Discovering Process Model from Event Logs by Considering Overlapping Rules

Yutika Amelia Effendi, Riyanarto Sarno

Abstract


Process Mining is a technique to automatically discover and analyze business processes from event logs. Discovering concurrent activities often uses process mining since there are many of them contained in business processes. Since researchers and practitioners are giving attention to the process discovery (one of process mining techniques), then the best result of  the  discovered process  models is  a  must. Nowadays, using process  execution  data in the  past, process  models with rules underlying decisions in processes can be enriched, called decision mining. Rules defined over process data specify choices between multiple activities. One out of multiple activities is allowed to be executed in existing decision mining methods or it is known as mutually-exclusive rules. Not only mutually-exclusive rules, but also fully deterministic because all factors which influence decisions are recorded. However, because of non-determinism or incomplete   information,   there   are   some   cases   that   are overlapping  in  process  model.  Moreover,  the  rules  which are generated  from  existing  method  are  not  suitable  with  the recorded data. In this paper, a discovery technique for process model with data by considering the overlapping rules from event logs is presented. Discovering overlapping rules uses decision tree learning techniques, which fit the recorded data better than the existing method. Process model discovery from event logs is generated using Modified Time-Based Heuristics Miner Algorithm. Last, online book store management process model is presented in High-level BPMN Process Model.

Keywords


Decision Mining; Process Mining; Overlapping Rules; Process Discovery; BPMN; Petri Net; Modified Time-based Heuristics Miner;

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