噪音&低频&在线类论文

摘要

[1] A procedural approach for evaluating the performance of business processes based on a model of quantative and qualitative measurements (2017)

(Mendes, Thiago and Santos, Simone | )

Abstract: Evaluating the performance of processes is of vital importance if organizations are to seek continuous improvements. It is by measuring processes that data on their performance is provided, thus showing the evolution of the organization in terms of its strategic objectives. These results will serve as the basis for making better decisions, thereby leading to continuous improvement. The approach set out in this paper is prompted by the relative lack of empirical investigations into performance measures contained in the literature and the difficulties that organizations face when trying to verify the results of their business processes. Based on analyzing studies selected in a Systematic Review of the Literature, there it was found the need to propose a new approach to evaluating business processes that brings together elements and recommendations selected from the analyzed approaches. In the evaluation of the proposed approach, a case study is discussed, to verify its applicability.

摘要： 如果组织要寻求持续的改进，则评估流程的绩效至关重要。通过测量过程，可以提供有关其绩效的数据，从而根据组织的战略目标显示组织的发展。这些结果将作为做出更好决策的基础，从而导致持续改进。本文中提出的方法是由于相对缺乏对文献中包含的绩效指标的实证研究以及组织在尝试验证其业务流程结果时面临的困难。在对《系统性文献综述》中选择的研究进行分析的基础上，发现有必要提出一种新的评估业务流程的方法，该方法将从分析的方法中选择的要素和建议汇总在一起。在对提议的方法进行评估时，将讨论一个案例研究，以验证其适用性。

下载地址 | 返回目录 | [10.1007/978-3-319-62386-3_23]

[2] 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]

[3] An Innovative Online Process Mining Framework for Supporting Incremental GDPR Compliance of Business Processes (2019)

(Zaman, Rashid and Cuzzocrea, Alfredo and Hassani, Marwan | )

Abstract: GDPR (General Data Protection Regulation) is a new regulation of the European Union that superimposes strict privacy constraints on storing, accessing and processing user data, as a way to ensure that personal user data are not violated neither disclosed without an explicit consent. As a consequence, business processes that interact with large amounts of such data may easily cause GDPR violations, due to the typical complexity of such processes. Inspired by these considerations, this paper highlights the challenges and critical aspects associated with the GDPR compliance journey when opting for naive straight-forward solutions. We propose a business-aware GDPR compliance journey using online process mining. Using several large log files generated based on a real scenario, we show that the proposed tool is both effective and efficient. As such, it proves to be a powerful concept for usage in incremental GDPR compliance environments.

摘要： GDPR（通用数据保护法规）是欧盟的一项新法规，该法规在存储，访问和处理用户数据时加入了严格的隐私限制，以确保不违反个人用户数据且未经明确同意也不会泄露个人数据。结果，由于此类过程的典型复杂性，与大量此类数据进行交互的业务流程可能会轻易导致违反GDPR。受这些考虑的启发，本文重点介绍了在选择简单的解决方案时与GDPR合规性之旅相关的挑战和关键方面。我们提出了使用在线流程挖掘的业务感知型GDPR合规性之旅。通过使用基于实际场景生成的多个大型日志文件，我们证明了该工具既有效又高效，因此，它被证明是用于增量GDPR合规环境的强大概念。

下载地址 | 返回目录 | [10.1109/BigData47090.2019.9005705]

[4] Business Process Deviance Mining (2018)

(Folino, Francesco and Pontieri, Luigi | )

Abstract: Business process anomaly detection; Business process deviation mining; Business process vari- ants analysis Definition

摘要： 业务流程异常检测；业务流程偏差挖掘；业务流程变体分析定义

下载地址 | 返回目录 | [10.1007/978-3-319-63962-8_100-1]

[5] Efficient pattern matching over event streams (2008)

(Agrawal, Jagrati and Diao, Yanlei and Gyllstrom, Daniel and Immerman, Neil | )

Abstract: Pattern matching over event streams is increasingly being employed in many areas including financial services, RFID-based inventory management, click stream analysis, and electronic health systems. While regular expression matching is well studied, pattern matching over streams presents two new challenges: Languages for pattern matching over streams are significantly richer than languages for regular expression matching. Furthermore, efficient evaluation of these pattern queries over streams requires new algorithms and optimizations: the conventional wisdom for stream query processing (i.e., using selection-join-aggregation) is inadequate. In this paper, we present a formal evaluation model that offers precise semantics for this new class of queries and a query evaluation framework permitting optimizations in a principled way. Wre further analyze the runtime complexity of query evaluation using this model and develop a suite of techniques that improve runtime efficiency by exploiting sharing in storage and processing. Our experimental results provide insights into the various factors on runtime performance and demonstrate the significant performance gains of our sharing techniques. textcopyright Copyright 2008 ACM.

摘要： 事件流的模式匹配在包括金融服务，基于RFID的库存管理，点击流分析和电子医疗系统在内的许多领域中越来越多地被采用。尽管对正则表达式匹配进行了很好的研究，但流上的模式匹配提出了两个新的挑战：流上的模式匹配语言比正则表达式匹配的语言丰富得多。此外，在流上对这些模式查询的有效评估需要新的算法和优化：用于流查询处理（即，使用选择连接聚合）的常规知识是不够的。在本文中，我们提供了一个正式的评估模型，该模型为此类新查询提供了精确的语义，并提供了一种允许以有原则的方式进行优化的查询评估框架。 Wre使用此模型进一步分析了查询评估的运行时复杂性，并开发了一套通过利用存储和处理中的共享来提高运行时效率的技术。我们的实验结果提供了有关运行时性能各种因素的见解，并证明了我们共享技术的显着性能提升。 textcopyright版权所有2008 ACM。

下载地址 | 返回目录 | [10.1145/1376616.1376634]

[6] Event stream processing with out-of-order data arrival (2007)

(Li, Ming and Liu, Mo and Ding, Luping and Rundensteiner, Elke A. and Mani, Murali | )

Abstract: Complex event processing has become increasingly important in modern applications, ranging from supply chain management for RFID tracking to real-time intrusion detection. The goal is to extract patterns from such event streams in order to make informed decisions in real-time. However, networking latencies and even machine failure may cause events to arrive out-of-order at the event stream processing engine. In this work, we address the problem of processing event pattern queries specified over event streams that may contain out-of-order data. First, we analyze the problems state-of-the-art event stream processing technology would experience when faced with out-of-order data arrival. We then propose a new solution of physical implementation strategies for the core stream algebra operators such as sequence scan and pattern construction, including stack-based data structures and associated purge algorithms. Optimizations for sequence scan and construction as well as state purging to minimize CPU cost and memory consumption are also introduced. Lastly, we conduct an experimental study demonstrating the effectiveness of our approach. textcopyright 2007 IEEE.

