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An experimental mining and analytics for discovering proportional process patterns from workflow enactment event logs

  • Kyoungsook Kim
  • Young-Koo Lee
  • Hyun Ahn
  • Kwanghoon Pio KimEmail author
Article
  • 39 Downloads

Abstract

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.

Keywords

Workflow process discovery Proportional process patterns Experimental analytics 

Notes

Acknowledgements

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringKyunghee UniversityYongin-siRepublic of Korea
  2. 2.Division of Computer Science and EngineeringKyonggi UniversitySuwon-siRepublic of Korea

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