Stochastic Process Discovery: Can It Be Done Optimally?

被引:3
作者
Leemans, Sander J. J. [1 ,2 ]
Li, Tian [1 ,4 ]
Montali, Marco [3 ]
Polyvyanyy, Artem [4 ]
机构
[1] Rhein Westfal TH Aachen, Aachen, Germany
[2] Fraunhofer, Frankfurt, Germany
[3] Free Univ Bozen Bolzano, Bolzano, Italy
[4] Univ Melbourne, Melbourne, Australia
来源
ADVANCED INFORMATION SYSTEMS ENGINEERING, CAISE 2024 | 2024年 / 14663卷
关键词
Stochastic process mining; stochastic process discovery;
D O I
10.1007/978-3-031-61057-8_3
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Process discovery is the problem of automatically constructing a process model from an event log of an information system that supports the execution of a business process in an organisation. In this paper, we study how to construct models that, in addition to the control flow of the process, capture the importance, in terms of probabilities, of various execution scenarios of the process. Such probabilistic aspects of the process are instrumental in understanding the process and to predict aspects of its future. We formally define the problem of stochastic process discovery, which aims to describe the processes captured in the event log. We study several implications of this definition, and introduce two discovery techniques that return optimal solutions in the presence and absence of a model of the control flow of the process. The proposed discovery techniques have been implemented and are publicly available. Finally, we evaluate the feasibility and applicability of the new techniques and show that their models outperform models constructed using existing stochastic discovery techniques.
引用
收藏
页码:36 / 52
页数:17
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