摘要： 复杂事件处理在现代应用中变得越来越重要，从RFID跟踪的供应链管理到实时入侵检测。目的是从此类事件流中提取模式，以便实时做出明智的决策。但是，网络等待时间甚至机器故障都可能导致事件在事件流处理引擎处无序到达。在这项工作中，我们解决了处理可能包含乱序数据的事件流上指定的事件模式查询的问题。首先，我们分析了最新的事件流处理技术在遇到乱序数据到达时将遇到的问题。然后，我们为核心流代数运算符（例如序列扫描和模式构造）提出了一种物理实施策略的新解决方案，包括基于堆栈的数据结构和相关的清除算法。还介绍了序列扫描和构建的优化以及状态清除，以最大程度地降低CPU成本和内存消耗。最后，我们进行了一项实验研究，证明了我们方法的有效性。 textcopyright 2007 IEEE。

下载地址 | 返回目录 | [10.1109/ICDCSW.2007.35]

[7] High-performance complex event processing over streams (2006)

(Wu, Eugene and Diao, Yanlei and Rizvi, Shariq | )

Abstract: In this paper, we present the design, implementation, and evaluation of a system that executes complex event queries over real-time streams of RFID readings encoded as events. These complex event queries filter and correlate events to match specific patterns, and transform the relevant events into new composite events for the use of external monitoring applications. Stream-based execution of these queries enables time-critical actions to be taken in environments such as supply chain management, surveillance and facility management, healthcare, etc. We first propose a complex event language that significantly extends existing event languages to meet the needs of a range of RFID-enabled monitoring applications. We then describe a query plan-based approach to efficiently implementing this language. Our approach uses native operators to efficiently handle query-defined sequences, which are a key component of complex event processing, and pipeline such sequences to subsequent operators that are built by leveraging relational techniques. We also develop a large suite of optimization techniques to address challenges such as large sliding windows and intermediate result sizes. We demonstrate the effectiveness of our approach through a detailed performance analysis of our prototype implementation under a range of data and query workloads as well as through a comparison to a state-of-the-art stream processor. Copyright 2006 ACM.

摘要： 在本文中，我们介绍了一种系统的设计，实现和评估，该系统可以对编码为事件的RFID读数的实时流执行复杂的事件查询。这些复杂的事件查询可以过滤和关联事件以匹配特定的模式，并将相关事件转换为新的复合事件以供外部监视应用程序使用。这些查询的基于流的执行使时间紧迫的动作能够在诸如供应链管理，监视和设施管理，医疗保健等环境中采取。一系列启用RFID的监视应用程序。然后，我们描述一种基于查询计划的方法来有效地实现这种语言。我们的方法使用本机运算符来有效处理查询定义的序列（这是复杂事件处理的关键组成部分），并将此类序列通过管道传递给利用关系技术构建的后续运算符。我们还开发了一大套优化技术来应对挑战，例如较大的滑动窗口和中等大小的结果。我们通过对一系列数据和查询工作负载下的原型实现进行详细的性能分析，并与最新的流处理器进行比较，从而证明了该方法的有效性。版权所有2006 ACM。

下载地址 | 返回目录 | [10.1145/1142473.1142520]

[8] 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]

[9] On the Move to Meaningful Internet Systems: OTM 2013 Conferences (2013)

(Scekic, Ognjen and Dorn, Christoph and Dustdar, Schahram | )

Abstract: Conventional incentive mechanisms were designed for business environments involving static business processes and a limited number of actors. They are not easily applicable to crowdsourcing and other social computing platforms, characterized by dynamic collaboration patterns and high numbers of actors, because the effects of incentives in these environments are often unforeseen and more costly than in a well-controlled environment of a traditional company. In this paper we investigate how to design and calibrate incentive schemes for crowdsourcing processes by simulating joint effects of a combination of different participation and incentive mechanisms applied to a working crowd. More specifically, we present a simulation model of incentive schemes and evaluate it on a relevant real-world scenario. We show how the model is used to simulate different compositions of incentive mechanisms and model parameters, and how these choices influence the costs on the system provider side and the number of malicious workers. textcopyright 2013 Springer-Verlag.

摘要： 常规激励机制是为涉及静态业务流程和有限参与者的业务环境设计的。它们不容易适用于以动态协作模式和大量参与者为特征的众包和其他社交计算平台，因为与传统公司的良好控制环境相比，这些环境中的激励作用通常是无法预见的且成本更高。在本文中，我们通过模拟应用于工作人群的不同参与和激励机制相结合的联合效应，研究了如何设计和校准用于众包流程的激励机制。更具体地说，我们提出了激励计划的仿真模型，并在相关的实际场景中对其进行了评估。我们将展示该模型如何用于模拟激励机制和模型参数的不同组成，以及这些选择如何影响系统提供商方面的成本和恶意工作人员的数量。 textcopyright 2013 Springer-Verlag。

下载地址 | 返回目录 | [10.1007/978-3-642-41030-7]

[10] Overlapping analytic stages in online process mining (2019)

(Tavares, Gabriel Marques and Ceravolo, Paolo and Da Costa | )

Abstract: Process mining uses business event logs to understand the flow of activities, to identify anomalous cases and to enhance processes. Today, real-time process mining tools mainly deal with a single task at a time (process discovery, conformance checking, process enhancement or concept change detection). In this paper, we introduce an underlined layer overlapping with multiple online process mining tasks to smooth their integration. Following a case clustering approach, based on trace and time analysis, our proposal supports simultaneously?: process discovery, conformance checking, and concept drift detection. We evaluated our approach and compared it with other techniques using both real-life and synthetic data, obtaining promising results.

