Francesco Folino, Gianluigi Greco, Antonella Guzzo, Luigi Pontieri



Process Mining techniques exploit the information stored in the executions log of a process in order to extract some high-level process model, which can be used for both analysis and design tasks. Most of these techniques focus on “structural” (control-flow oriented) aspects of the process, in that they only consider what elementary activities were executed and in which ordering. In this way, any other “non-structural” information, usually kept in real log systems (e.g., activity executors, parameter values, and time-stamps), is completely disregarded, yet being a potential source of knowledge. In this paper, we overcome this limitation by proposing a novel approach for discovering process models, where the behavior of a process is characterized from both structural and non-structural viewpoints. In a nutshell, different variants of the process (classes) are recognized through a structural clustering approach, and represented with a collection of specific workflow models. Relevant correlations between these classes and non-structural properties are made explicit through a rule-based classification model, which can be exploited for both explanation and prediction purposes. Results on real-life application scenario evidence that the discovered models are often very accurate and capture important knowledge on the process behavior.


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Paper Citation

in Harvard Style

Folino F., Greco G., Guzzo A. and Pontieri L. (2008). DISCOVERING MULTI-PERSPECTIVE PROCESS MODELS . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 70-77. DOI: 10.5220/0001705700700077

in Bibtex Style

author={Francesco Folino and Gianluigi Greco and Antonella Guzzo and Luigi Pontieri},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 978-989-8111-37-1
AU - Folino F.
AU - Greco G.
AU - Guzzo A.
AU - Pontieri L.
PY - 2008
SP - 70
EP - 77
DO - 10.5220/0001705700700077