Heuristic mining: Adaptive process simplification in education

• Published in: 2012 Tenth International Conference on ICT and Knowledge Engineering pp. 221 - 227
• Main Authors: Ayutaya, Nutchar Senewong Na, Palungsuntikul, P., Premchaiswadi, W.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2012
• Subjects: Business; causal dependency relationship; Data mining; Educational institutions; Heuristic algorithms; Heuristics Miner plugin; Noise; Process control; Process Mining; PROM; students' registration process; workflow model
• ISBN: 1467323160; 9781467323161
• ISSN: 2157-0981; 2157-099X
• DOI: 10.1109/ICTKE.2012.6408559

Process mining techniques allow for extracting information from event logs. For example, the audit trails of a workflow management system or the transaction logs of an enterprise resource planning system can be used to discover models describing processes, organizations, and products. ProM is a generic tool for implementing process mining tools in a standard environment. Prom aims at improving this by providing techniques and tools for discovering process, organizational, social, and performance information from event logs. In this paper we introduce the challenging process mining domain and discuss a heuristics driven process mining algorithm; the so-called "Heuristics Miner" in detail. Heuristics Miner is an applied applicable mining algorithm that can deal with noise, and can be used to express the main behavior registered in an event log. In the "case study" section of this paper, we used an students' registration event log consisted of 299 cases and 569 events. The data was received from one of the universities in Thailand. Finally, the HM algorithm focused on the control flow perspective and generated a process model in the form of a Heuristics Net for the given event log.