Automatic Discovery of Multi-perspective Process Model using
Reinforcement Learning
- URL: http://arxiv.org/abs/2211.16687v1
- Date: Wed, 30 Nov 2022 02:18:29 GMT
- Title: Automatic Discovery of Multi-perspective Process Model using
Reinforcement Learning
- Authors: Sunghyun Sim, Ling Liu, Hyerim Bae
- Abstract summary: We propose an automatic discovery framework of a multi-perspective process model based on deep Q-Learning.
Our Dual Experience Replay with Experience Distribution (DERED) approach can automatically perform process model discovery steps, conformance check steps, and enhancements steps.
We validate our approach using six real-world event datasets collected in port logistics, steel manufacturing, finance, IT, and government administration.
- Score: 7.5989847759545155
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Process mining is a methodology for the derivation and analysis of process
models based on the event log. When process mining is employed to analyze
business processes, the process discovery step, the conformance checking step,
and the enhancements step are repeated. If a user wants to analyze a process
from multiple perspectives (such as activity perspectives, originator
perspectives, and time perspectives), the above procedure, inconveniently, has
to be repeated over and over again. Although past studies involving process
mining have applied detailed stepwise methodologies, no attempt has been made
to incorporate and optimize multi-perspective process mining procedures. This
paper contributes to developing a solution approach to this problem. First, we
propose an automatic discovery framework of a multi-perspective process model
based on deep Q-Learning. Our Dual Experience Replay with Experience
Distribution (DERED) approach can automatically perform process model discovery
steps, conformance check steps, and enhancements steps. Second, we propose a
new method that further optimizes the experience replay (ER) method, one of the
key algorithms of deep Q-learning, to improve the learning performance of
reinforcement learning agents. Finally, we validate our approach using six
real-world event datasets collected in port logistics, steel manufacturing,
finance, IT, and government administration. We show that our DERED approach can
provide users with multi-perspective, high-quality process models that can be
employed more conveniently for multi-perspective process mining.
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