ORCID Profile
0000-0002-5088-7602
Current Organisation
University of Adelaide
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Publisher: P.G. Demidov Yaroslavl State University
Date: 19-12-2018
DOI: 10.18255/1818-1015-2018-6-711-725
Abstract: Finding graph-edit distance (graph similarity) is an important task in many computer science areas, such as image analysis, machine learning, chemicalinformatics. Recently, with the development of process mining techniques, it became important to adapt and apply existing graph analysis methods to examine process models (annotated graphs) discovered from event data. In particular, finding graph-edit distance techniques can be used to reveal patterns (subprocesses), compare discovered process models. As it was shown experimentally and theoretically justified, exact methods for finding graph-edit distances between discovered process models (and graphs in general) are computationally expensive and can be applied to small models only. In this paper, we present and assess accuracy and performance characteristics of an inexact genetic algorithm applied to find distances between process models discovered from event logs. In particular, we find distances between BPMN (Business Process Model and Notation) models discovered from event logs by using different process discovery algorithms. We show that the genetic algorithm allows us to dramatically reduce the time of comparison and produces results which are close to the optimal solutions (minimal graph edit distances calculated by the exact search algorithm).
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2017
Publisher: Emerald
Date: 16-10-2018
DOI: 10.1108/BPMJ-02-2018-0051
Abstract: The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs. This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition. The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 20-10-2015
Publisher: Pleiades Publishing Ltd
Date: 09-2010
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 05-2017
Publisher: Springer International Publishing
Date: 21-12-2018
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2014
Publisher: Pleiades Publishing Ltd
Date: 2012
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Springer International Publishing
Date: 2018
Location: Russian Federation
No related grants have been discovered for Anna Kalenkova.