Configuring SQL-based process mining for performance and storage optimisation

Schönig Stefan, Claudio Di Ciccio, Jan Mendling

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

Process mining is the area of research that embraces the automated discovery, conformance checking and enhancement of process models. Declarative process mining approaches offer capabilities to automatically discover models of flexible processes from event logs. However, they often suffer from performance issues with real-life event logs, especially when constraints to be discovered go beyond a standard repertoire of templates. By leveraging relational database performance technology, a new approach based on SQL querying has been recently introduced, to improve performance though still keeping the nature of discovered constraints customisable. In this paper, we provide an in-depth analysis of configuration parameters that allow for a speed-up of the answering time and a decrease of storage space needed for query processing. Thereupon, we provide configuration recommendations for process mining with SQL on relational databases.
Original languageEnglish
Title of host publicationProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC 2019
Editors Chih-Cheng Hung and George A. Papadopoulos
Place of PublicationLimassol, Cyprus
PublisherACM Press
Pages94 - 97
DOIs
Publication statusPublished - 2019

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 102
  • 502
  • 502050 Business informatics

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