摘要： 流程挖掘使用业务事件日志来了解活动流程，识别异常情况并增强流程。如今，实时流程挖掘工具主要一次处理一个任务（流程发现，一致性检查，流程增强或概念更改检测）。在本文中，我们介绍了一个带有下划线的图层，该图层与多个在线流程挖掘任务重叠以平滑它们的集成。根据案例聚类方法，基于跟踪和时间分析，我们的建议同时支持以下内容：流程发现，一致性检查和概念漂移检测。我们评估了我们的方法，并将其与使用现实和综合数据的其他技术进行比较，获得了可喜的结果。

下载地址 | 返回目录 | [10.1109/SCC.2019.00037]

[11] Quality-driven processing of sliding window aggregates over out-of-order data streams (2015)

(Ji, Yuanzhen and Zhou, Hongjin and Jerzak, Zbigniew and Nica, Anisoara and Hackenbroich, Gregor and Fetzer, Christof | )

Abstract: One fundamental challenge in data stream processing is to cope with the ubiquity of disorder of tuples within a stream caused by network latency, operator parallelization, merging of asynchronous streams, etc. High result accuracy and low result latency are two conflicting goals in out-of-order stream processing. Different applications may prefer different extent of trade-offs between the two goals. However, existing disorder handling solutions either try to meet one goal to the extreme by sacrificing the other, or try to meet both goals but have shortcomings including unguaranteed result accuracy or increased complexity in operator implementation and application logic. To meet different application requirements on the latency versus result accuracy trade-off in out-of-order stream processing, in this paper, we propose to make this trade-off user-configurable. Particularly, focusing on sliding window aggregates, we introduce AQ-K-slack, a buffer-based quality-driven disorder handling approach. AQ-K-slack leverages techniques from the fields of sampling-based approximate query processing and control theory. It can adjust the input buffer size dynamically to minimize the result latency, while respecting user-specified threshold on relative errors in produced query results. AQ-K-slack requires no a priori knowledge of disorder characteristics of data streams, and imposes no changes to the query operator implementation or the application logic. Experiments over real-world out-of-order data streams show that, compared to the state-of-art, AQ-K-slack can reduce the average buffer size, thus the average result latency, by at least 51% while respecting user-specified requirement on the accuracy of query results.

摘要： 数据流处理中的一个基本挑战是应对由于网络延迟，操作员并行化，异步流合并等导致的流中元组混乱的普遍性。高结果准确性和低结果延迟是两个相互矛盾的目标。顺序流处理。不同的应用程序可能希望在两个目标之间进行不同程度的权衡。但是，现有的疾病处理解决方案要么试图通过牺牲另一个目标来实现一个极端，要么试图同时实现两个目标，但存在缺点，包括无法保证的结果准确性或操作员实施和应用逻辑的复杂性增加。为了满足无序流处理中延迟和结果精度权衡的不同应用需求，本文提出使这种权衡可由用户配置。特别是，针对滑动窗口聚合，我们介绍了AQ-K-slack，这是一种基于缓冲区的质量驱动的异常处理方法。 AQ-K-slack利用了基于采样的近似查询处理和控制理论领域的技术。它可以动态调整输入缓冲区的大小，以最大程度地减少结果延迟，同时遵守用户指定的阈值，以防止产生的查询结果出现相对错误。 AQ-K松弛不需要先验知识即可了解数据流的无序特性，并且无需对查询运算符实现或应用程序逻辑进行任何更改。对现实世界中的无序数据流进行的实验表明，与现有技术相比，AQ-K-slack可以减少平均缓冲区大小，从而使平均结果延迟至少降低51％。，同时遵守用户指定的查询结果准确性要求。

下载地址 | 返回目录 | [10.1145/2675743.2771828]

[12] Sequence pattern query processing over out-of-order event streams (2009)

(Liu, Mo and Li, Ming and Golovnya, Denis and Rundensteiner, Elke A. and Claypool, Kajal | )

Abstract: Complex event processing has become increasingly important in modern applications, ranging from RFID tracking for supply chain management to real-time intrusion detection. A key aspect of complex event processing is to extract patterns from event streams to make informed decisions in real-time. However, network latencies and machine failures may cause events to arrive out-of-order at the event processing engine. State-of-the-art event stream processing technology experiences significant challenges when faced with out-of-order data arrival including output blocking, huge system latencies, memory resource overflow, and incorrect result generation. To address these problems, we propose two alternate solutions: aggressive and conservative strategies respectively to process sequence pattern queries on out-of-order event streams. The aggressive strategy produces maximal output under the optimistic assumption that out-of-order event arrival is rare. In contrast, to tackle the unexpected occurrence of an out-of-order event and with it any premature erroneous result generation, appropriate error compensation methods are designed for the aggressive strategy. The conservative method works under the assumption that outof-order data may be common, and thus produces output only when its correctness can be guaranteed. A partial order guarantee (POG) model is proposed under which such correctness can be guaranteed. For robustness under spiky workloads, both strategies are supplemented with persistent storage support and customized access policies. Our experimental study evaluates the robustness of each method, and compares their respective scope of applicability with state-of-art methods. textcopyright 2009 IEEE.

