Abstract
Existing work in process mining focuses on the discovery of the underlying process model from their instances. In this paper, we do not assume the existence of a single process model to which all process instances comply, and the goal is to discover a set of frequently occurring temporal patterns. Discovery of temporal patterns can be applied to various application domains to support crucial business decision-making. In this study, we formally defined the temporal pattern discovery problem, and developed and evaluated three different temporal pattern discovery algorithms, namely TP-Graph, TP-Itemset and TP-Sequence. Their relative performances are reported.
| Original language | English |
|---|---|
| Pages (from-to) | 345-364 |
| Number of pages | 20 |
| Journal | Computers in Industry |
| Volume | 53 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2004 Apr 1 |
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Engineering(all)
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Cite this
Hwang, S. Y., Wei, C. P., & Yang, W. S. (2004). Discovery of temporal patterns from process instances. Computers in Industry, 53(3), 345-364. https://doi.org/10.1016/j.compind.2003.10.006