Liquid Business Process Model Collections

van der Aalst, Wil M.P., La Rosa, Marcello, ter Hofstede, Arthur H.M., & Wynn, Moe T. (2014) Liquid Business Process Model Collections. In Gianni, Daniele, D'Ambrogio, Andrea, & Tolk, Andreas (Eds.) Modeling and Simulation-Based Systems Engineering Handbook. Taylor & Frances Group, Boca Raton, pp. 401-424.

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Abstract

Many organizations realize that increasing amounts of data (“Big Data”) need to be dealt with intelligently in order to compete with other organizations in terms of efficiency, speed and services. The goal is not to collect as much data as possible, but to turn event data into valuable insights that can be used to improve business processes. However, data-oriented analysis approaches fail to relate event data to process models. At the same time, large organizations are generating piles of process models that are disconnected from the real processes and information systems. In this chapter we propose to manage large collections of process models and event data in an integrated manner. Observed and modeled behavior need to be continuously compared and aligned. This results in a “liquid” business process model collection, i.e. a collection of process models that is in sync with the actual organizational behavior. The collection should self-adapt to evolving organizational behavior and incorporate relevant execution data (e.g. process performance and resource utilization) extracted from the logs, thereby allowing insightful reports to be produced from factual organizational data.

Impact and interest:

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ID Code: 83640
Item Type: Book Chapter
Keywords: process model collection, process mining, concept drift, predictive analytics, process model, automated process discovery, business process management
ISBN: 9781466571457
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 by Taylor & Francis Group, LLC
Deposited On: 16 Apr 2015 02:35
Last Modified: 29 Apr 2016 12:57

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