Abstract
Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. Through process mining, decision makers can discover process models from data, compare expected and actual behaviors, and enrich models with key information about their actual execution. To be applicable, process mining techniques require the input data to be explicitly structured in the form of an event log, which lists when and by whom different case objects (i.e., process instances) have been subject to the execution of tasks. Unfortunately, in many real world set-ups, such event logs are not explicitly given, but are instead implicitly represented in legacy information systems. To apply process mining in this widespread setting, there is a pressing need for techniques able to support various process stakeholders in data preparation and log extraction from legacy information systems. The purpose of this paper is to single out this challenging, open issue, and didactically introduce how techniques from intelligent data management, and in particular ontology-based data access, provide a viable solution with a solid theoretical basis.
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Notes
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Small and medium-sized enterprises.
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Enterprise Resource Planning.
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Customer Relationship Management.
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Supply Chain Management.
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We consider here the case of an information system consisting of a single relational data source. Multiple data sources can be wrapped by a federation tool and presented as a single source.
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In \(\textit{DL-Lite}_{\mathcal {{A}}}\), features are actually called attributes. Here we use the term “feature” to avoid confusion with attributes of UML (see later).
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See http://www.omg.org/spec/UML/2.5/ for the latest version of UML at the moment of writing.
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If the roles of the association are not specified in the UML class diagram, we may use arbitrary fresh DL role names, each of which is identified by the name of the association and the component.
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The formal counterpart of such an SQL query is a first-order logic (FOL) query with distinguished variables \(\vec {x}\).
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Acknowledgements
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 (ONtology-driven PROcess Mining)”. We thank 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., Santoso, A. (2017). OBDA for Log Extraction in Process Mining. In: Ianni, G., et al. Reasoning Web. Semantic Interoperability on the Web. Reasoning Web 2017. Lecture Notes in Computer Science(), vol 10370. Springer, Cham. https://doi.org/10.1007/978-3-319-61033-7_9
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