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 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Engineering(all)
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Discovery of temporal patterns from process instances. / Hwang, San Yih; Wei, Chih Ping; Yang, Wan Shiou.
In: Computers in Industry, Vol. 53, No. 3, 01.04.2004, p. 345-364.Research output: Contribution to journal › Article
TY - JOUR
T1 - Discovery of temporal patterns from process instances
AU - Hwang, San Yih
AU - Wei, Chih Ping
AU - Yang, Wan Shiou
PY - 2004/4/1
Y1 - 2004/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0347600602&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0347600602&partnerID=8YFLogxK
U2 - 10.1016/j.compind.2003.10.006
DO - 10.1016/j.compind.2003.10.006
M3 - Article
AN - SCOPUS:0347600602
VL - 53
SP - 345
EP - 364
JO - Computers in Industry
JF - Computers in Industry
SN - 0166-3615
IS - 3
ER -