机器学习&过程挖掘

摘要

[1] A comparative analysis of business process model similarity measures (2017)

Abstract: To work efficiently with and unlock the potentials of business process models, measuring their similarity is a basic requirement. Thus, many automatic similarity measurement approaches have been developed during the last years, which utilize very different aspects of a model. At the same time, it is unclear which measures can be meaningfully applied in which context and how they behave in general. Hence, this paper analyzes how the values of existing similarity measures correlate and how corresponding implementations perform with respect to their resource consumption. The results of our analysis show that the similarity values of most measures highly correlate while their performance prohibits the usage of more than 50{\%} of the measures in practice.

摘要： 为了有效地利用和挖掘业务流程模型的潜力，测量它们的相似性是一项基本要求。因此，在最近几年中，已经开发了许多自动相似性测量方法，它们利用了模型的非常不同的方面。同时，目前尚不清楚哪些措施可以在哪种情况下有意义地应用，以及它们的总体表现如何。因此，本文分析了现有相似性度量的值如何相互关联以及相对应的实现方式在其资源消耗方面的表现。我们的分析结果表明，大多数度量的相似性值高度相关，而它们的性能则禁止在实践中使用超过50 {\％}的度量。

下载地址 | 返回目录 | [10.1007/978-3-319-58457-7_23]

[2] A deep learning approach for predicting process behaviour at runtime (2017)

Abstract: Predicting the final state of a running process, the remaining time to completion or the next activity of a running process are important aspects of runtime process management. Runtime management requires the ability to identify processes that are at risk of not meeting certain criteria in order to offer case managers decision information for timely intervention. This in turn requires accurate prediction models for process outcomes and for the next process event, based on runtime information available at the prediction and decision point. In this paper, we describe an initial application of deep learning with recurrent neural networks to the problem of predicting the next process event. This is both a novel method in process prediction, which has previously relied on explicit process models in the form of Hidden Markov Models (HMM) or annotated transition systems, and also a novel application for deep learning methods.

摘要： 预测正在运行的进程的最终状态，完成过程所需的剩余时间或正在运行的进程的下一个活动是运行时进程管理的重要方面。运行时管理要求具有识别可能不符合某些标准的风险的能力，以便为案例管理人员提供决策信息，以便及时进行干预。反过来，需要基于预测和决策点可用的运行时信息，为过程结果和下一过程事件提供准确的预测模型。在本文中，我们描述了具有递归神经网络的深度学习在预测下一个过程事件的问题上的初步应用。这既是过程预测中的一种新颖方法，该方法先前依赖于隐马尔可夫模型（HMM）或带注释的过渡系统形式的显式过程模型，而且是深度学习方法的一种新颖应用。

下载地址 | 返回目录 | [10.1007/978-3-319-58457-7_24]

[3] A descriptive clustering approach to the analysis of quantitative business-process deviances (2017)

Abstract: Increasing attention has been paid to the problem of explaining and analyzing “deviant cases” generated by a business process, i.e. instances of the process that diverged from prescribed/expected behavior (e.g. frauds, faults, SLA violations). In many real settings, such cases are labelled with a numerical deviance measure, and the analyst wants to obtain a fine grain unsupervised classification of them, which will help her recognize and explain different deviance scenarios. Unfortunately, current approaches rely on preliminary labelling all the cases, stored in some an execution log, as either deviant or non-deviant, and then inducing a rule-based classifier for discriminating among the two classes. By contrast, we here propose a method that discovers accurate and readable devianceaware clusters (of cases) defined in terms of descriptive rules over both properties and behavioral aspects of the cases. Each cluster is also equipped with summary information that allows to derive effective distribution charts and a high-level process map, both emphasizing the distinctive features of the cluster. Tests on a real-life log confirmed the ability of the approach to find easily-interpretable clustering models capturing relevant deviance scenarios.

