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Learning Business Rules for Adaptive Process Models

Authors:
Hans Friedrich Witschel
Tuan Quoc Nguyen
Knut Hinkelmann

Keywords: Business process intelligence, process mining, knowledge work, workflow management

Abstract:
This work presents a new approach to handling knowledge-intensive business processes in an adaptive, flexible and accurate way. We propose to support processes by executing a process skeleton, consisting of the most important recurring activities of the process, through a workflow engine. This skeleton should be kept simple. The corresponding workflow is complemented by two features: firstly, a task management tool through which workflow tasks are delivered and that give human executors flexibility and freedom to adapt tasks by adding subtasks and resources as required by the context. And secondly, a component that learns business rules from the log files of this task management and that will predict subtasks and resources on the basis of knowledge from previous executions. We present supervised and unsupervised approaches for rule learning and evaluate both on a real business process with 61 instances. Results are promising, showing that meaningful rules can be learned even from this comparatively small data set.

Pages: 8 to 13

Copyright: Copyright (c) IARIA, 2012

Publication date: July 22, 2012

Published in: conference

ISSN: 2308-4391

ISBN: 978-1-61208-223-3

Location: Nice, France

Dates: from July 22, 2012 to July 27, 2012