Abstract
Process mining is an emerging discipline whose aim is to discover, monitor and improve real processes by extracting knowledge from event logs representing actual process executions in a given organizational setting. In this light, it can be applied only if faithful event logs, adhering to accepted standards (such as XES), are available. In many real-world settings, though, such event logs are not explicitly given, but are instead implicitly represented inside legacy information systems of organizations, which are typically managed through relational technology. In this work, we devise a novel framework that supports domain experts in the extraction of XES event log information from legacy relational databases, and consequently enables the application of standard process mining tools on such data. Differently from previous work, the extraction is driven by a conceptual representation of the domain of interest in terms of an ontology. On the one hand, this ontology is linked to the underlying legacy data leveraging the well-established ontology-based data access (OBDA) paradigm. On the other hand, our framework allows one to enrich the ontology through user-oriented log extraction annotations, which can be flexibly used to provide different log-oriented views over the data. Different data access modes are then devised so as to view the legacy data through the lens of XES.
Original language | English |
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Title of host publication | Business Process Management Workshops : BPM 2015, 13th International Workshops, Innsbruck, Austria, August 31 - September 3, 2015 : Revised Papers |
Editors | M. Reichert, H.A. Reijers |
Publisher | Springer |
Pages | 140-153 |
Number of pages | 14 |
ISBN (Electronic) | 978-3-319-42887-1 |
ISBN (Print) | 9783319428864 |
DOIs | |
Publication status | Published - 2015 |
Event | 11th International Workshop on Business Process Intelligence (BPI 2015) - Innsbruck, Austria Duration: 31 Aug 2015 → 31 Aug 2015 Conference number: 11 https://www.win.tue.nl/bpi/doku.php?id=2015:start http://www.win.tue.nl/bpi/doku.php?id=2015:challenge&redirect=1id=2015/challenge |
Publication series
Name | Lecture Notes in Business Information Processing |
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Volume | 256 |
ISSN (Print) | 18651348 |
Workshop
Workshop | 11th International Workshop on Business Process Intelligence (BPI 2015) |
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Abbreviated title | BPI 2015 |
Country/Territory | Austria |
City | Innsbruck |
Period | 31/08/15 → 31/08/15 |
Internet address |
Keywords
- Event data
- Log extraction
- Multi-perspective process mining
- Ontology-based data access