A probabilistic-based approach to process model discovery

Malu Castellanos, Fabio Casati, Umeshwar Dayal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Process discovery is crucial for understanding how business operations are performed and how to improve them. The opportunity to discover process models exists given that many systems underlying the execution of process steps log their execution times. However, there are many challenges to discover the actual processes particularly complex ones and without making unrealistic assumptions. In this paper we present a novel probabilistic-based approach to discover high quality process models of any complexity. The approach has a series of steps to discover links between nodes corresponding to execution dependencies between tasks and at the end it ranks these links according to their probabilities of actually existing and classifies them according to their type. In this paper we formulate the process discovery problem, describe the challenges and describe our solution.

Original languageEnglish
Title of host publicationICDE Workshops 2011 - 2011 IEEE 27th International Conference on Data Engineering Workshops
Pages232-237
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011 - Hannover, Germany
Duration: 11 Apr 201116 Apr 2011

Conference

Conference2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011
CountryGermany
CityHannover
Period11.4.1116.4.11

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Castellanos, M., Casati, F., & Dayal, U. (2011). A probabilistic-based approach to process model discovery. In ICDE Workshops 2011 - 2011 IEEE 27th International Conference on Data Engineering Workshops (pp. 232-237). [5767637] https://doi.org/10.1109/ICDEW.2011.5767637