摘要： 复杂事件处理在现代应用中变得越来越重要，从用于供应链管理的RFID跟踪到实时入侵检测。复杂事件处理的关键方面是从事件流中提取模式以实时做出明智的决策。但是，网络等待时间和计算机故障可能会导致事件在事件处理引擎处无序到达。当遇到乱序的数据到达时，最新的事件流处理技术将面临巨大的挑战，包括输出阻塞，巨大的系统等待时间，内存资源溢出和错误的结果生成。为了解决这些问题，我们提出了两种替代解决方案：积极策略和保守策略分别用于处理乱序事件流上的序列模式查询。在乐观的假设（乱序事件很少发生）的情况下，积极的策略会产生最大的输出。相反，为了解决意外事件的意外发生以及随之而来的任何过早错误结果的产生，针对激进策略设计了适当的错误补偿方法。保守方法在无序数据可能是常见的假设下工作，因此仅在可以保证其正确性的情况下才产生输出。提出了一种可以保证这种正确性的部分订单保证（POG）模型。为了在棘手的工作负载下保持鲁棒性，这两种策略都补充有持久性存储支持和自定义访问策略。我们的实验研究评估了每种方法的鲁棒性，并将它们各自的适用范围与最新方法进行了比较。 textcopyright 2009 IEEE。

下载地址 | 返回目录 | [10.1109/ICDE.2009.95]

[13] Understanding the Need for New Perspectives on BPM in the Digital Age: An Empirical Analysis (2019)

(Imgrund, Florian and Janiesch, Christian | )

Abstract: The emergence of digital technology is substantially changing the way we communicate and collaborate. In recent years, groundbreaking business model innovations have disrupted industries by the dozen, shifting previously unchallenged global players out of the market within shortest time. Although business process management (BPM) is often identified as a main driver for organizational efficiency in this context, there is little understanding of how its methods and tools can successfully navigate organizations through the uncertainty brought by todays highly dynamic market environments. However, we see more and more contributions emerging that question the timeliness of BPM due to its lack of context sensitivity. In this context, the inflexibility and over-functionalization of hierarchical management structures is often referred to as the primary reason why organizations fail to achieve the flexibility, agility, and responsiveness needed to address todays entrepreneurial challenges. In this research paper, we question whether the contemporary BPM body of knowledge is still sufficient to equip organizations with the competitive advantages and operational excellence that have long yielded sustainable growth and business success. In fact, our empirical observations indicate that the vertical management of functional units inherent to current BPM is increasingly being replaced by adaptive and context-sensitive management approaches drawing on agile methodologies and modular process improvements. From a total of 17 interviews, we derive five criteria that the respondents consider as essential to strengthen the position of BPM in the digital age.

摘要： 数字技术的出现极大地改变了我们沟通和协作的方式。近年来，突破性的商业模式创新已经破坏了数十个行业，使以前毫无挑战的全球参与者在最短的时间内退出了市场。尽管业务流程管理（在这种情况下，BPM）通常被认为是组织效率的主要驱动力，人们对其方法和工具如何通过当今高度动态的市场环境带来的不确定性如何成功地引导组织的了解很少，但是，我们看到越来越多的贡献正在涌现。该问题由于缺乏上下文敏感性而对BPM的及时性提出了质疑，在这种情况下，层次管理结构的不灵活性和过度功能化通常被称为组织无法实现所需的灵活性，敏捷性和响应能力的主要原因。应对当今的企业挑战。在这篇研究论文中，我们质疑当代的BPM知识体系是否仍然足以使组织具有长期产生可持续增长和业务成功的竞争优势和卓越运营。实际上，我们的经验观察表明，当前BPM固有的功能单元的垂直管理正越来越多地被基于敏捷方法和模块化流程改进的适应性和上下文相关的管理方法所取代。从总共17次访谈中，我们得出了五项标准，被调查者认为这对于加强BPM在数字时代的地位至关重要。

下载地址 | 返回目录 | [10.1007/978-3-030-37453-2_24]

[14] A multi-view multi-dimensional ensemble learning approach to mining business process deviances (2016)

(Cuzzocrea, Alfredo and Folino, Francesco and Guarascio, Massimo and Pontieri, Luigi | Proceedings of the International Joint Conference on Neural Networks)

Abstract: The execution logs of a business process have been recently exploited to extract classification models for discriminating deviant instances of the process - i.e. instances diverging from normal/desired outcomes (e.g., frauds, faults, SLA violations). Regarding all log traces as sequences of task labels, current solutions essentially map each trace onto a vector space where the features correspond to sequence-oriented patterns, and any standard classifier-induction method can be applied to separate the two classes of instances. An ensemble-learning approach was also recently proposed to combine multiple base learners trained on heterogenous pattern-based log views. However, as these approaches simply abstract each event into an activity symbol, they disregard all the non structural event data that are typically stored in real-life logs, and which may well help improve the detection of deviances. Moreover, the usefulness of deviance models could be enhanced by equipping each prediction with a confidence measure, allowing the analyst to focus on (or prioritize) more suspicious cases. To overcome these limitations, we propose a multi-view ensemble learning approach, which: (i) fully exploits the multi-dimensional nature of log events, with the help of a clustering-based trace abstraction method; and (ii) implements a context- and probability-aware stacking method for combining base models predictions. Tests on a real-life log confirmed the validity of the approach, and its capability to achieve compelling performances w.r.t. state-of-the-art methods.

摘要： 最近已利用业务流程的执行日志来提取分类模型，以区分流程的偏差实例，即与正常/期望结果（例如欺诈，错误，SLA违规）背离的实例。关于所有日志跟踪作为任务标签的序列，当前的解决方案实质上将每条迹线映射到向量空间，在这些空间中，这些特征对应于面向序列的模式，并且可以使用任何标准的分类器归纳方法来分离两类实例。最近，他们还提出了将训练有素的多个基础学习者结合在一起的方法，但是这些方法只是将每个事件抽象为一个活动符号，因此他们忽略了通常存储在现实日志中的所有非结构性事件数据，并且这可能会有助于改善对偏差的检测。此外，可以通过为每个预测变量配备偏差模型来提高偏差模型的实用性。采取信心措施，让分析师专注于（或优先处理）更多可疑案件。为了克服这些限制，我们提出了一种多视图集成学习方法，该方法：（i）在基于聚类的跟踪抽象方法的帮助下，充分利用日志事件的多维性质；（ii）实现上下文和概率感知的堆叠方法，以结合基本模型的预测。对真实日志的测试证实了该方法的有效性，以及通过引人注目的性能达到令人信服的性能的能力。最先进的方法。

下载地址 | 返回目录 | [10.1109/IJCNN.2016.7727691]

[15] An anomaly detection technique for business processes based on extended dynamic Bayesian networks (2019)

(Pauwels, Stephen | Proceedings of the ACM Symposium on Applied Computing)

Abstract: Checking and analyzing various executions of different Business Processes can be a tedious task as the logs from these executions may contain lots of events, each with a (possibly large) number of attributes. We developed a way to automatically model the behavior captured in log files with dozens of attributes. The advantage of our method is that we do not need any prior knowledge about the data and the attributes. The learned model can then be used to detect anomalous executions in the data. To achieve this we extend the existing Dynamic Bayesian Networks with other (existing) techniques to better model the normal behavior found in log files. We introduce a new algorithm that is able to learn a model of a log file starting from the data itself. The model is capable of scoring events and cases, even when new values or new combinations of values appear in the log file, and has the ability to give a decomposition of the given score, indicating the root cause for the anomalies. Furthermore we show that our model can be used in a more general way for detecting Concept Drift.

