Name
Abstract | ||
|---|---|---|
Many forms of data analysis require timestamp information to order the occurrences of events. The process mining discipline uses historical records of process executions, called event logs, to derive insights into business process behaviours and performance. Events in event logs must be ordered, typically achieved using timestamps. The importance of timestamp information means that it needs to be of high quality. To the best of our knowledge, no (semi-) automated support exists for detecting and repairing ordering-related imperfection issues in event logs. We describe a set of timestamp-based indicators for detecting event ordering imperfection issues in a log and our approach to repairing identified issues using domain knowledge. Lastly, we evaluate our approach implemented in the open-source process mining framework, ProM, using two publicly available logs. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-91563-0_17 | ADVANCED INFORMATION SYSTEMS ENGINEERING, CAISE 2018 |
Keywords | Field | DocType |
Event log imperfection,Event ordering,Data quality | Data mining,Prom,Data quality,Business process,Domain knowledge,Computer science,Work in process,Timestamp,Process mining | Conference |
Volume | ISSN | Citations |
10816 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 17 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prabhakar M. Dixit | 1 | 1 | 0.72 |
| Suriadi Suriadi | 2 | 210 | 18.89 |
| robert d andrews | 3 | 21 | 2.95 |
| Moe Thandar Wynn | 4 | 168 | 18.68 |
| arthur h m ter hofstede | 5 | 2913 | 200.53 |
| Joos C. A. M. Buijs | 6 | 298 | 19.02 |
| W. M. Aalst | 7 | 59 | 7.56 |