Artikel in Tagungsband INPROC-2014-28

Gröger, Christoph; Schwarz, Holger; Mitschang, Bernhard: Prescriptive Analytics for Recommendation-based Business Process Optimization.
In: Abramowicz, Witold (Hrsg); Kokkinaki, Angelika (Hrsg): Proceedings of the 17th International Conference on Business Information Systems (BIS), 22-23 May, 2014, Larnaca, Cyprus.
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik.
176, S. 25-37, englisch.
Springer, Mai 2014.
Artikel in Tagungsband (Konferenz-Beitrag).
CR-Klassif.H.2.8 (Database Applications)
KeywordsPrescriptive Analytics, Process Optimization, Process Warehouse, Data Mining, Business Intelligence, Decision Support

Continuously improved business processes are a central success factor for companies. Yet, existing data analytics do not fully exploit the data generated during process execution. Particularly, they miss prescriptive techniques to transform analysis results into improvement actions. In this paper, we present the data-mining-driven concept of recommendation-based business process op-timization on top of a holistic process warehouse. It prescriptively generates ac-tion recommendations during process execution to avoid a predicted metric de-viation. We discuss data mining techniques and data structures for real-time prediction and recommendation generation and present a proof of concept based on a prototypical implementation in manufacturing.

Abteilung(en)Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Anwendersoftware
Eingabedatum15. April 2014
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