摘要： 已经越来越多地注意解释和分析业务流程所产生的“异常案例”的问题，即与规定/预期行为（例如欺诈，过错，违反SLA的行为）不同的流程实例。在许多实际环境中，此类情况会用数字偏差度量标记，分析师希望对它们进行细粒度的无监督分类，这将有助于她识别和解释不同的偏差情况。不幸的是，当前的方法依赖于将所有案例的初步标记为异常或非异常，并存储在执行日志中，然后引入基于规则的分类器来区分这两个类。相比之下，我们在此提出一种方法，该方法可发现根据案例的属性和行为方面的描述规则定义的（案例）准确且可读的异常意识集群。每个集群还配备了摘要信息，这些摘要信息可用于得出有效的分布图和高级流程图，这两个信息都强调了集群的独特功能。对真实日志的测试证实了该方法能够找到捕获相关偏差场景的易于解释的聚类模型的能力。

[下载地址](https://doi.org/10.1145/3019612 http://dl.acm.org/citation.cfm?doid=3019612.3019660) | 返回目录 | [10.1145/3019612.3019660]

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

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]

[5] BINet: Multivariate business process anomaly detection using deep learning (2018)

Abstract: In this paper, we propose BINet, a neural network architecture for real-time multivariate anomaly detection in business process event logs. BINet has been designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a heuristic for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level, but also on event attribute level. We compare BINet to 6 other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 60 synthetic and 21 real life event logs using artificial anomalies. BINet reached an average F1 score over all detection levels of 0.83, whereas the next best approach, a denoising autoencoder, reached only 0.74. This F1 score is calculated over two different levels of detection, namely case and attribute level. BINet reached 0.84 on case and 0.82 on attribute level, whereas the next best approach reached 0.78 and 0.71 respectively.

摘要： 在本文中，我们提出了BINet，这是一种用于在业务流程事件日志中进行实时多元异常检测的神经网络体系结构。 BINet旨在处理业务流程的控制流和数据透视图。此外，我们提出了一种启发式方法，用于自动设置异常检测算法的阈值。我们证明了BINet不仅可以用于案例级别，还可以用于事件属性级别，以检测事件日志中的异常。我们将BINet与其他6种最先进的异常检测算法进行了比较，并使用人工异常对60个合成事件和21个现实事件日志的详尽数据集进行了评估。 BINet在所有检测级别上均达到了平均F1分数0.83，而第二好的方法是去噪自动编码器，仅达到0.74。在两个不同的检测级别（案例和属性级别）上计算此F1分数。 BINet在案例上达到0.84，在属性级别上达到0.82，而次佳的方法分别达到0.78和0.71。

下载地址 | 返回目录 | [10.1007/978-3-319-98648-7_16]

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

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。在很多情况下，事件日志中记录的事件的粒度太细，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。我们表明，当流程发现算法仅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]

[7] Exploiting event log event attributes in RNN based prediction (2020)

Abstract: In predictive process analytics, current and historical process data in event logs is used to predict the future, e.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction models. Thus far, event attributes have not been fully utilized in these models. The biggest challenge in exploiting them in prediction models is the potentially large amount of event attributes and attribute values. We present a novel clustering technique that allows for trade-offs between prediction accuracy and the time needed for model training and prediction. As an additional finding, we also find that this clustering method combined with having raw event attribute values in some cases provides even better prediction accuracy at the cost of additional time required for training and prediction.

摘要： 在预测性过程分析中，事件日志中的当前和历史过程数据用于预测未来，例如，预测下一个活动或过程将需要完成多长时间。递归神经网络（RNN）及其子类已被证明非常适合创建预测模型。到目前为止，事件属性尚未在这些模型中得到充分利用。在预测模型中利用它们的最大挑战是潜在的大量事件属性和属性值。我们提出了一种新颖的聚类技术，可以在预测精度与模型训练和预测所需的时间之间进行权衡。作为另一个发现，我们还发现，这种聚类方法在某些情况下结合使用原始事件属性值可以提供更好的预测准确性，但需要付出额外的训练和预测时间。

[下载地址](http://arxiv.org/abs/1904.06895 http://link.springer.com/10.1007/978-3-030-46633-6{\_}4) | 返回目录 | [10.1007/978-3-030-46633-6_4]

[8] Neural approach to the discovery problem in process mining (2018)

Abstract: “Process mining deals with various types of formal models. Some of them are used at intermediate stages of synthesis and analysis, whereas others are the desired goals themselves. Transition systems (TS) are widely used in both scenarios. Process discovery, which is a special case of the synthesis problem, tries to find patterns in event logs. In this paper, we propose a new approach to the discovery problem based on recurrent neural networks (RNN). Here, an event log serves as a training sample for a neural network; the algorithm extracts RNNs internal state as the desired TS that describes the behavior present in the log. Models derived by the approach contain all behaviors from the event log (i.e. are perfectly fit) and vary in simplicity and precision, the key model quality metrics. One of the main advantages of the neural method is the natural ability to detect and merge common behavioral parts that are scattered across the log. The paper studies the proposed method, its properties and possible cases where the application of this approach is sensible as compared to other methods of TS synthesis.”

