loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.116.118.244

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic