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.
Original language | English |
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Title of host publication | Advanced Information Systems Engineering - 30th International Conference, CAiSE 2018, Proceedings |
Publisher | Springer |
Pages | 274-290 |
Number of pages | 17 |
ISBN (Print) | 9783319915623 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
Event | 30th International Conference on Advanced Information Systems Engineering, CAiSE 2018 - Tallinn, Estonia Duration: 11 Jun 2018 → 15 Jun 2018 Conference number: 30 https://caise2018.ut.ee/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10816 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 30th International Conference on Advanced Information Systems Engineering, CAiSE 2018 |
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Abbreviated title | CAiSE 2018 |
Country/Territory | Estonia |
City | Tallinn |
Period | 11/06/18 → 15/06/18 |
Internet address |
Keywords
- Data quality
- Event log imperfection
- Event ordering