An experimental mining and analytics for discovering proportional process patterns from workflow enactment event logs
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In this paper, we carry out an experimental analytics to show how much perfectly the conceptual mining framework is operable on re-discovering workflow process patterns and their enacted proportions from the workflow enactment event histories logged in a format of XES standardized schema. In principle, the framework must be able to properly handle all the workflow process patterns based upon the four types of control-flow primitives such as linear (sequential), disjunctive (selective), conjunctive (parallel), and loop (iterative) process patterns. The paper focuses on implementing an algorithmic mining framework only for discovering all the process patterns and their enacted proportions. To prove the functional correctness of the framework, we carry out an experimental mining and analytics on the real workflow instance enactment event histories of 10,000 workcases, and we finally visualize the mining and analytic artifacts and describe the implications of the results of the experiment.
KeywordsWorkflow process discovery Proportional process patterns Experimental analytics
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (Grant No. 2017R1A2B2010697).
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