Many companies have adopted Process-aware Information Systems (PAIS) for supporting their business processes in some form. These systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. Proper analysis of PAIS execution logs can yield important knowledge and help organizations improve the quality of their services. Starting from a process model as it is possible to discover 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 discuss further challenges
related to this approach. To verify the presented ideas a Decision Miner has been implemented within the ProM framework.
|Place of Publication||Eindhoven|
|Publisher||Technische Universiteit Eindhoven|
|Number of pages||16|
|Publication status||Published - 2006|
|Name||BETA publicatie : working papers|