Case and Activity Identification for Mining Process Models from Middleware

Bala, Saimir and Mendling, Jan and Schimak, Martin and Queteschiner, Peter (2018) Case and Activity Identification for Mining Process Models from Middleware. In: The Practice of Enterprise Modeling. Springer, Cham, PoEM, Vienna. pp. 86-102. ISBN 978-3-030-02302-7


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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.

Item Type: Book Section
Additional Information: This work has been funded by the Austrian Research Promotion Agency (FFG) under grant 862950 (Business Process Optimization Toolkit).
Keywords: Business process management, Process monitoring, Process mining, Case identification
Divisions: Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft > Mendling
Version of the Document: Submitted
Variance from Published Version: Minor
Depositing User: Saimir Bala
Date Deposited: 02 Nov 2018 07:40
Last Modified: 16 Oct 2019 01:09
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