摘要： 检查和分析不同业务流程的各种执行可能是一项繁琐的任务，因为来自这些执行的日志可能包含许多事件，每个事件都有（可能很大）数量的属性。我们开发了一种自动建模具有数十个属性的日志文件中捕获的行为的方法。该方法的优点是我们不需要有关数据和属性的任何先验知识。然后，可以将学习到的模型用于检测数据中的异常执行。为实现此目的，我们使用其他（现有）技术扩展了现有的动态贝叶斯网络，以更好地对日志文件中的正常行为进行建模。我们引入了一种新算法，该算法能够从数据本身开始学习日志文件的模型。即使在日志文件中出现新值或值的新组合时，该模型也能够对事件和案例进行评分，并且能够对给定的分数进行分解，从而指出异常的根本原因。此外，我们证明了我们的模型可以以更通用的方式用于检测概念漂移。

下载地址 | 返回目录 | [10.1145/3297280.3297326]

[16] An anti-noise process mining algorithm based on minimum spanning tree clustering (2018)

(Li, Weimin and Zhu, Heng and Liu, Wei and Chen, Dehua and Jiang, Jiulei and Jin, Qun | IEEE Access)

Abstract: Many human-centric systems have begun to use business process management technology in production. With the operation of business process management systems, more and more business process logs and human-centric data have been accumulated. However, the effective utilization and analysis of these event logs are challenges that people need to solve urgently. Process mining technology is a branch of business process management technology. It can extract process knowledge from event logs and build process models, which helps to detect and improve business processes. The current process mining algorithms are inadequate in dealing with log noise. The family of alpha-algorithms ignores the impact of noise, which is unrealistic in real-life logs. Most of the process mining algorithms that can handle noise also lack reasonable denoising thresholds. In this paper, a new assumption on noise is given. Furthermore, an anti-noise process mining algorithm that can deal with noise is proposed. The decision rules of the selective, parallel, and non-free choice structures are also given. The proposed algorithm framework discovers the process model and transforms it into a Petri network representation. We calculate the distance between traces to build the minimum spanning tree on which clusters are generated. The traces of the non-largest clusters are treated as noise, and the largest cluster is mined. Finally, the algorithm can discover the regular routing structure and solve the problem of noise. The experimental results show the correctness of the algorithm when compared with the $alpha$ ++ algorithm.

摘要： 许多以人为中心的系统已开始在生产中使用业务流程管理技术。随着业务流程管理系统的运行，已经积累了越来越多的业务流程日志和以人为中心的数据。但是，有效利用和分析这些事件日志是人们迫切需要解决的挑战。流程挖掘技术是业务流程管理技术的一个分支。它可以从事件日志中提取流程知识并建立流程模型，从而有助于检测和改进业务流程。当前的过程挖掘算法不足以处理对数噪声。 alpha算法系列忽略了噪声的影响，这在现实生活中是不现实的。大多数可以处理噪声的过程挖掘算法也缺乏合理的降噪阈值。在本文中，给出了关于噪声的新假设。此外，提出了一种可以处理噪声的抗噪过程挖掘算法。还给出了选择，并行和非自由选择结构的决策规则。所提出的算法框架发现了过程模型，并将其转换为Petri网络表示。我们计算迹线之间的距离，以构建生成簇的最小生成树。将非最大簇的轨迹视为噪声，并挖掘最大簇。最后，该算法可以发现规则的路由结构并解决噪声问题。实验结果表明，与$ alpha $ ++算法相比，该算法是正确的。

下载地址 | 返回目录 | [10.1109/ACCESS.2018.2865540]

[17] 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]

[18] 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]

[19] 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]

[20] 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]

[21] 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]

[22] 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)

[下载地址](http://arxiv.org/abs/1704.08101 http://dx.doi.org/10.1007/s10115-017-1060-2) | 返回目录 | [10.1007/s10115-017-1060-2]

[23] 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)

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

[24] From event logs to goals: a systematic literature review of goal-oriented process mining (2020)

(Ghasemi, Mahdi and Amyot, Daniel | Requirements Engineering)

Abstract: Process mining helps infer valuable insights about business processes using event logs, whereas goal modeling focuses on the representation and analysis of competing goals of stakeholders and systems. Although there are clear benefits in mining the goals of existing processes, goal-oriented approaches that consider logs during model construction are still rare. Process mining techniques, when generalizing large instance-level data into process models, can be considered as a data-driven complement to use case/scenario elicitation. Requirements engineers can exploit process mining techniques to find new system or process requirements in order to align current practices and desired ones. This paper provides a systemic literature review, based on 24 papers rigorously selected from four popular search engines in 2018, to assess the state of goal-oriented process mining. Through two research questions, the review highlights that the use of process mining in association with goals does not yet have a coherent line of research, whereas intention mining (where goal models are mined) shows a meaningful trace of research. Research about performance indicators measuring goals associated with process mining is also sparse. Although the number of publications in process mining and goal modeling is trending up, goal mining and goal-oriented process mining remain modest research areas. Yet, synergetic effects achievable by combining goals and process mining can potentially augment the precision, rationality and interpretability of mined models and eventually improve opportunities to satisfy system stakeholders.