摘要： “过程挖掘处理各种类型的形式模型。其中一些模型用于综合和分析的中间阶段，而另一些模型本身就是期望的目标。在这两种情况下都广泛使用过渡系统（TS）。过程发现是一种特殊的综合问题，试图在事件日志中找到模式，本文提出了一种基于递归神经网络（RNN）的发现问题的新方法，这里，事件日志可以作为事件日志的训练样本神经网络；该算法提取RNN的内部状态作为所需的TS，以描述日志中存在的行为，该方法得出的模型包含事件日志中的所有行为（即完全匹配），并且在简单性和精度方面各不相同，这是关键模型神经方法的主要优点之一是能够检测和合并散布在日志中的常见行为部分的自然能力，因此本文研究了提出的方法及其性能与其他TS合成方法相比，这种方法的应用更为合理。”

下载地址 | 返回目录 | [10.1007/978-3-030-11027-7_25]

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

[10] Predictive business process monitoring with LSTM neural networks (2017)

Abstract: Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-and-error and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods.

摘要： “预测性业务流程监视方法利用流程已完成案例的日志，以便对其运行案例进行预测。该空间中的现有方法是为特定的预测任务量身定制的。而且，它们的相对精度对手头的数据集高度敏感，因此要求用户在特定环境中应用时要反复试验和调整。本文研究了长期短期记忆（LSTM）神经网络，该方法可为各种预测性过程监控任务建立一致的准确模型。首先，我们证明了LSTM优于现有技术来预测运行案例的下一个事件及其时间戳。接下来，我们展示如何使用模型来预测下一个任务，以便预测正在运行的案例的完整延续。最后，我们使用相同的方法来预测剩余时间，并表明该方法优于现有的量身定制的方法。

[下载地址](http://link.springer.com/10.1007/978-3-319-59536-8 http://link.springer.com/10.1007/978-3-319-59536-8{\_}30) | 返回目录 | [10.1007/978-3-319-59536-8_30]

[11] A Novel Business Process Prediction Model Using a Deep Learning Method (2020)

Abstract: The ability to proactively monitor business processes is a main competitive differentiator for firms. Process execution logs generated by process aware information systems help to make process specific predictions for enabling a proactive situational awareness. The goal of the proposed approach is to predict the next process event from the completed activities of the running process instance, based on the execution log data from previously completed process instances. By predicting process events, companies can initiate timely interventions to address undesired deviations from the desired workflow. The paper proposes a multi-stage deep learning approach that formulates the next event prediction problem as a classification problem. Following a feature pre-processing stage with n-grams and feature hashing, a deep learning model consisting of an unsupervised pre-training component with stacked autoencoders and a supervised fine-tuning component is applied. Experiments on a variety of business process log datasets show that the multi-stage deep learning approach provides promising results. The study also compared the results to existing deep recurrent neural networks and conventional classification approaches. Furthermore, the paper addresses the identification of suitable hyperparameters for the proposed approach, and the handling of the imbalanced nature of business process event datasets.

摘要： 主动监控业务流程的能力是公司的主要竞争优势。由过程感知信息系统生成的过程执行日志有助于做出特定于过程的预测，以实现主动的态势感知。所提出的方法的目的是基于来自先前完成的流程实例的执行日志数据，根据正在运行的流程实例的完成的活动来预测下一个流程事件。通过预测流程事件，公司可以及时采取干预措施，以解决与所需工作流之间的不希望有的偏差。本文提出了一种多阶段深度学习方法，该方法将下一事件预测问题表述为分类问题。在具有n元语法和特征哈希的特征预处理阶段之后，应用了一个深度学习模型，该模型由无监督的预训练组件与堆叠的自动编码器和有监督的微调组件组成。对各种业务流程日志数据集的实验表明，多阶段深度学习方法提供了可喜的结果。该研究还将结果与现有的深度递归神经网络和常规分类方法进行了比较。此外，本文致力于为所提出的方法确定合适的超参数，以及处理业务流程事件数据集的不平衡性质。

