Name
Abstract | ||
|---|---|---|
Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-49435-3_19 | CAiSE |
DocType | Citations | PageRank |
Conference | 2 | 0.37 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taymouri Farbod | 1 | 2 | 0.37 |
| marcello la rosa | 2 | 1402 | 81.70 |
| Carmona Josep | 3 | 2 | 0.37 |