Case and Activity Identification for Mining Process Models from Middleware

Saimir Bala, Jan Mendling, Martin Schimak, Peter Queteschiner

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider.
Original languageEnglish
Title of host publicationThe Practice of Enterprise Modeling
Editors Robert Andrei Buchmann, Dimitris Karagiannis, Marite Kirikova
Place of PublicationPoEM, Vienna
PublisherSpringer, Cham
Pages86 - 102
ISBN (Print)978-3-030-02302-7
DOIs
Publication statusPublished - 2018

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 102 not use (legacy)
  • 102015 Information systems
  • 502050 Business informatics

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