A probabilistic-based approach to process model discovery

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 language	English
Title of host publication	ICDE Workshops 2011 - 2011 IEEE 27th International Conference on Data Engineering Workshops
Pages	232-237
Number of pages	6
DOIs	https://doi.org/10.1109/ICDEW.2011.5767637
Publication status	Published - 2011
Externally published	Yes
Event	2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011 - Hannover, Germany Duration: 11 Apr 2011 → 16 Apr 2011

Conference

Conference	2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011
Country	Germany
City	Hannover
Period	11.4.11 → 16.4.11

ASJC Scopus subject areas

Software
Signal Processing
Information Systems

Access to Document

10.1109/ICDEW.2011.5767637

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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., 5767637, pp. 232-237, 2011 IEEE 27th International Conference on Data Engineering Workshops, ICDE 2011, Hannover, Germany, 11.4.11. https://doi.org/10.1109/ICDEW.2011.5767637

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