Journal article

Automated discovery of declarative process models with correlated data conditions

V Leno, M Dumas, FM Maggi, M La Rosa, A Polyvyanyy

Information Systems | Elsevier | Published : 2020

Abstract

Automated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems. Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation). When dealing with highly variable processes, the resulting procedural models are often too complex to be practically usable. An alternative approach is to discover declarative process models, which represent the behavior of the process as a set of constraints. Declarative process discovery techniques have been shown to produce simpler models than procedural ones, particularly for processes with high variability. However, the bulk of approaches..

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Grants

Awarded by Australian Research Council


Awarded by Estonian Research Council


Funding Acknowledgements

This work is partly supported by the Estonian Research Council (IUT20-55) and by the Australian Research Council (DP180102839).