In: Lecture Notes in Computer Science : Business Process Management, Volume 4102, 2006, pages 420-425. 2006. URL: http://dx.doi.org/10.1007/1184176033.
Abstract: Process-aware Information Systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. Proper analysis of these execution logs can yield important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions. Decision mining, also referred to as decision point analysis, aims at the detection of data dependencies that affect the routing of a case. In this paper we describe how machine learning techniques can be leveraged for this purpose, and we present a Decision Miner implemented within the ProM framework.
Keywords: Business Process Intelligence; Process Mining; Petri Nets; Decision Trees.
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