Name
Abstract | ||
|---|---|---|
Real-life processes are typically less structured and more complex than expected by stakeholders. For this reason, process discovery techniques often deliver models less understandable and useful than expected. In order to address this issue, we propose a method based on statistical inference for pre-processing event logs. We measure the distance between different segments of the event log, computing the probability distribution of observing activities in specific positions. Because segments are generated based on time-domain, business rules or business management system properties, we get a characterisation of these segments in terms of both business and process aspects. We demonstrate the applicability of this approach by developing a case study with real-life event logs and showing that our method is offering interesting properties in term of computational complexity. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-65015-9_4 | Lecture Notes in Business Information Processing |
Keywords | Field | DocType |
Process mining,Event-log clustering,Pre-processing,Lightweight trace profiling | Data mining,Computer science,Probability distribution,Statistical inference,Business management,Business process discovery,Business rule,Process mining,Process management,Computational complexity theory | Conference |
Volume | ISSN | Citations |
297 | 1865-1348 | 1 |
PageRank | References | Authors |
0.36 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Ceravolo | 1 | 252 | 44.89 |
| Ernesto Damiani | 2 | 3911 | 416.18 |
| Mohammadsadegh Torabi | 3 | 1 | 0.36 |
| Sylvio Barbon | 4 | 46 | 10.97 |