下载地址 | 返回目录 | [10.1007/s12599-018-0551-3]

[12] A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs (2016)

Abstract: Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lions share of process mining research has been focusing on process discovery (creating process models from raw data) and replay techniques to check conformance and analyze bottlenecks. These techniques have helped organizations to address compliance and performance problems. However, for a more refined analysis, it is essential to correlate different process characteristics. For example, do deviations from the normative process cause additional delays and costs? Are rejected cases handled differently in the initial phases of the process? What is the influence of a doctors experience on treatment process? These and other questions may involve process characteristics related to different perspectives (control-flow, data-flow, time, organization, cost, compliance, etc.). Specific questions (e.g., predicting the remaining processing time) have been investigated before, but a generic approach was missing thus far. The proposed framework unifies a number of approaches for correlation analysis proposed in literature, proposing a general solution that can perform those analyses and many more. The approach has been implemented in ProM and combines process and data mining techniques. In this paper, we also demonstrate the applicability using a case study conducted with the UWV (Employee Insurance Agency), one of the largest “administrative factories” in The Netherlands.

摘要： 过程挖掘可以被视为基于模型的过程分析与面向数据的分析技术之间的缺失链接。 Lions在过程挖掘研究中的份额一直集中在过程发现（从原始数据创建过程模型）和重放技术来检查一致性和分析瓶颈。这些技术已帮助组织解决合规性和性能问题。但是，为了进行更精细的分析，必须关联不同的过程特征。例如，偏离规范过程是否会导致额外的延迟和成本？在流程的初始阶段，被拒绝的案件处理方式是否有所不同？医生的经验对治疗过程有什么影响？这些和其他问题可能涉及与不同角度（控制流，数据流，时间，组织，成本，合规性等）有关的过程特征。之前已经研究了特定的问题（例如，预测剩余的处理时间），但是到目前为止，缺少通用方法。提出的框架统一了文献中提出的多种相关分析方法，并提出了可以执行这些分析的通用解决方案。该方法已在ProM中实现，并结合了流程和数据挖掘技术。在本文中，我们还将通过与荷兰最大的“行政工厂”之一的UWV（雇员保险局）进行的案例研究来证明其适用性。

下载地址 | 返回目录 | [10.1016/j.is.2015.07.003]

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

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。我们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]

[14] An interdisciplinary comparison of sequence modeling methods for next-element prediction (2020)

Abstract: Data of sequential nature arise in many application domains in the form of, e.g., textual data, DNA sequences, and software execution traces. Different research disciplines have developed methods to learn sequence models from such datasets: (i) In the machine learning field methods such as (hidden) Markov models and recurrent neural networks have been developed and successfully applied to a wide range of tasks, (ii) in process mining process discovery methods aim to generate human-interpretable descriptive models, and (iii) in the grammar inference field the focus is on finding descriptive models in the form of formal grammars. Despite their different focuses, these fields share a common goal: learning a model that accurately captures the sequential behavior in the underlying data. Those sequence models are generative, i.e., they are able to predict what elements are likely to occur after a given incomplete sequence. So far, these fields have developed mainly in isolation from each other and no comparison exists. This paper presents an interdisciplinary experimental evaluation that compares sequence modeling methods on the task of next-element prediction on four real-life sequence datasets. The results indicate that machine learning methods, which generally do not aim at model interpretability, tend to outperform methods from the process mining and grammar inference fields in terms of accuracy.

