一流公民的预测变量，或者增强了现有的基于回归的过程挖掘技术，或者直接基于排队论。特别是，我们的预测变量以多类服务流程为目标，其中按影响其处理的类型对请求进行分类。此外，我们引入了队列挖掘技术，该技术从流程执行期间信息系统记录的事件日志中得出预测变量。我们基于来自电信和金融领域的大型现实数据集的评估表明，我们的技术可以提供准确的在线案例延迟预测，并且相对于忽略排队论的预测指标，它们的性能得到了显着提高。

下载地址 | 返回目录 | [10.1016/j.is.2015.03.010]

[108] Rediscovering workflow models from event-based data using little thumb (2003)

(Weijters, A. J.M.M. and Van der Aalst | Integrated Computer-Aided Engineering)

Abstract: Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we propose a technique for rediscovering workflow models. This technique uses workflow logs to discover the workflow process as it is actually being executed. The workflow log contains information about events taking place. We assume that these events are totally ordered and each event refers to one task being executed for a single case. This information can easily be extracted from transactional information systems (e.g., Enterprise Resource Planning systems such as SAP and Baan). The rediscovering technique proposed in this paper can deal with noise and can also be used to validate workflow processes by uncovering and measuring the discrepancies between prescriptive models and actual process executions.

摘要： 当代工作流程管理系统由明确的流程模型驱动，即，为了制定给定的工作流程，需要完全指定的工作流程设计。创建工作流设计是一个复杂的耗时过程，通常，实际工作流过程与管理层认为的过程之间存在差异。因此，我们提出了一种重新发现工作流模型的技术。该技术使用工作流日志来发现实际执行的工作流过程。工作流日志包含有关发生的事件的信息。我们假设这些事件是完全有序的，并且每个事件都涉及一个针对单个案例执行的任务。可以从交易信息系统（例如，诸如SAP和Baan之类的企业资源计划系统）中轻松提取此信息。本文提出的重新发现技术可以处理噪声，还可以通过发现和测量说明性模型与实际过程执行之间的差异来验证工作流程。

下载地址 | 返回目录 | [10.3233/ica-2003-10205]

[109] Rediscovering workflow models from event-based data (2001)

(Hoste, V. and de Pauw, G. | Proceedings of the eleventh Belgian-Dutch Conference on Machine Learning (BENELEARN 2001), December 21, Antwerp, Belgium)

摘要： 当代工作流程管理系统由明确的流程模型驱动，即，为了制定给定的工作流程，需要完全指定的工作流程设计。创建工作流程设计是一个复杂的耗时过程，通常实际的工作流程与管理人员认为的流程之间存在差异。因此，我们提出了一种用于过程挖掘的技术。该技术使用工作流日志来发现实际执行的工作流过程。本文提出的过程挖掘技术可以处理噪声，还可以通过发现和测量规范模型与实际过程执行之间的差异来验证工作流过程。

下载地址 | 返回目录 | []

[110] Semantics and analysis of business process models in BPMN (2008)

(Dijkman, Remco M. and Dumas, Marlon and Ouyang, Chun | Information and Software Technology)

Abstract: The Business Process Modelling Notation (BPMN) is a standard for capturing business processes in the early phases of systems development. The mix of constructs found in BPMN makes it possible to create models with semantic errors. Such errors are especially serious, because errors in the early phases of systems development are among the most costly and hardest to correct. The ability to statically check the semantic correctness of models is thus a desirable feature for modelling tools based on BPMN. Accordingly, this paper proposes a mapping from BPMN to a formal language, namely Petri nets, for which efficient analysis techniques are available. The proposed mapping has been implemented as a tool that, in conjunction with existing Petri net-based tools, enables the static analysis of BPMN models. The formalisation also led to the identification of deficiencies in the BPMN standard specification. textcopyright 2008 Elsevier B.V. All rights reserved.

摘要： 业务流程建模表示法（BPMN）是用于在系统开发的早期阶段捕获业务流程的标准。 BPMN中发现的各种构造使得创建带有语义错误的模型成为可能。这样的错误尤为严重，因为系统开发早期的错误是代价最高且最难纠正的错误之一。因此，静态检查模型的语义正确性的能力是基于BPMN的建模工具的理想功能。因此，本文提出了从BPMN到正式语言即Petri网的映射，为此可以使用有效的分析技术。拟议的映射已实现为一种工具，可与现有基于Petri网的工具结合使用，从而能够对BPMN模型进行静态分析。形式化还导致识别BPMN标准规范中的缺陷。 textcopyright 2008 Elsevier B.V.保留所有权利。

下载地址 | 返回目录 | [10.1016/j.infsof.2008.02.006]

[111] Simulated annealing overview (2003)

(Busetti, Franco | World Wide Web URL www. geocities. com/ ldots)

Abstract: Simulated annealing (SA) is a random-search technique which exploits $backslash$ nan analogy between the way in which a metal cools and freezes into $backslash$ na minimum energy crystalline structure (the annealing process) and $backslash$ nthe search for a minimum in a more general system; it forms the basis $backslash$ nof an optimisation technique for combinatorial and other problems.

