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An Algorithm for Mining Indirect Dependencies From Loop-Choice-Driven Loop Structure via Petri Nets
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AI
DOI：10.1109/TSMC.2021.3126473.png)
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Abstract
En 中文
Process mining is an emerging technology used to extract, detect, and improve actual processes by extracting knowledge from event logs generated from information systems. In the production process, we can obtain the optimal process based on practical experience. Indirect dependencies may exist among different structures in the optimal process model discovered from the event log of the executions that perform better. However, the existing process mining algorithms cannot effectively mine the indirect dependencies among different structures. To compensate for this deficiency, an algorithm named AlphaID is proposed in this article, and it can mine the indirect dependencies in a loop-choice-driven loop structure. First, two algorithms are proposed to efficiently identify loop sequences and choice sequences from event logs. Then, the concept of association rules is proposed to describe indirect dependencies among different structures. Next, we expand the ordinary Petri net and redefine the new transition firing rules to represent the process model obtained by AlphaID. Finally, the correctness and effectiveness of the algorithm are verified by an artificial case and a real case. AlphaID is integrated into the ProM which is an open-source process mining tool platform as a plug-in.
Keywords:
Petri nets
Production
Data mining
Sun
PROM
Data models
Blockchains
Indirect dependency
loop structure
Petri net
process mining
ProM
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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