Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning
نویسندگان
چکیده
Business process simulation is a well-known approach to estimate the impact of changes with respect time and cost measures -- practice known as what-if analysis. The usefulness such estimations hinges on accuracy underlying model. Data-Driven Simulation (DDS) methods leverage mining techniques learn models from event logs. Empirical studies have shown that, while DDS adequately capture observed sequences activities their frequencies, they fail accurately temporal dynamics real-life processes. In contrast, generative Deep Learning (DL) are better able dynamics. drawback DL that users cannot alter them for analysis due black-box nature. This paper presents hybrid logs wherein (stochastic) model extracted via techniques, then combined generate timestamped sequences. An experimental evaluation shows resulting match pure models, partially retaining capability approaches.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2022
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-07472-1_4