摘要： 模拟退火（SA）是一种随机搜索技术，在金属冷却并冻结成最小能量晶体结构（退火过程）和$ 反斜杠$ n在更通用的系统中搜索最小值；它构成了针对组合问题和其他问题的优化技术的基础反斜杠。

下载地址 | 返回目录 | [10.1.1.66.5018]

[112] Split miner: automated discovery of accurate and simple business process models from event logs (2019)

(Augusto, Adriano and Conforti, Raffaele and Dumas, Marlon and La Rosa | Knowledge and Information Systems)

Abstract: The problem of automated discovery of process models from event logs has been intensively researched in the past two decades. Despite a rich field of proposals, state-of-the-art automated process discovery methods suffer from two recurrent deficiencies when applied to real-life logs: (i) they produce large and spaghetti-like models; and (ii) they produce models that either poorly fit the event log (low fitness) or over-generalize it (low precision). Striking a trade-off between these quality dimensions in a robust and scalable manner has proved elusive. This paper presents an automated process discovery method, namely Split Miner, which produces simple process models with low branching complexity and consistently high and balanced fitness and precision, while achieving considerably faster execution times than state-of-the-art methods, measured on a benchmark covering twelve real-life event logs. Split Miner combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict and causal relations between neighbors in the directly-follows graph. Split Miner is also the first automated process discovery method that is guaranteed to produce deadlock-free process models with concurrency, while not being restricted to producing block-structured process models.

摘要： 在过去的二十年中，已经广泛研究了从事件日志中自动发现过程模型的问题。尽管提出了很多建议，但最先进的自动过程发现方法在应用于真实日志时会遇到两个经常性的缺陷：（ii）他们产生的模型要么不太适合事件日志（适应性低），要么过于概括了事件日志（降低了精度）。事实证明，以健壮和可扩展的方式在这些质量维度之间进行折衷是难以实现的。本文提出了一种自动过程发现方法，即Split Miner，它可以生成简单的过程模型，具有较低的分支复杂度，并且始终具有较高且平衡的适用性和精度，同时与在现有技术中测量的最新方法相比，可以实现更快的执行时间。涵盖十二个现实事件日志的基准。 Split Miner结合了一种新颖的方法来过滤由事件日志引起的直接关注图，以及一种用于识别拆分网关组合的方法，该组合可准确捕获直接关注图中邻居之间的并发，冲突和因果关系。 Split Miner还是第一种自动过程发现方法，可以保证并发地产生无死锁的过程模型，而不仅限于产生块结构过程模型。

下载地址 | 返回目录 | [10.1007/s10115-018-1214-x]

[113] Statistical relational learning for workflow mining (2016)

(Bellodi, Elena and Riguzzi, Fabrizio and Lamma, Evelina | Intelligent Data Analysis)

Abstract: The management of business processes can support efficiency improvements in organizations. One of the most interesting problems is the mining and representation of process models in a declarative language. Various recently proposed knowledge-based languages showed advantages over graph-based procedural notations. Moreover, rapid changes of the environment require organizations to check how compliant are new process instances with the deployed models. We present a Statistical Relational Learning approach to Workflow Mining that takes into account both flexibility and uncertainty in real environments. It performs automatic discovery of process models expressed in a probabilistic logic. It uses the existing DPML algorithm for extracting first-order logic constraints from process logs. The constraints are then translated into Markov Logic to learn their weights. Inference on the resulting Markov Logic model allows a probabilistic classification of test traces, by assigning them the probability of being compliant to the model. We applied this approach to three datasets and compared it with DPML alone, five Petri net- and EPC-based process mining algorithms and Tilde. The technique is able to better classify new execution traces, showing higher accuracy and areas under the PR/ROC curves in most cases.

