Abstract
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.
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Notes
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In W3C terminology, a profile is a sublanguage.
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It is important to notice that the possible absence of an actual value for Order_Date does not contrast with the class diagram of Fig. 2, which dictates that every purchase order has exactly one creation time. In fact, conceptual models are interpreted under incomplete information: the absence of the creation date for an order does not mean that the order has no creation date, but that such an order has a creation date that is not certainly known.
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In the left-hand side of a mapping, curly brackets are used to denote answer variables of the SQL query in the right-hand side.
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In OWL terms, it is a data property.
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Acknowledgement
This research has been partially supported by the Euregio IPN12 “KAOS: Knowledge-Aware Operational Support” project, which is funded by the “European Region Tyrol-South Tyrol-Trentino” (EGTC) under the first call for basic research projects and by the UNIBZ internal project “OnProm”. We thank Ario Santoso for the development of the log extraction plug-in of onprom, and Wil van der Aalst for the interesting discussions and insights on the problem of extracting event logs from legacy information systems.
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Calvanese, D., Kalayci, T.E., Montali, M., Tinella, S. (2017). Ontology-Based Data Access for Extracting Event Logs from Legacy Data: The onprom Tool and Methodology. In: Abramowicz, W. (eds) Business Information Systems. BIS 2017. Lecture Notes in Business Information Processing, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-319-59336-4_16
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