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Authors: Alfredo Cuzzocrea 1 ; Francesco Folino 2 ; Massimo Guarascio 2 and Luigi Pontieri 2

Affiliations: 1 ICAR-CNR and University of Trieste, Italy ; 2 ICAR-CNR, Italy

Keyword(s): Business Process Intelligence, Classification, Deviance Detection.

Related Ontology Subjects/Areas/Topics: Coupling and Integrating Heterogeneous Data Sources ; Databases and Information Systems Integration ; Enterprise Information Systems

Abstract: This paper significantly extends a previous proposal where an innovative ensemble-learning framework for mining business process deviances that exploits multi-view learning has been provided. Here, we introduce some relevant contributions: (i) a further learning method that extends and refines the previous methods via introducing the idea of probabilistically combining different deviance detection models (DDMs); (ii) a complete conceptual architecture that implements the extended multi-view ensemble-learning framework; (iii) a wide and comprehensive experimental assessment of the framework, even in comparison with existent competitors. The investigated scientific context falls in the so-called Business Process Intelligence (BPI) research area, which is relevant for a wide number of real-life applications. These novel contributions clearly confirm the flexibility, the reliability and the effectiveness of the general deviance detection framework, respectively.

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Paper citation in several formats:
Cuzzocrea, A.; Folino, F.; Guarascio, M. and Pontieri, L. (2017). Extensions, Analysis and Experimental Assessment of a Probabilistic Ensemble-learning Framework for Detecting Deviances in Business Process Instances. In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-247-9; ISSN 2184-4992, SciTePress, pages 162-173. DOI: 10.5220/0006340001620173

@conference{iceis17,
author={Alfredo Cuzzocrea. and Francesco Folino. and Massimo Guarascio. and Luigi Pontieri.},
title={Extensions, Analysis and Experimental Assessment of a Probabilistic Ensemble-learning Framework for Detecting Deviances in Business Process Instances},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2017},
pages={162-173},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006340001620173},
isbn={978-989-758-247-9},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Extensions, Analysis and Experimental Assessment of a Probabilistic Ensemble-learning Framework for Detecting Deviances in Business Process Instances
SN - 978-989-758-247-9
IS - 2184-4992
AU - Cuzzocrea, A.
AU - Folino, F.
AU - Guarascio, M.
AU - Pontieri, L.
PY - 2017
SP - 162
EP - 173
DO - 10.5220/0006340001620173
PB - SciTePress

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