摘要： 业务流程的管理可以支持组织效率的提高。最有趣的问题之一是以声明性语言进行过程模型的挖掘和表示。最近提出的各种基于知识的语言都比基于图的过程符号更具优势。此外，环境的快速变化要求组织检查新流程实例与已部署模型的符合性。我们提出了一种用于工作流挖掘的统计关系学习方法，该方法考虑了实际环境中的灵活性和不确定性。它执行以概率逻辑表示的过程模型的自动发现。它使用现有的DPML算法从流程日志中提取一阶逻辑约束。然后将约束转换为马尔可夫逻辑以了解其权重。通过对结果迹线的马尔可夫逻辑模型进行推论，可以通过对测试迹线分配符合模型的概率来对它们进行概率分类。我们将此方法应用于三个数据集，并将其与单独的DPML，五种基于Petri网和EPC的过程挖掘算法以及Tilde进行了比较。该技术能够更好地对新的执行轨迹进行分类，在大多数情况下显示出更高的准确性和PR / ROC曲线下的区域。

下载地址 | 返回目录 | [10.3233/IDA-160818]

[114] The state of the art of business process management research as published in the BPM conference: Recommendations for progressing the field (2016)

(Recker, Jan and Mendling, Jan | Business and Information Systems Engineering)

Abstract: The research field of Business Process Management (BPM) has gradually developed as a discipline situated within the computer, management and information systems sciences. Its evolution has been shaped by its own conference series, the BPM conference. Still, as with any other academic discipline, debates accrue and persist, which target the identity as well as the quality and maturity of the BPM field. In this paper, we contribute to the debate on the identity and progress of the BPM conference research community through an analysis of the BPM conference proceedings. We develop an understanding of signs of progress of research presented at this conference, where, how, and why papers in this conference have had an impact, and the most appropriate formats for disseminating influential research in this conference. Based on our findings from this analysis, we provide conclusions about the state of the conference series and develop a set of recommendations to further develop the conference community in terms of research maturity, methodological advance, quality, impact, and progression.

摘要： 业务流程管理（BPM）的研究领域已逐渐发展为位于计算机，管理和信息系统科学领域的一门学科。它的发展受到其自己的会议系列BPM会议的影响。但是，与任何其他学术学科一样，辩论仍在进行并持续存在，其针对性以及BPM领域的质量和成熟度。在本文中，我们通过对BPM会议过程的分析，为BPM会议研究界的身份和进步的辩论做出贡献。我们对本次会议上提出的研究进展的迹象，本次会议的论文在何处，如何发生以及为什么产生影响的理解以及在本次会议上散发有影响力的研究的最适当形式的理解都得到了理解。根据分析得出的结论，我们提供有关会议系列状况的结论，并根据研究的成熟度，方法论的进步，质量，影响和进展，提出一系列建议，以进一步发展会议社区。

下载地址 | 返回目录 | [10.1007/s12599-015-0411-3]

[115] The use of software product lines for business process management: A systematic literature review (2013)

(Santos Rocha | Information and Software Technology)

Abstract: Context: Business Process Management (BPM) is a potential domain in which Software Product Line (PL) can be successfully applied. Including the support of Service-oriented Architecture (SOA), BPM and PL may help companies achieve strategic alignment between business and IT. Objective Presenting the results of a study undertaken to seek and assess PL approaches for BPM through a Systematic Literature Review (SLR). Moreover, identifying the existence of dynamic PL approaches for BPM. Method A SLR was conducted with four research questions formulated to evaluate PL approaches for BPM. Results 63 papers were selected as primary studies according to the criteria established. From these primary studies, only 15 papers address the specific dynamic aspects in the context evaluated. Moreover, it was found that PLs only partially address the BPM lifecycle since the last business process phase is not a current concern on the found approaches. Conclusions The found PL approaches for BPM only cover partially the BPM lifecycle, not taking into account the last phase which restarts the lifecycle. Moreover, no wide dynamic PL proposal was found for BPM, but only the treatment of specific dynamic aspects. The results indicate that PL approaches for BPM are still at an early stage and gaining maturity. textcopyright 2013 Elsevier B.V. All rights reserved.

摘要： 上下文：业务流程管理（BPM）是可以成功应用软件产品线（PL）的潜在领域。 BPM和PL包括面向服务的体系结构（SOA）的支持，可以帮助公司实现业务与IT之间的战略一致性。目的介绍通过系统文献综述（SLR）寻求和评估BPM的PL方法的研究结果。此外，确定用于BPM的动态PL方法的存在。方法采用四个研究问题进行SLR，以评估BPM的PL方法。结果按照既定标准，选择63篇论文作为基础研究。从这些基础研究中，只有15篇论文针对所评估的环境中的具体动态方面。而且，发现由于最后的业务流程阶段并不是所找到方法的当前关注点，PL仅部分解决了BPM生命周期。结论发现的BPM PL方法仅部分覆盖BPM生命周期，而没有考虑重新启动生命周期的最后阶段。而且，没有发现针对BPM的广泛的动态PL提议，而只是针对特定动态方面的处理。结果表明，针对BPM的PL方法仍处于早期阶段，并且已经成熟。 textcopyright 2013 Elsevier B.V.保留所有权利。