摘要： 在许多应用领域中，顺序性质的数据以例如文本数据，DNA序列和软件执行轨迹的形式出现。不同的研究学科已经开发了从此类数据集中学习序列模型的方法：（i）在机器学习领域，诸如（隐式）马尔可夫模型和递归神经网络等方法已经得到开发，并成功应用于各种任务；（ii）在过程挖掘中，过程发现方法旨在生成人类可解释的描述模型，并且（iii）在语法推断领域中，重点在于以形式语法的形式查找描述模型。尽管它们有不同的重点，但这些领域有一个共同的目标：学习一个模型，该模型可以准确地捕获基础数据中的顺序行为。这些序列模型是可生成的，即，它们能够预测在给定的不完整序列之后可能发生的元素。到目前为止，这些领域的发展主要是彼此孤立的，没有可比性。本文提出了一个跨学科的实验评估，该评估对序列建模方法在四个真实序列数据集上的下一元素预测任务进行了比较。结果表明，通常不以模型可解释性为目标的机器学习方法在准确性方面往往优于过程挖掘和语法推理领域的方法。

下载地址 | 返回目录 | [10.1007/s10270-020-00789-3]

[15] Bayesian network construction from event log for lateness analysis in port logistics (2015)

Abstract: “The handling of containers in port logistics consists of several activities, such as discharging, loading, gate-in and gate-out, among others. These activities are carried out using various equipment including quay cranes, yard cranes, trucks, and other related machinery. The high inter-dependency among activities and equipment on various factors often puts successive activities off schedule in real-time, leading to undesirable activity down time and the delay of activities. A late container process, in other words, can negatively affect the scheduling of the following ones. The purpose of the study is to analyze the lateness probability using a Bayesian network by considering various factors in container handling. We propose a method to generate a Bayesian network from a process model which can be discovered from event logs in port information systems. In the network, we can infer the activities lateness probabilities and, sequentially, provide to port managers recommendations for improving existing activities.”

摘要： “港口物流中的集装箱处理包括几项活动，例如卸货，装载，进站和出站。这些活动使用各种设备进行，包括码头起重机，堆场起重机，卡车和其他设备。活动和设备之间在各种因素上的高度相互依赖关系经常使连续活动实时地偏离计划，从而导致不良的活动停工时间和活动延迟，换句话说，延迟的集装箱过程可能会对活动产生负面影响我们的目的是通过考虑容器处理中的各种因素使用贝叶斯网络分析延迟概率，我们提出了一种从过程模型中生成贝叶斯网络的方法，该模型可以从事件日志中发现在网络中，我们可以推断出活动的迟到概率，并依次向港口管理者提供有关改善现有活动。”

下载地址 | 返回目录 | [10.1016/j.cie.2014.11.003]

[16] Business Process Deviance Mining (2019)

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 | [10.1007/978-3-319-77525-8_100]

[17] Categorical reparameterization with gumbel-softmax (2019)

Abstract: Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution. We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification.

摘要： 分类变量是代表世界上离散结构的自然选择。但是，由于无法反向传播样本，因此随机神经网络很少使用分类潜变量。在这项工作中，我们提出了一种有效的梯度估算器，该梯度估算器将分类分布中的不可微样本替换为新型Gumbel-Softmax分布中的可微样本。该分布具有可以平滑地退火为分类分布的基本特性。我们证明，在结构化输出预测和具有分类潜变量的无监督生成建模任务上，我们的Gumbel-Softmax估计器优于最新的梯度估计器，并在半监督分类上实现了大幅度提速。

下载地址 | 返回目录 | []

[18] Clustering-Based Predictive Process Monitoring (2019)

Abstract: The enactment of business processes is generally supported by information systems that record data about each process execution (a.k.a. case). This data can be analyzed via a family of methods broadly known as process mining. Predictive process monitoring is a process mining technique concerned with predicting how running (uncompleted) cases will unfold up to their completion. In this paper, we propose a predictive process monitoring framework for estimating the probability that a given predicate will be fulfilled upon completion of a running case. The framework takes into account both the sequence of events observed in the current trace, as well as data attributes associated to these events. The prediction problem is approached in two phases. First, prefixes of previous (completed) cases are clustered according to control flow information. Second, a classifier is built for each cluster using event data attributes to discriminate between cases that lead to a fulfillment of the predicate under examination and cases that lead to a violation within the cluster. At runtime, a prediction is made on a running case by mapping it to a cluster and applying the corresponding classifier. The framework has been implemented in the ProM toolset and validated on a log pertaining to the treatment of cancer patients in a large hospital.

