Name
Abstract | ||
|---|---|---|
Process mining is a family of techniques to analyze business processes based on event logs recorded by their supporting information systems. Two recurrent bottlenecks of existing process mining techniques when confronted with real-life event logs are scalability and interpretability of the outputs. A common approach to tackle these limitations is to decompose the process under analysis into a set of stages, such that each stage can be mined separately. However, existing techniques for automated discovery of stages from event logs produce decompositions that are very different from those that domain experts would produce manually. This paper proposes a technique that, given an event log, discovers a stage decomposition that maximizes a measure of modularity borrowed from the field of social network analysis. An empirical evaluation on real-life event logs shows that the produced decompositions more closely approximate manual decompositions than existing techniques. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59536-8_36 | ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2017) |
Keywords | Field | DocType |
Process mining,Decomposition,Clustering,Modularity,Multistage | Information system,Data mining,Interpretability,Business process,Computer science,Artificial intelligence,Cluster analysis,Business process discovery,Machine learning,Modularity,Scalability,Process mining | Conference |
Volume | ISSN | Citations |
10253 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hoang Nguyen | 1 | 36 | 7.00 |
| Marlon Dumas | 2 | 5742 | 371.10 |
| arthur h m ter hofstede | 3 | 2913 | 200.53 |
| marcello la rosa | 4 | 1402 | 81.70 |
| Fabrizio Maria Maggi | 5 | 832 | 45.07 |