Extensions, Analysis and Experimental Assessment of a Probabilistic Ensemble-learning Framework for Detecting Deviances in Business Process Instances

Alfredo Cuzzocrea, Francesco Folino, Massimo Guarascio, Luigi Pontieri

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 Harvard Style

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, pages 162-173. DOI: 10.5220/0006340001620173


in Bibtex Style

@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},
}


in EndNote Style

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
AU - Cuzzocrea A.
AU - Folino F.
AU - Guarascio M.
AU - Pontieri L.
PY - 2017
SP - 162
EP - 173
DO - 10.5220/0006340001620173