摘要： 业务流程的制定通常由记录有关每个流程执行（也称为案例）的数据的信息系统支持。可以通过广泛称为过程挖掘的一系列方法来分析此数据。预测性过程监视是一种过程挖掘技术，它与预测正在运行（未完成）的案例将如何完成直至完成有关。在本文中，我们提出了一个预测性过程监视框架，用于估计在运行案例完成后给定谓词将被满足的概率。该框架同时考虑了在当前跟踪中观察到的事件顺序以及与这些事件相关的数据属性。预测问题分为两个阶段。首先，根据控制流信息对先前（完成）案例的前缀进行聚类。其次，使用事件数据属性为每个聚类构建分类器，以区分导致检查中的谓词实现的情况和导致聚类内违规的情况。在运行时，通过将正在运行的案例映射到集群并应用相应的分类器来进行预测。该框架已在ProM工具集中实现，并在与大型医院中癌症患者治疗有关的日志中得到了验证。

下载地址 | 返回目录 | [10.1109/TSC.2016.2645153]

[19] DeepAlign: Alignment-Based Process Anomaly Correction Using Recurrent Neural Networks (2020)

Abstract: In this paper, we propose DeepAlign, a novel approach to multi-perspective process anomaly correction, based on recurrent neural networks and bidirectional beam search. At the core of the DeepAlign algorithm are two recurrent neural networks trained to predict the next event. One is reading sequences of process executions from left to right, while the other is reading the sequences from right to left. By combining the predictive capabilities of both neural networks, we show that it is possible to calculate sequence alignments, which are used to detect and correct anomalies. DeepAlign utilizes the case-level and event-level attributes to closely model the decisions within a process. We evaluate the performance of our approach on an elaborate data corpus of 252 realistic synthetic event logs and compare it to three state-of-the-art conformance checking methods. DeepAlign produces better corrections than the rest of the field reaching an overall F1 score of 0.9572 across all datasets, whereas the best comparable state-of-the-art method reaches 0.6411.

摘要： 在本文中，我们提出了DeepAlign，这是一种基于递归神经网络和双向波束搜索的多视角过程异常校正的新方法。 DeepAlign算法的核心是经过训练可预测下一个事件的两个循环神经网络。一种是从左到右读取流程执行顺序，而另一种是从右到左读取顺序。通过结合两个神经网络的预测能力，我们表明可以计算用于检测和纠正异常的序列比对。 DeepAlign利用案例级和事件级属性来紧密建模流程中的决策。我们在252个真实的合成事件日志的详尽数据集上评估了我们的方法的性能，并将其与三种最新的一致性检查方法进行了比较。与所有其他数据集相比，DeepAlign产生的校正要好于其余字段，在所有数据集中的F1总得分为0.9572，而最佳可比的最新方法达到0.6411。

下载地址 | 返回目录 | [10.1007/978-3-030-49435-3_20]

[20] Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction (2020)

Abstract: Predictive process monitoring aims at forecasting the behavior, performance, and outcomes of business processes at runtime. It helps identify problems before they occur and re-allocate resources before they are wasted. Although deep learning (DL) has yielded breakthroughs, most existing approaches build on classical machine learning (ML) techniques, particularly when it comes to outcome-oriented predictive process monitoring. This circumstance reflects a lack of understanding about which event log properties facilitate the use of DL techniques. To address this gap, the authors compared the performance of DL (i.e., simple feedforward deep neural networks and long short term memory networks) and ML techniques (i.e., random forests and support vector machines) based on five publicly available event logs. It could be observed that DL generally outperforms classical ML techniques. Moreover, three specific propositions could be inferred from further observations: First, the outperformance of DL techniques is particularly strong for logs with a high variant-to-instance ratio (i.e., many non-standard cases). Second, DL techniques perform more stably in case of imbalanced target variables, especially for logs with a high event-to-activity ratio (i.e., many loops in the control flow). Third, logs with a high activity-to-instance payload ratio (i.e., input data is predominantly generated at runtime) call for the application of long short term memory networks. Due to the purposive sampling of event logs and techniques, these findings also hold for logs outside this study.

