Diego Calvanese, Tahir Emre Kalayci, Marco Montali, Ario Santoso. The onprom Toolchain for Extracting Business Process Logs using Ontology-based Data Access. In Robert Clarisó, Henrik Leopold, Jan Mendling, Wil M. P. van der Aalst, Akhil Kumar 0001, Brian T. Pentland, Mathias Weske, editors, Proceedings of the BPM Demo Track and BPM Dissertation Award co-located with 15th International Conference on Business Process Modeling (BPM 2017), Barcelona, Spain, September 13, 2017. Volume 1920 of CEUR Workshop Proceedings, CEUR-WS.org, 2017. [doi]
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 onprom 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.