摘要： 流程挖掘可使用事件日志帮助推断有关业务流程的宝贵见解，而目标建模则着重于利益相关者和系统相互竞争的目标的表示和分析。尽管挖掘现有流程的目标有明显的好处，但是在模型构建过程中考虑日志的面向目标的方法仍然很少。当将大型实例级数据概括为流程模型时，流程挖掘技术可以视为对用例/场景启发的数据驱动补充。需求工程师可以利用过程挖掘技术来查找新的系统或过程需求，以使当前的实践与所需的实践保持一致。本文基于2018年从四个热门搜索引擎中严格选择的24篇论文，提供了系统的文献综述，以评估面向目标的过程挖掘的状态。通过两个研究问题，该评论强调指出，与目标关联的过程挖掘的使用尚不具有连贯的研究思路，而意图挖掘（在其中挖掘了目标模型）显示了有意义的研究痕迹。关于衡量与过程挖掘相关的目标的性能指标的研究也很少。尽管在过程挖掘和目标建模方面的出版物数量呈上升趋势，但目标挖掘和面向目标的过程挖掘仍处于中等研究领域。然而，通过结合目标和过程挖掘可以实现的协同效应可以潜在地提高挖掘模型的准确性，合理性和可解释性，并最终改善满足系统利益相关者的机会。

下载地址 | 返回目录 | [10.1007/s00766-018-00308-3]

[25] From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoring (2019)

(Senderovich, Arik and Francescomarino, Chiara Di and Maggi, Fabrizio Maria | Information Systems)

Abstract: Predictive process monitoring (PPM) is a research area that focuses on predicting measures of interest (e.g., the completion time) for running cases based on event logs. State-of-the-art PPM techniques only consider intra-case information that comes from the case whose measures of interest one wishes to predict. However, in many systems, the outcome of a running case depends on the interplay of all cases that are being executed concurrently, or can be derived from the characteristics of cases that are executed in the same period of time. For example, in many situations, running cases compete over scarce resources, and the completion time of a running case can be derived from the number of similar cases running concurrently. In this work, we present a general framework for feature encoding that relies on a bi-dimensional state space representation. The first dimension corresponds to intra-case dependencies and utilizes existing feature encoding techniques. The second dimension encodes inter-case features using two approaches: (1) a knowledge-driven encoding (KDE), which assumes prior knowledge on case types, and (2) a data-driven encoding (DDE), which automatically identifies case types from data using case proximity metrics. Both approaches partition the event log into sets of cases that share common characteristics, and derive features according to these commonalities. We demonstrate the usefulness of the proposed framework with an empirical evaluation carried out against two real-life datasets coming from an outpatient hospital process and a manufacturing process.

摘要： 预测性过程监控（PPM）是一个研究领域，致力于基于事件日志预测运行案例的关注度量（例如完成时间）。最新的PPM技术仅考虑案例内的信息，这些信息来自希望预测其感兴趣的度量的案例。但是，在许多系统中，正在运行的案例的结果取决于同时执行的所有案例的相互作用，或者可以从在同一时间段内执行的案例的特征得出。例如，在许多情况下，正在运行的案例会争夺稀缺的资源，而正在运行的案例的完成时间可以从同时运行的相似案例的数量得出。在这项工作中，我们提出了一种基于二维状态空间表示的特征编码的通用框架。第一维对应于案例内依赖性，并利用现有特征编码技术。第二维使用两种方法对案例之间的特征进行编码：（1）知识驱动的编码（KDE），它假定对案例类型具有先验知识；（2）数据驱动的编码（DDE），它自动识别案例类型使用案例接近度指标从数据中获取数据。两种方法都将事件日志划分为具有共同特征的案例集，并根据这些共同特征派生特征。我们通过对来自门诊医院过程和生产过程的两个实际数据集进行的经验评估，证明了所提出框架的有用性。

下载地址 | 返回目录 | [10.1016/j.is.2019.01.007]

[26] Identifying critical nodes in complex networks via graph convolutional networks (2020)

(Yu, En Yu and Wang, Yue Ping and Fu, Yan and Chen, Duan Bing and Xie, Mei | Knowledge-Based Systems)

Abstract: Critical nodes of complex networks play a crucial role in effective information spreading. There are many methods have been proposed to identify critical nodes in complex networks, ranging from centralities of nodes to diffusion-based processes. Most of them try to find what kind of structure will make the node more influential. In this paper, inspired by the concept of graph convolutional networks(GCNs), we convert the critical node identification problem in complex networks into a regression problem. Considering adjacency matrices of networks and convolutional neural networks(CNNs), a simply yet effectively method named RCNN is presented to identify critical nodes with the best spreading ability. In this approach, we can generate feature matrix for each node and use a convolutional neural network to train and predict the influence of nodes. Experimental results on nine synthetic and fifteen real networks show that under Susceptible–Infected–Recovered (SIR) model, RCNN outperforms the traditional benchmark methods on identifying critical nodes under spreading dynamic.

摘要： 复杂网络的关键节点在有效的信息传播中起着至关重要的作用。已经提出了许多方法来识别复杂网络中的关键节点，范围从节点的中心到基于扩散的过程。他们中的大多数人试图找到哪种结构将使节点更具影响力。在本文中，受图卷积网络（GCN）概念的启发，我们将复杂网络中的关键节点识别问题转换为回归问题。考虑到网络和卷积神经网络（CNN）的邻接矩阵，提出了一种简单而有效的名为RCNN的方法来识别具有最佳扩展能力的关键节点。在这种方法中，我们可以为每个节点生成特征矩阵，并使用卷积神经网络来训练和预测节点的影响。在9个合成网络和15个真实网络上的实验结果表明，在敏感-感染-恢复（SIR）模型下，RCNN在传播动态条件下识别关键节点方面优于传统基准方法。

下载地址 | 返回目录 | [10.1016/j.knosys.2020.105893]

[27] Interactive process miner: a new approach for process mining (2018)

(Yurek, Ismail and Birant, Derya and Birant, Kokten Ulas | Turkish Journal of Electrical Engineering and Computer Sciences)

