Name
Abstract | ||
|---|---|---|
Considerable amounts of business process event logs can be collected by modern information systems. Process discovery aims to uncover a process model from an event log. Many process discovery approaches have been proposed, however, most of them have difficulties in handling large-scale event logs. Motivated by PageRank, in this paper we propose LogRank, a graph-based ranking model, for event log sampling. Using LogRank, a large-scale event log can be sampled to a smaller size that can be efficiently handled by existing discovery approaches. Moreover, we introduce an approach to measure the quality of a sample log with respect to the original one from a discovery perspective. The proposed sampling approach has been implemented in the open-source process mining toolkit ProM. The experimental analyses with both synthetic and real-life event logs demonstrate that the proposed sampling approach provides an effective solution to improve process discovery efficiency as well as ensuring high quality of the discovered model. |
Year | Venue | Field |
|---|---|---|
2018 | KSEM | Information system,PageRank,Data mining,Prom,Ranking,Business process,Computer science,Sampling (statistics),Artificial intelligence,Business process discovery,Machine learning,Process mining |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cong Liu | 1 | 128 | 14.67 |
| Yulong Pei | 2 | 47 | 13.84 |
| Qingtian Zeng | 3 | 242 | 43.67 |
| Hua Duan | 4 | 110 | 19.58 |