Process Mining Based on Statistic Ordering Relations of Events

Abstract:

An explicit process model is vital in business processes. However, it is complicated and time consuming to create a workflow design. Also discords usually occur between the perceived management processes and the actual workflow processes. Under this condition, the process discovery techniques emerge. The aim is to rebuild a workflow model (e.g. a Petri net) of a business process based on the execution log. The model should give an abstract representation of the system and reproduce the log. This model can be further applied for process redesign/improvement and performance/reliability evaluation. In this paper, we present a new algorithm derived from α-algorithm for process discovery in term of Petri nets, where statistic long distance causal relationship is taken into consideration. Also this algorithm covers some shortages in α-algorithm.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1959-1966
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1959
• Online since: September 2013
• Authors: Huan Zhou, Chuang Lin, Yi Ping Deng
• Keywords: Petri Net, Process Mining, Statistical Casual Relationship, Workflow Mining

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