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Authors: Francesco Folino 1 ; Gianluigi Greco 2 ; Antonella Guzzo 3 and Luigi Pontieri 1

Affiliations: 1 ICAR-CNR, Italy ; 2 UNICAL, Italy ; 3 DEIS, UNICAL, Italy

ISBN: 978-989-8111-37-1

ISSN: 2184-4992

Keyword(s): Business Process Intelligence, Process Mining, Decision Trees.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Business Analytics ; Business Process Management ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; e-Business ; Enterprise Engineering ; Enterprise Information Systems ; Health Information Systems ; Industrial Applications of Artificial Intelligence ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: 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 cor relations 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. (More)

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Paper citation in several formats:
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 6: ICEIS, ISBN 978-989-8111-37-1, pages 70-77. DOI: 10.5220/0001705700700077

@conference{iceis08,
author={Francesco Folino and Gianluigi Greco and Antonella Guzzo and Luigi Pontieri},
title={DISCOVERING MULTI-PERSPECTIVE PROCESS MODELS},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 6: ICEIS,},
year={2008},
pages={70-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001705700700077},
isbn={978-989-8111-37-1},
}

TY - CONF

JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 6: ICEIS,
TI - DISCOVERING MULTI-PERSPECTIVE PROCESS MODELS
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

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