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2019 International Conference on Process Mining (ICPM)
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.
Proceedings of ISP RAS are a double-blind peer-reviewed journal publishing scientific articles in the areas of system programming, software engineering, and computer science. The journal's goal is to develop a respected network of knowledge in the mentioned above areas by publishing high quality articles on open access. The journal is intended for researchers, students, and practitioners.
Process mining is a new emerging discipline related to process management, formal process models, and data mining. One of the main tasks of process mining is the model synthesis (discovery) based on the event logs. A wide range of algorithms for process model discovery, analysis, and enhancement are developed. The real-life event logs often contain noise of different types. In this paper we describe the main causes of noise in the event logs, and study the effect of noise on the performance of process discovery algorithms. The experimental results of application of the main process discovery algorithms to artificial event logs with noise are provided. Specially generated event logs with noise of different types were processed using the four basic discovery techniques. Although modern algorithms can cope with some types of noise, in most cases, their use does not lead to obtaining a satisfactory result. Thus, there is a need for more sophisticated algorithms to deal with noise of different types.
On the Project of Development of Global Processes Monitoring System Based on Internet News. An approach to the processes analysis on the basis of the data on events mined from newsfeeds is described. Retrieved data are processed with the means of Process Mining allowing constructing the formal models of processes.
Seit ihrem Entwurf im Jahr 1962 sind Petrinetze in ganz unterschiedlichen Bereichen eingesetzt worden. Obwohl sie graphisch dargestellt werden und intuitiv einfach verständlich sind, haben Petrinetze eine formal eindeutige Semantik mit einer Vielzahl mathematischer Analysetechniken. Sie reichen vom Model Checking und der Strukturellen Analyse über das Process Mining bis zur Performanz-Analyse. Im Lauf der Zeit haben Petrinetze solide Grundlagen für die Forschung zum Geschäftsprozess-Management (BPM) beigetragen. Sie umfassen Methoden, Techniken und Werkzeuge um Geschäftsprozesse zu entwerfen, implementieren, verwalten und zu analysieren. Die etablierten Modellierungsmethoden und Workflow-Managementsysteme verwenden Token-basierte, von Petrinetzen entlehnte Beschreibungen. Nutzer moderner BPM-Analysetechniken wissen oft gar nicht, dass ihre Geschäfts- prozesse intern als Petrinetze repräsentiert werden. Dieser Beitrag zeigt die grundlegende Rolle von Petrinetzen im BPM.
Since their inception in 1962, Petri nets have been used in a wide variety of application domains. Although Petri nets are graphical and easy to understand, they have formal semantics and allow for analysis techniques ranging from model checking and structural analysis to process mining and performance analysis. Over time Petri nets emerged as a solid foundation for Business Process Management (BPM) research. The BPM discipline develops methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. Mainstream business process modeling notations and workflow management systems are using token-based semantics borrowed from Petri nets. Moreover, state-of-the-art BPM analysis techniques are using Petri nets as an internal representation. Users of BPM methods and tools are often not aware of this. This paper aims to unveil the seminal role of Petri nets in BPM.
Companies from various domains record their operational behavior in a form of event logs. These event logs can be analyzed and relevant process models representing the real companies’ behavior can be discovered. One of the main advantages of the process discovery methods is that they commonly produce models in a form of graphs which can be easily visualized giving an intuitive view of the executed processes. Moreover, the graph-based representation opens new challenging perspectives for the application of graph comparison methods to find and explicitly visualize differences between discovered process models (representing real behavior) and reference process models (representing expected behavior). Another important area where graph comparison algorithms can be used is the recognition of process modeling patterns. Unfortunately, exact graph comparison algorithms are computationally expensive. In this paper, we adapt an inexact tabu search algorithm to find differences between BPMN (Business Process Model and Notation) models. The tabu search and greedy algorithms were implemented within the BPMNDiffViz tool and were tested on BPMN models discovered from synthetic and real-life event logs. It was experimentally shown that inexact tabu search algorithm allows to find a solution which is close to the optimal in most of the cases. At the same, its computational complexity is significantly lower than the complexity of the exact A search algorithm investigated earlier.
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.