摘要： “预测性流程监视旨在在运行时预测业务流程的行为，性能和结果。它有助于在问题发生之前就确定问题，并在浪费之前重新分配资源。尽管深度学习（DL）取得了突破，但是大多数现有方法都建立在经典机器学习（ML）技术的基础上，尤其是在面向结果的预测过程监视方面。这种情况反映了缺乏对哪些事件日志属性有助于DL技术使用的理解。为了解决这一差距，作者根据五个公开可用的事件日志比较了DL（即简单的前馈深度神经网络和长期短期记忆网络）和ML技术（即随机森林和支持向量机）的性能。可以观察到，DL通常优于经典的ML技术。此外，可以从进一步的观察中推断出三个具体的主张：首先，对于高变体实例比率（即，许多非标准案例）的原木，DL技术的性能特别强。其次，在目标变量不平衡的情况下，DL技术的性能会更加稳定，尤其是对于具有较高事件与活动比率（即，控制流中的许多循环）的日志而言。第三，活动对实例有效负载比高的日志（即输入数据主要在运行时生成）要求使用长期短期存储网络。由于对事件日志和技术进行了有目的的抽样，这些发现也适用于本研究之外的日志。

下载地址 | 返回目录 | [10.1007/s12599-020-00645-0]

[21] Novice debugging in block-based and hybrid environments (2020)

Abstract: “Debugging is an important skill for novice programmers to master, but many students struggle to learn how to debug due in part to difficulty with program syntax. Block-based environments provide an alternative to traditional textual programming that reduces syntax errors, and recently hybrid block-based/textual environments have become more common. This poster presents preliminary research to understand how novice debugging strategies differ between blockbased and hybrid environments. We assigned seven participants to debug four programs within one of the two environments and conducted interviews about their debugging approaches. Thematic analysis of interview responses suggest that students adjusted their strategies based on their prior experience with textual environments. By understanding novice programmers strategies in these environments, the field can move toward more effectively supporting productive strategies.”

摘要： “调试是新手程序员掌握的一项重要技能，但是许多学生都在努力学习如何调试，部分原因是程序语法困难。基于块的环境为传统的文本编程提供了一种替代方法，可以减少语法错误，并且最近可以混合使用基于块/文本的环境已变得更加普遍。此海报提供了初步研究以了解基于块的环境和混合环境之间的新手调试策略有何不同。我们分配了七名参与者来调试这两种环境之一中的四个程序，并就其调试方法进行了访谈。对访谈回复的主题分析表明，学生根据先前在文本环境中的经验调整了他们的策略。通过了解新手程序员在这些环境中的策略，该领域可以朝着更有效地支持生产策略的方向发展。”

下载地址 | 返回目录 | [10.1145/1122445.1122456]

[22] Predicting process behaviour using deep learning (2017)

Abstract: Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.

摘要： 预测业务流程行为是业务流程管理的重要方面。受自然语言处理研究的推动，本文描述了具有递归神经网络的深度学习在预测业务流程中下一个事件的问题中的应用。这既是在很大程度上依赖于显式过程模型的过程预测中的一种新颖方法，又是深度学习方法的一种新颖应用。该方法在两个真实的数据集上进行了评估，我们的结果在预测精度上超过了最新技术。

下载地址 | 返回目录 | [10.1016/j.dss.2017.04.003]

[23] Predictive monitoring of business processes: A survey (2018)

Abstract: Nowadays, process mining is becoming a growing area of interest in business process management (BPM). Process mining consists in the extraction of information from the event logs of a business process. From this information, we can discover process models, monitor and improve our processes. One of the applications of process mining, is the predictive monitoring of business process. The aim of these techniques is the prediction of quantifiable metrics of a running process instance with the generation of predictive models. The most representative approaches for the runtime prediction of business process are summarized in this paper. The different types of computational predictive methods, such as statistical techniques or machine learning approaches, and certain aspects as the type of predicted values and quality evaluation metrics, have been considered for the categorization of these methods. This paper also includes a summary of the basic concepts, as well as a global overview of the process predictive monitoring area, that can be used to support future efforts of researchers and practitioners in this research field.