Abstract: Process mining is a technique for extracting knowledge from event logs recorded by an information system. In the process discovery phase of process mining, a process model is constructed to represent the business processes systematically and to give a general opinion about the progressive of processes in the event log. The constructed process model can be very complex as a result of structured and unstructured processes recorded in real life. Previous studies proposed different approaches to filter or eliminate some processes from the model to simplify it by implementing some statistical or mathematical formulas rather than user interactions. The main objective of this study is to develop an algorithm that is capable of working on large volume of event logs and handling the execution records of running process instances to analyze the execution of processes. The other significant principle is to provide an interactive method to ensure the decisions that will be taken to improve the execution of processes by verifying in a simulation environment before being put into practice. This study proposes a novel algorithm, named interactive process miner, to create a process model based on event logs and a new approach that contains three different features, including activity deletion, aggregation, and addition operations on the existing process model. The experimental results show a fundamental improvement in performance compared to the existing algorithms. As a result of this study, users will have an opportunity to analyze a large volume of event logs in a short time with low memory usage and to modify the created process model to observe the impact of improvement changes in a simulation environment before applying any changes to a system in real life.

摘要： 过程挖掘是一种从信息系统记录的事件日志中提取知识的技术。在流程挖掘的流程发现阶段，将构建一个流程模型来系统地表示业务流程，并在事件日志中对流程的进展给出总体看法。由于现实生活中记录的结构化和非结构化过程，构造的过程模型可能非常复杂。先前的研究提出了不同的方法来通过执行一些统计或数学公式而不是用户交互来从模型中过滤或消除某些过程以简化模型。这项研究的主要目的是开发一种能够处理大量事件日志并处理正在运行的流程实例的执行记录以分析流程执行情况的算法。另一个重要的原则是提供一种交互式方法，以确保在实践之前通过在仿真环境中进行验证来确保为改进流程的执行而采取的决策。这项研究提出了一种名为交互式过程挖掘器的新颖算法，该算法可基于事件日志创建一个过程模型，并提供了一种包含三种不同功能的新方法，包括在现有过程模型上进行活动删除，聚合和添加操作。实验结果表明，与现有算法相比，性能有了根本改善。这项研究的结果是，用户将有机会在短时间内以较低的内存使用量分析大量事件日志，并修改创建的过程模型，以在应用任何更改之前观察仿真环境中改进更改的影响。进入现实生活中的系统。

下载地址 | 返回目录 | [10.3906/elk-1708-112]

[28] Mining process models with non-free-choice constructs (2007)

(Wen, Lijie and Van Der Aalst | Data Mining and Knowledge Discovery)

Abstract: Process mining aims at extracting information from event logs to capture the business process as it is being executed. Process mining is particularly useful in situations where events are recorded but there is no system enforcing people to work in a particular way. Consider for example a hospital where the diagnosis and treatment activities are recorded in the hospital information system, but where health-care professionals determine the careflow. Many process mining approaches have been proposed in recent years. However, in spite of many researchers persistent efforts, there are still several challenging problems to be solved. In this paper, we focus on mining non-free-choice constructs, i.e., situations where there is a mixture of choice and synchronization. Although most real-life processes exhibit non-free-choice behavior, existing algorithms are unable to adequately deal with such constructs. Using a Petri-net-based representation, we will show that there are two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. We propose an algorithm that is able to deal with both kinds of dependencies. The algorithm has been implemented in the ProM framework and experimental results shows that the algorithm indeed significantly improves existing process mining techniques. textcopyright 2007 Springer Science+Business Media, LLC.

摘要： 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。在记录事件但没有强制人们以特定方式工作的系统的情况下，过程挖掘特别有用。例如，考虑一家医院，其诊断和治疗活动记录在医院信息系统中，但是由医疗保健专业人员确定护理流程。近年来，已经提出了许多过程挖掘方法。然而，尽管许多研究人员做出了不懈的努力，仍然有一些有挑战性的问题需要解决。在本文中，我们专注于挖掘非自由选择的构造，即选择和同步混合在一起的情况。尽管大多数现实生活过程都表现出非自由选择行为，但是现有算法无法充分处理此类构造。使用基于Petri网的表示法，我们将显示任务之间存在两种因果关系，即显式和隐式的。我们提出了一种能够处理两种依赖性的算法。该算法已在ProM框架中实现，实验结果表明，该算法确实大大改善了现有的流程挖掘技术。 textcopyright 2007 Springer Science + Business Media，LLC。

下载地址 | 返回目录 | [10.1007/s10618-007-0065-y]

[29] Online conformance checking using behavioural patterns (2018)

(Burattin, Andrea and van Zelst, Sebastiaan J. and Armas-Cervantes, Abel and van Dongen, Boudewijn F. and Carmona, Josep | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics))

Abstract: New and compelling regulations (e.g., the GDPR in Europe) impose tremendous pressure on organizations, in order to adhere to standard procedures, processes, and practices. The field of conformance checking aims to quantify the extent to which the execution of a process, captured within recorded corresponding event data, conforms to a given reference process model. Existing techniques assume a post-mortem scenario, i.e. they detect deviations based on complete executions of the process. This limits their applicability in an online setting. In such context, we aim to detect deviations online (i.e., in-vivo), in order to provide recovery possibilities before the execution of a process instance is completed. Also, current techniques assume cases to start from the initial stage of the process, whereas this assumption is not feasible in online settings. In this paper, we present a generic framework for online conformance checking, in which the underlying process is represented in terms of behavioural patterns and no assumption on the starting point of cases is needed. We instantiate the framework on the basis of Petri nets, with an accompanying new unfolding technique. The approach is implemented in the process mining tool ProM, and evaluated by means of several experiments including a stress-test and a comparison with a similar technique.

