The onprom Toolchain for Extracting Business Process Logs using Ontology-based Data Access

Diego Calvanese, Tahir Emre Kalayci, Marco Montali, and Ario Santoso

Proc. of the BPM Demo Track and BPM Dissertation Award, co-located with 15th Int. Conf. on Business Process Management (BPM 2017). Volume 1920 of CEUR Workshop Proceedings, http://ceur-ws.org/. 2017.

Process mining techniques require the input data to be explicitly structured in the form of an event log. Unfortunately, in many real world settings, such event logs are not explicitly given, but they are implicitly stored in legacy information systems. Therefore, to enable process mining, there is a need to support the data preparation and the log extraction from legacy information systems. The tool-chain aims at supporting users in the semi-automatic extraction of event logs from a legacy information system, reflecting different process-related views on the same data, and consequently facilitating multi-perspective process mining. The tool-chain is based on the ontology-based data access paradigm, and consists of three components, namely UML editor, annotation editor, and log extractor. Each component can be used both as a plug-in for the extensible process mining framework ProM, or within an integrated toolkit. The produced logs are fully compliant with the XES standard.


@inproceedings{BPM-demo-2017,
   title = "The onprom Toolchain for Extracting Business Process Logs using
Ontology-based Data Access",
   year = "2017",
  author = "Diego Calvanese and Kalayci, Tahir Emre and Marco Montali and
Ario Santoso",
   booktitle = "Proc. of the BPM Demo Track and BPM Dissertation Award,
co-located with 15th Int. Conf. on Business Process Management
(BPM 2017)",
   volume = "1920",
   series = "CEUR Workshop Proceedings, http://ceur-ws.org/",
}
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