Efficient and Customisable Declarative Process Mining with SQL

Stefan Schönig, Andreas Solti, Cristina Cabanillas Macias, Stefan Jablonski, Jan Mendling

Publication: Chapter in book/Conference proceedingContribution to conference proceedings


Flexible business processes can often be modelled more easily using a declarative rather than a procedural modelling approach. Process mining aims at automating the discovery of business process models. Existing declarative process mining approaches either suffer from performance issues with real-life event logs or limit their expressiveness to a specific set of constraint types. Lately, RelationalXES, a relational database architecture for storing event log data, has been introduced. In this paper, we introduce a mining approach that directly works on relational event data by querying the log with conventional SQL. By leveraging database performance technology, the mining procedure is fast without limiting itself to detecting certain control-flow constraints. Queries can be customised and cover process perspectives beyond control flow, e.g., organisational aspects. We evaluated the performance and the capabilities of our approach with regard to several real-life event logs.
Original languageEnglish
Title of host publication28th International Conference on Advanced Information Systems Engineering (CAiSE)
Editors Selmin Nurcan, Pnina Soffer, Marko Bajec, Johann Eder
Place of PublicationLjubljana, Slovenia
PublisherSpringer International Publishing
Pages290 - 305
Publication statusPublished - 2016

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 502050 Business informatics

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