Name
Abstract | ||
|---|---|---|
Early detection of business process drifts from event logs enables analysts to identify changes that may negatively affect process performance. However, detecting a process drift without characterizing its nature is not enough to support analysts in understanding and rectifying process performance issues. We propose a method to characterize process drifts from event streams, in terms of the behavioral relations that are modified by the drift. The method builds upon a technique for online drift detection, and relies on a statistical test to select the behavioral relations extracted from the stream that have the highest explanatory power. The selected relations are then mapped to typical change patterns to explain the detected drifts. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in characterizing process drifts, and performs significantly better than alternative techniques. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59536-8_14 | ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2017) |
Field | DocType | Volume |
Business process management,Data mining,Business process,Computer science,Concept drift,Explanatory power,STREAMS,Change patterns,Statistical hypothesis testing,Process mining | Conference | 10253 |
ISSN | Citations | PageRank |
0302-9743 | 7 | 0.48 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alireza Ostovar | 1 | 34 | 3.04 |
| Abderrahmane Maaradji | 2 | 89 | 6.87 |
| marcello la rosa | 3 | 1402 | 81.70 |
| Arthur H. M. ter Hofstede | 4 | 537 | 52.24 |