下载地址 | 返回目录 | [10.1016/j.infsof.2013.02.007]

[116] User-guided discovery of declarative process models (2011)

(Maggi, Fabrizio M. and Mooij, Arjan J. and Van Der Aalst | IEEE SSCI 2011: Symposium Series on Computational Intelligence - CIDM 2011: 2011 IEEE Symposium on Computational Intelligence and Data Mining)

Abstract: Process mining techniques can be used to effectively discover process models from logs with example behaviour. Cross-correlating a discovered model with information in the log can be used to improve the underlying process. However, existing process discovery techniques have two important drawbacks. The produced models tend to be large and complex, especially in flexible environments where process executions involve multiple alternatives. This overload of information is caused by the fact that traditional discovery techniques construct procedural models explicitly showing all possible behaviours. Moreover, existing techniques offer limited possibilities to guide the mining process towards specific properties of interest. These problems can be solved by discovering declarative models. Using a declarative model, the discovered process behaviour is described as a (compact) set of rules. Moreover, the discovery of such models can easily be guided in terms of rule templates. This paper uses DECLARE, a declarative language that provides more flexibility than conventional procedural notations such as BPMN, Petri nets, UML ADs, EPCs and BPEL. We present an approach to automatically discover DECLARE models. This has been implemented in the process mining tool ProM. Our approach and toolset have been applied to a case study provided by the company Thales in the domain of maritime safety and security. textcopyright 2011 IEEE.

摘要： 过程挖掘技术可用于从具有示例行为的日志中有效发现过程模型。将发现的模型与日志中的信息进行互相关可用于改善基础过程。但是，现有的过程发现技术有两个重要的缺点。生成的模型往往很大且很复杂，尤其是在流程执行涉及多种选择的灵活环境中。信息的这种超载是由以下事实引起的：传统的发现技术构造了明确显示所有可能行为的程序模型。而且，现有技术提供了将采矿过程引向感兴趣的特定特性的有限可能性。这些问题可以通过发现声明模型来解决。使用声明性模型，将发现的过程行为描述为（紧凑）规则集。此外，可以根据规则模板轻松地指导此类模型的发现。本文使用DECLARE，这是一种声明性语言，比诸如BPMN，Petri网，UML AD，EPC和BPEL之类的常规过程符号提供了更大的灵活性。我们提出了一种自动发现DECLARE模型的方法。这已在过程挖掘工具ProM中实现。我们的方法和工具集已应用于Thales公司在海上安全和保障领域提供的案例研究。 textcopyright 2011 IEEE。

下载地址 | 返回目录 | [10.1109/CIDM.2011.5949297]

[117] Using process mining for the analysis of an e-trade system: a case study (2014)

(Mitsyuk, Alexey and Kalenkova, Anna and Shershakov, Sergey | Бизнес-Информатика)

Abstract: E-trade systems are widely used to automate sales processes. Inefficiencies and bottlenecks in the sales processes lead to business losses. Conventional approaches to identifying problems require much time and result in subjective conclusions. This paper proposes an approach for the analysis of e-trade system processes based on the application of process mining techniques. Process mining aims to discover, analyze, repair and improve real business processes on the basis of behavior of an information system recorded in an event log. Using process mining techniques, we have analyzed process running in an online ticket booking information system. This work has shown that process mining can give insight into the e-trade processes and can produce information for their improvement. The case study carried out allows formulating appropriate recommendations. The article also presents the real outcome of using process mining techniques. We have generalized the applied approach and showed how it could be used to the investigation of a wide spectrum of e-trade information systems. During the case study we mostly used a software framework named ProM, which includes a substantial number of plug- ins implementing process mining methods. Using software for automatic process analysis and discovery, one should be careful with the interpretation of particular methods output. Pitfalls and difficulties of applying process mining techniques to the logs of e-trade systems have also been shown. Key

