Case and Activity Identification for Mining Process Models from Middleware

Saimir Bala, Jan Mendling, Martin Schimak, Peter Queteschiner

Publikation: Beitrag in Buch/KonferenzbandBeitrag in Konferenzband

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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.
OriginalspracheEnglisch
Titel des SammelwerksThe Practice of Enterprise Modeling
Herausgeber*innen Robert Andrei Buchmann, Dimitris Karagiannis, Marite Kirikova
ErscheinungsortPoEM, Vienna
VerlagSpringer Cham
Seiten86 - 102
ISBN (Print)978-3-030-02302-7
DOIs
PublikationsstatusVeröffentlicht - 2018

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

  • 102022 Softwareentwicklung
  • 102
  • 102015 Informationssysteme
  • 502050 Wirtschaftsinformatik

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