摘要： 如今，流程挖掘已成为业务流程管理（BPM）的一个增长领域。流程挖掘包括从业务流程的事件日志中提取信息。从这些信息中，我们可以发现流程模型，监视和改进我们的流程。流程挖掘的应用之一是对业务流程的预测性监视。这些技术的目的是通过生成预测模型来预测正在运行的流程实例的可量化指标。本文总结了最有代表性的业务流程运行时预测方法。对于这些方法的分类，已经考虑了不同类型的计算预测方法，例如统计技术或机器学习方法，以及某些方面，如预测值和质量评估指标的类型。本文还包括基本概念的摘要以及过程预测监视领域的全局概述，这些过程可用于支持研究人员和从业人员在该研究领域的未来努力。

下载地址 | 返回目录 | [10.1109/TSC.2017.2772256]

[24] Process mining for python (PM4py): Bridging the gap between process- And data science (2019)

Abstract: “Process mining, i.e., a sub-field of data science focusing on the analysis of event data generated during the execution of (business) processes, has seen a tremendous change over the past two decades. Starting off in the early 2000s, with limited to no tool support, nowadays, several software tools, i.e., both open-source, e.g., ProM and Apromore, and commercial, e.g., Disco, Celonis, ProcessGold, etc., exist. The commercial process mining tools provide limited support for implementing custom algorithms. Moreover, both commercial and open-source process mining tools are often only accessible through a graphical user interface, which hampers their usage in large-scale experimental settings. Initiatives such as RapidProM provide process mining support in the scientific workflow-based data science suite RapidMiner. However, these offer limited to no support for algorithmic customization. In the light of the aforementioned, in this paper, we present a novel process mining library, i.e., Process Mining for Python (PM4Py), that aims to bridge this gap, providing integration with state-of-the-art data science libraries, e.g., pandas, numpy, scipy and scikit-learn. We provide a global overview of the architecture and functionality of PM4Py, accompanied by some representative examples of its usage.”

摘要： “过程挖掘，即专注于分析（业务）过程执行过程中生成的事件数据的数据科学子领域，在过去的二十年中发生了巨大的变化。从2000年代初期开始，限于无工具支持，如今，存在几种软件工具，即开源软件（例如ProM和Apromore）和商业软件（例如Disco，Celonis，ProcessGold等）。此外，通常只能通过图形用户界面访问商业和开源过程挖掘工具，这阻碍了它们在大规模实验环境中的使用； RapidProM等举措在基于科学工作流的基础上提供了过程挖掘支持。数据科学套件RapidMiner。但是，这些提供的功能不限于不支持算法定制，因此，鉴于上述原因，在本文中，我们提出了一个新颖的过程挖掘库，即Python的过程挖掘（PM4Py）旨在弥合这一差距，提供与最新数据科学库（例如熊猫，numpy，scipy和scikit-learn）的集成。我们提供了有关PM4Py的体系结构和功能的全球概述，并附有其用法的一些代表性示例。”

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[25] Towards a data science toolbox for industrial analytics applications (2018)

Abstract: Manufacturing companies today have access to a vast number of data sources providing gigantic amounts of process and status data. Consequently, the need for analytical information systems is ever-growing to guide corporate decision-making. However, decision-makers in production environments are still very much focused on static, explanatory modeling provided by business intelligence suites instead of embracing the opportunities offered by predictive analytics. We develop a data science toolbox for manufacturing prediction tasks to bridge the gap between machine learning research and concrete practical needs. We provide guidelines and best practices for modeling, feature engineering and interpretation. To this end, we leverage tools from business information systems as well as machine learning. We illustrate the usage of this toolbox by means of a real-world manufacturing defect prediction case study. Thereby, we seek to enhance the understanding of predictive modeling. In particular, we want to emphasize that simply dumping data into “smart” algorithms is not the silver bullet. Instead, constant refinement and consolidation are required to improve the predictive power of a business analytics solution.

摘要： 今天的制造公司可以访问提供大量过程和状态数据的大量数据源。因此，对于指导公司决策的分析信息系统的需求不断增长。但是，生产环境中的决策者仍然非常专注于商业智能套件提供的静态，解释性模型，而不是拥抱预测分析提供的机会。我们开发了用于制造预测任务的数据科学工具箱，以弥合机器学习研究与具体实际需求之间的鸿沟。我们提供有关建模，特征工程和解释的指南和最佳实践。为此，我们利用商业信息系统和机器学习中的工具。我们通过实际的制造缺陷预测案例研究来说明此工具箱的用法。因此，我们寻求增强对预测建模的理解。特别要强调的是，简单地将数据转储到“智能”算法中并不是万灵丹。相反，需要不断完善和整合以提高业务分析解决方案的预测能力。

下载地址 | 返回目录 | [10.1016/j.compind.2017.09.003]

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