Andrea Burattin
Associate Professor
Technical University of Denmark
Discovering high-level BPMN process models from event data
A. Kalenkova, A. Burattin, M. de Leoni, W. van der Aalst, A. Sperduti
Abstract
Purpose
The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias.
Design/methodology/approach
The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs.
Findings
This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition.
Originality/value
The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.
Paper Information and Files
In Business Process Management Journal, vol. 25 (2019), no. 5, pp. 995-1019.
- DOI: 10.1108/BPMJ-02-2018-0051
- BibTeX: see at DBLP
- Download text
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