摘要： 电子贸易系统被广泛用于使销售过程自动化。销售过程中的效率低下和瓶颈导致业务损失。传统的识别问题的方法需要花费大量时间并得出主观结论。本文提出了一种分析e的方法基于流程挖掘技术的贸易系统流程。流程挖掘旨在基于事件日志中记录的信息系统的行为来发现，分析，修复和改进实际业务流程。在线票务预订信息系统中运行的流程，这项工作表明流程挖掘可以深入了解电子交易流程并可以提供信息以进行改进，案例研究可以提出适当的建议。使用过程挖掘技术的成果。我们已经概括了应用的方法，并展示了它如何可用于调查各种电子贸易信息系统。在案例研究中，我们主要使用了一个名为ProM的软件框架，其中包括大量实现过程挖掘方法的插件。使用软件进行自动过程分析和发现时，应特别注意特定方法输出的解释。还显示了将过程挖掘技术应用于电子交易系统日志的陷阱和困难。键

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[118] Workflow Mining: Which Processes can be Rediscovered (2002)

(Van Der Aalst | BETA Working Paper Series, WP 74)

Abstract: Contemporary workflow management systems are chiven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for (re)discovering workflow models. Starting point for such techniques are so-called workflow logs containing information about the workflow process as it is actually being executed. Unfortunately, it is not possible to (re)discover every workflow process. In this paper we explore the class of workflow processes which can be discovered. The theoretical results presented in this paper demonstrate that most practical workflow processes fit into this class. The tool MiMo, also presented in this paper, supports the (re)discovery of these processes.

摘要： 当代工作流程管理系统由显式流程模型创建，即，为了制定给定的工作流程，需要完全指定的工作流程设计。创建工作流程设计是一个复杂的耗时过程，通常实际的工作流程与管理人员认为的流程之间存在差异。因此，我们开发了用于（重新）发现工作流模型的技术。这种技术的起点是所谓的工作流日志，其中包含有关实际执行的工作流过程的信息。不幸的是，不可能（重新）发现每个工作流程。在本文中，我们探索了可以发现的工作流过程的类别。本文介绍的理论结果表明，大多数实用的工作流过程都适合此类。本文还介绍了MiMo工具，它支持（重新）发现这些过程。

下载地址 | 返回目录 | [10.1.1.11.9090]

[119] Workflow mining: A survey of issues and approaches (2003)

(Van der Aalst | Data and Knowledge Engineering)

Abstract: Many of todays information systems are driven by explicit process models. Workflow management systems, but also ERP, CRM, SCM, and B2B, are configured on the basis of a workflow model specifying the order in which tasks need to be executed. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. To support the design of workflows, we propose the use of workflow mining. Starting point for workflow mining is a so-called workflow log containing information about the workflow process as it is actually being executed. In this paper, we introduce the concept of workflow mining and present a common format for workflow logs. Then we discuss the most challenging problems and present some of the workflow mining approaches available today. textcopyright 2003 Elsevier B.V. All rights reserved.

摘要： 当今的许多信息系统是由显式过程模型驱动的。工作流管理系统以及ERP，CRM，SCM和B2B，都是根据工作流模型配置的，该模型指定了执行任务的顺序。创建工作流程设计是一个复杂的耗时过程，通常实际的工作流程与管理人员认为的流程之间存在差异。为了支持工作流的设计，我们建议使用工作流挖掘。工作流挖掘的起点是所谓的工作流日志，其中包含有关实际上正在执行的工作流过程的信息。在本文中，我们介绍了工作流挖掘的概念，并提出了工作流日志的通用格式。然后，我们讨论最具挑战性的问题，并提出一些当今可用的工作流挖掘方法。 textcopyright 2003 Elsevier B.V.保留所有权利。

下载地址 | 返回目录 | [10.1016/S0169-023X(03)00066-1]

[120] Workflow mining: Discovering process models from event logs (2004)

(Van Der Aalst | IEEE Transactions on Knowledge and Data Engineering)

Abstract: Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called workflow log containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we will also demonstrate that it is not possible to discover arbitrary workflow processes. In this paper, we explore a class of workflow processes that can be discovered. We show that the $alpha$ -algorithm can successfully mine any workflow represented by a so-called SWF-net.

摘要： 当代工作流程管理系统由明确的流程模型驱动，即，为了制定给定的工作流程，需要完全指定的工作流程设计。创建工作流程设计是一个复杂的耗时过程，通常，实际的工作流程过程与管理层认为的过程之间存在差异。因此，我们开发了用于发现工作流模型的技术。这种技术的起点是所谓的工作流日志，其中包含有关实际上正在执行的工作流过程的信息。我们提出了一种新算法，可以从这种日志中提取过程模型，并用Petri网表示它。但是，我们还将证明不可能发现任意工作流程。在本文中，我们探索了可以发现的一类工作流程。我们证明了$ alpha $-算法可以成功地挖掘由所谓的SWF网络代表的任何工作流程。

下载地址 | 返回目录 | [10.1109/TKDE.2004.47]

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