摘要： 新的，引人注目的法规（例如欧洲的GDPR）对组织施加了巨大的压力，以遵守标准的程序，流程和惯例。一致性检查领域旨在量化在记录的相应事件数据中捕获的过程执行与给定参考过程模型一致的程度。现有技术假定事后分析场景，即它们基于过程的完整执行来检测偏差。这限制了它们在在线环境中的适用性。在这种情况下，我们旨在在线（即，体内）检测偏差，以便在流程实例的执行完成之前提供恢复可能性。同样，当前的技术假设情况从流程的初始阶段开始，而这种假设在在线环境中是不可行的。在本文中，我们提出了一个用于在线一致性检查的通用框架，该框架中的基本过程以行为模式表示，不需要对案例的起点进行假设。我们在Petri网的基础上实例化该框架，并附带一种新的展开技术。该方法在过程挖掘工具ProM中实现，并通过包括压力测试和与类似技术的比较在内的多个实验进行了评估。

下载地址 | 返回目录 | [10.1007/978-3-319-98648-7_15]

[30] Online conformance checking: relating event streams to process models using prefix-alignments (2019)

(van Zelst, Sebastiaan J. and Bolt, Alfredo and Hassani, Marwan and van Dongen, Boudewijn F. and van der Aalst, Wil M.P. | International Journal of Data Science and Analytics)

Abstract: Companies often specify the intended behaviour of their business processes in a process model. Conformance checking techniques allow us to assess to what degree such process models and corresponding process execution data correspond to one another. In recent years, alignments have proven extremely useful for calculating conformance checking statistics. Existing techniques to compute alignments have been developed to be used in an offline, a posteriori setting. However, we are often interested in observing deviations at the moment they occur, rather than days, weeks or even months later. Hence, we need techniques that enable us to perform conformance checking in an online setting. In this paper, we present a novel approach to incrementally compute prefix-alignments, paving the way for real-time online conformance checking. Our experiments show that the reuse of previously computed prefix-alignments enhances memory efficiency, whilst preserving prefix-alignment optimality. Moreover, we show that, in case of computing approximate prefix-alignments, there is a clear trade-off between memory efficiency and approximation error.

摘要： 公司通常在流程模型中指定其业务流程的预期行为。一致性检查技术使我们能够评估这种流程模型和相应的流程执行数据在多大程度上相互对应。近年来，对齐已被证明对于计算一致性检查统计数据极为有用。已经开发了用于计算路线的现有技术，以用于离线后验设置中。但是，我们经常有兴趣观察偏差发生的时间，而不是几天，几周甚至几个月。因此，我们需要使我们能够在在线设置中执行一致性检查的技术。在本文中，我们提出了一种新颖的增量计算前缀对齐方式，为实时在线一致性检查铺平了道路。我们的实验表明，先前计算的前缀对齐方式的重用可提高内存效率，同时保留前缀对齐方式的最优性。此外，我们表明，在计算近似前缀对齐的情况下，内存效率和近似误差之间存在明显的权衡。

下载地址 | 返回目录 | [10.1007/s41060-017-0078-6]

[31] Petri Nets with Parameterised Data: Modelling and Verification (Extended Version) (2020)

(Ghilardi, Silvio and Gianola, Alessandro and Montali, Marco and Rivkin, Andrey | )

Abstract: During the last decade, various approaches have been put forward to integrate business processes with different types of data. Each of such approaches reflects specific demands in the whole process-data integration spectrum. One particular important point is the capability of these approaches to flexibly accommodate processes with multiple cases that need to co-evolve. In this work, we introduce and study an extension of coloured Petri nets, called catalog-nets, providing two key features to capture this type of processes. On the one hand, net transitions are equipped with guards that simultaneously inspect the content of tokens and query facts stored in a read-only, persistent database. On the other hand, such transitions can inject data into tokens by extracting relevant values from the database or by generating genuinely fresh ones. We systematically encode catalog-nets into one of the reference frameworks for the (parameterised) verification of data and processes. We show that fresh-value injection is a particularly complex feature to handle, and discuss strategies to tame it. Finally, we discuss how catalog nets relate to well-known formalisms in this area.

摘要： 在过去的十年中，已经提出了各种方法来将业务流程与不同类型的数据集成在一起。每种方法都反映了整个过程数据集成范围内的特定需求。一个特别重要的一点是，这些方法能够灵活地适应需要共同发展的多个案例的流程。在这项工作中，我们介绍并研究了彩色Petri网的扩展，称为目录网，提供了捕获此类过程的两个关键功能。一方面，网络转换配备了防护程序，可同时检查令牌的内容和查询存储在只读持久数据库中的事实。另一方面，通过从数据库中提取相关值或生成真正新鲜的值，此类转换可以将数据注入令牌。我们将商品目录网系统地编码为参考框架之一，用于（参数化的）数据和过程验证。我们展示了新值注入是处理中特别复杂的功能，并讨论了驯服它的策略。最后，我们讨论目录网如何与该领域的知名形式主义联系起来。

下载地址 | 返回目录 | []

[32] Prediction-based resource allocation using LSTM and minimum cost and maximum flow algorithm (2019)

(Park, Gyunam and Song, Minseok | Proceedings - 2019 International Conference on Process Mining, ICPM 2019)

Abstract: Predictive business process monitoring aims at providing the predictions about running instances by analyzing logs of completed cases of a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using LSTM with online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.

摘要： 预测性业务流程监视旨在通过分析业务流程已完成案例的日志来提供有关运行实例的预测。最近，许多研究着重于通过预测业务执行过程中的潜在问题来提高业务流程的生产率和效率。但是，大多数研究都缺乏建议改进流程的具体措施。他们将其留给用户的主观判断。在本文中，我们提出了一种新颖的方法，将预测性业务流程监视的结果与实际业务流程的改进联系起来。更详细地讲，我们通过利用预测在非千篇一律的在线环境中优化了资源分配，在该环境中，调度所需的信息有限。所提出的方法集成了离线预测模型构造，该模型使用LSTM预测正在处理的实例的处理时间和下一个活动，并具有从最小成本和最大流量算法扩展来的在线资源分配。为了验证所提出的方法，我们使用了来自全球金融组织的人工事件日志和真实事件日志进行了实验。

下载地址 | 返回目录 | [10.1109/ICPM.2019.00027]

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

(Garcia, Cleiton dos Santos and Meincheim, Alex and Faria Junior | Expert Systems with Applications)

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]

中文摘要仅供参考