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Case and Activity Identification for Mining Process Models from Middleware

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.
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https://hal.inria.fr/hal-02156457
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Submitted on : Friday, June 14, 2019 - 1:48:16 PM
Last modification on : Saturday, June 15, 2019 - 1:21:46 AM

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Saimir Bala, Jan Mendling, Martin Schimak, Peter Queteschiner. Case and Activity Identification for Mining Process Models from Middleware. 11th IFIP Working Conference on The Practice of Enterprise Modeling (PoEM), Oct 2018, Vienna, Austria. pp.86-102, ⟨10.1007/978-3-030-02302-7_6⟩. ⟨hal-02156457⟩

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