Diego Calvanese, Tahir Emre Kalayci, Marco Montali, Stefano Tinella. Ontology-Based Data Access for Extracting Event Logs from Legacy Data: The onprom Tool and Methodology. In Witold Abramowicz, editor, Business Information Systems - 20th International Conference, BIS 2017, Poznan, Poland, June 28-30, 2017, Proceedings. Volume 288 of Lecture Notes in Business Information Processing, pages 220-236, Springer, 2017. [doi]
Process mining aims at discovering, monitoring, and improving business processes by extracting knowledge from event logs. In this respect, process mining can be applied only if there are proper event logs that are compatible with accepted standards, such as extensible event stream (XES). Unfortunately, in many real world set-ups, such event logs are not explicitly given, but instead are implicitly represented in legacy information systems. In this work, we exploit a framework and associated methodology for the extraction of XES event logs from relational data sources that we have recently introduced. Our approach is based on describing logs by means of suitable annotations of a conceptual model of the available data, and builds on the ontology-based data access (OBDA) paradigm for the actual log extraction. Making use of a real-world case study in the services domain, we compare our novel approach with a more traditional extract-transform-load based one, and are able to illustrate its added value. We also present a set of tools that we have developed and that support the OBDA-based log extraction framework. The tools are integrated as plugins of the ProM process mining suite.