Computing and Information Systems - Research Publications
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ItemAutomated Repair of Process Models with Non-Local Constraints Using State-Based Region Theory(IOS PRESS, 2021-06-26)State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.
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ItemAgent System Mining: Vision, Benefits, and Challenges(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021)In the Agent-Based Modeling (ABM) paradigm, an organization is a Multi-Agent System (MAS) composed of autonomous agents inducing business processes. Process Mining automates the creation, update, and analysis of explicit business process models based on event data. Process Mining techniques make simplifying assumptions about the processes discovered from data. However, actual business processes are often more complex than those restricted by Process Mining assumptions. Several Process Mining approaches relax these standard assumptions by discovering more realistic process models. These approaches can discover more realistic process models. However, these models are often difficult to visualize and, consequently, to understand. Many MASs induce processes whose behaviors become more complex with each next embraced time step, while the complexities of these MASs remain constant. Thus, the ABM paradigm can cope naturally with the increasing complexity of the discovered process models. This paper proposes Agent System Mining (ASM) and ASM Framework. ASM combines Process Mining and ABM in the Business Process Management (BPM) context to infer MAS models of operational business processes from real-world event data, while ASM Framework maps ASM activities to different phases of the MAS modeling lifecycle. The paper also discusses the benefits of using ASM and outlines challenges associated with the implementation of the ASM Framework.
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ItemStructural and Behavioral Biases in Process Comparison Using Models and Logs(Springer, 2021)Process models automatically discovered from event logs represent business process behavior in a compact graphical way. To compare process variants, e.g., to explore how the system’s behavior changes over time or between customer segments, analysts tend to visually compare conceptual process models discovered from different “slices” of the event log, solely relying on the structure of these models. However, the structural distance between two process models does not always reflect the behavioral distance between the underlying event logs and thus structural comparison should be applied with care. This paper aims to investigate relations between structural and behavioral process distances and explain when structural distance between two discovered process models can be used to assess the behavioral distance between the corresponding event logs. Keywords: Process mining · Variant analysis · Structural distance · BPMN
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ItemAutomated Repair of Process Models with Non-local Constraints Using State-Based Region Theory(IOS Press, 2022-01-10)State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.
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ItemConformance checking of partially matching processes: An entropy-based approach(Elsevier, 2021-01-20)Conformance checking is an area of process mining that studies methods for measuring and characterizing commonalities and discrepancies between processes recorded in event logs of IT-systems and designed processes, either captured in explicit process models or implicitly induced by information systems. Applications of conformance checking range from measuring the quality of models automatically discovered from event logs, via regulatory process compliance, to automated process enhancement. Recently, process mining researchers initiated a discussion on the desired properties the conformance measures should possess. This discussion acknowledges that existing measures often do not satisfy the desired properties. Besides, there is a lack of understanding by the process mining community of the desired properties for conformance measures that address partially matching processes, i.e., processes that are not identical but differ in some process steps. In this article, we extend the recently introduced precision and recall conformance measures between an event log and process model that are based on the concept of entropy from information theory to account for partially matching processes. We discuss the properties the presented extended measures inherit from the original measures as well as properties for partially matching processes the new measures satisfy. All the presented conformance measures have been implemented in a publicly available tool. We present qualitative and quantitative evaluations based on our implementation that show the feasibility of using the proposed measures in industrial settings.
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ItemEntropia: A family of entropy-based conformance checking measures for process mining(CEUR Workshop Proceedings, 2020-01-01)This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encodes most of the traces from the log. By definition, the measures possess useful properties and can often be computed quickly.
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ItemA Spectrum of Entropy-Based Precision and Recall Measurements Between Partially Matching Designed and Observed Processes(Springer, 2020)Modern software systems are often built using service-oriented principles. Atomic components, be that web-or micro services, allow constructing flexible and loosely coupled systems. In such systems, services are building blocks orchestrated by business processes the system supports. Due to the complexity and heterogeneity of industrial software systems, implemented processes may deviate from those initially designed. In this paper, we propose a spectrum of conformance measurements. The spectrum results from a generalization of the recently introduced entropy-based approaches for measuring precision and recall between observed process executions and designed process models. The new generalized measures of precision and recall inherit the desired for this class of measures properties and provide analysts with flexible control over the sensitivity for identifying commonalities and discrepancies in the compared processes and performance of the techniques. The reported evaluation based on our implementation of the measures over real-world event logs and automatically discovered models confirms the feasibility of using the approach in industrial settings.
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ItemA Framework for Estimating Simplicity of Automatically Discovered Process Models Based on Structural and Behavioral Characteristics(Springer, 2020)A plethora of algorithms for automatically discovering process models from event logs has emerged. The discovered models are used for analysis and come with a graphical flowchart-like representation that supports their comprehension by analysts. According to the Occam’s Razor principle, a model should encode the process behavior with as few constructs as possible, that is, it should not be overcomplicated without necessity. The simpler the graphical representation, the easier the described behavior can be understood by a stakeholder. Conversely, and intuitively, a complex representation should be harder to understand. Although various conformance checking techniques that relate the behavior of discovered models to the behavior recorded in event logs have been proposed, there are no methods for evaluating whether this behavior is represented in the simplest possible way. Existing techniques for measuring the simplicity of discovered models focus on their structural characteristics such as size or density, and ignore the behavior these models encoded. In this paper, we present a conceptual framework that can be instantiated into a concrete approach for estimating the simplicity of a model, considering the behavior the model describes, thus allowing a more holistic analysis. The reported evaluation over real-life event logs for several instantiations of the framework demonstrates its feasibility in practice.
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ItemAutomated Repair of Process Models Using Non-Local Constraints(Springer, 2020)State-of-the-art process discovery methods construct free-choice process models from event logs. Hence, the constructed models do not take into account indirect dependencies between events. Whenever the input behavior is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for the enhancement of free-choice process models, by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from both the performance of existing process discovery methods, and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log, while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing process models discovered from event logs.
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ItemMonotone Conformance Checking for Partially Matching Designed and Observed Processes(IEEE, 2019-06-24)Conformance checking is a subarea of process mining that studies relations between designed processes, also called process models, and records of observed processes, also called event logs. In the last decade, research in conformance checking has proposed a plethora of techniques for characterizing the discrepancies between process models and event logs. Often, these techniques are also applied to measure the quality of process models automatically discovered from event logs. Recently, the process mining community has initiated a discussion on the desired properties of such measures. This discussion witnesses the lack of measures with the desired properties and the lack of properties intended for measures that support partially matching processes, i.e., processes that are not identical but differ in some steps. The paper at hand addresses these limitations. Firstly, it extends the recently introduced precision and recall conformance measures between process models and event logs that possess the desired property of monotonicity with the support of partially matching processes. Secondly, it introduces new intuitively desired properties of conformance measures that support partially matching processes and shows that our measures indeed possess them. The new measures have been implemented in a publicly available tool. The reported qualitative and quantitative evaluations based on our implementation demonstrate the feasibility of using the proposed measures in industrial settings.