Follow
Massimo Guarascio
Massimo Guarascio
Researcher, Institute for high performance computing and networking (ICAR-CNR)
Verified email at icar.cnr.it - Homepage
Title
Cited by
Cited by
Year
Discovering context-aware models for predicting business process performances
F Folino, M Guarascio, L Pontieri
On the Move to Meaningful Internet Systems: OTM 2012: Confederated …, 2012
1322012
Mining predictive process models out of low-level multidimensional logs
F Folino, M Guarascio, L Pontieri
Advanced Information Systems Engineering: 26th International Conference …, 2014
442014
Mining multi-variant process models from low-level logs
F Folino, M Guarascio, L Pontieri
Business Information Systems: 18th International Conference, BIS 2015 …, 2015
242015
A data-driven prediction framework for analyzing and monitoring business process performances
A Bevacqua, M Carnuccio, F Folino, M Guarascio, L Pontieri
Enterprise Information Systems: 15h International Conference, ICEIS 2013 …, 2014
242014
On learning effective ensembles of deep neural networks for intrusion detection
F Folino, G Folino, M Guarascio, FS Pisani, L Pontieri
Information Fusion 72, 48-69, 2021
232021
Discovering High-Level Performance Models for Ticket Resolution Processes: (Short Paper)
F Folino, M Guarascio, L Pontieri
On the Move to Meaningful Internet Systems: OTM 2013 Conferences …, 2013
222013
A cloud-based prediction framework for analyzing business process performances
E Cesario, F Folino, M Guarascio, L Pontieri
Availability, Reliability, and Security in Information Systems: IFIP WG 8.4 …, 2016
212016
Predictive monitoring of temporally-aggregated performance indicators of business processes against low-level streaming events
A Cuzzocrea, F Folino, M Guarascio, L Pontieri
Information Systems 81, 236-266, 2019
202019
A multi-view multi-dimensional ensemble learning approach to mining business process deviances
A Cuzzocrea, F Folino, M Guarascio, L Pontieri
2016 International Joint Conference on Neural Networks (IJCNN), 3809-3816, 2016
202016
A Prediction Framework for Proactively Monitoring Aggregate Process-Performance Indicators
F Francesco, M Guarascio, P Luigi
IEEE International Enterprise Distributed Object Computing Conference, EDOC …, 2015
20*2015
A Deep Learning Approach for Detecting Security Attacks on Blockchain
F Scicchitano, A Liguori, M Guarascio, E Ritacco, G Manco
ITASEC, 2020
192020
Context-aware predictions on business processes: an ensemble-based solution
F Folino, M Guarascio, L Pontieri
New Frontiers in Mining Complex Patterns: First International Workshop …, 2013
192013
High quality true-positive prediction for fiscal fraud detection
S Basta, F Fassetti, M Guarascio, G Manco, F Giannotti, D Pedreschi, ...
2009 IEEE International Conference on Data Mining Workshops, 7-12, 2009
192009
A Data-adaptive Trace Abstraction Approach to the Prediction of Business Process Performances
A Bevacqua, M Carnuccio, F Folino, M Guarascio, L Pontieri
ICEIS (1), 56-65, 2013
152013
Deep learning
M Guarascio, G Manco, E Ritacco
Academic Press, 2019
142019
A robust and versatile multi-view learning framework for the detection of deviant business process instances
A Cuzzocrea, F Folino, M Guarascio, L Pontieri
International Journal of Cooperative Information Systems 25 (04), 1740003, 2016
142016
A multi-view learning approach to the discovery of deviant process instances
A Cuzzocrea, F Folino, M Guarascio, L Pontieri
On the Move to Meaningful Internet Systems: OTM 2015 Conferences …, 2015
142015
Rule learning with probabilistic smoothing
G Costa, M Guarascio, G Manco, R Ortale, E Ritacco
Data Warehousing and Knowledge Discovery: 11th International Conference …, 2009
132009
Knowledge discovery in databases
M Guarascio, G Manco, E Ritacco
Encyclopedia of Bioinformatics and Computational Biology: ABC of …, 2018
112018
A descriptive clustering approach to the analysis of quantitative business-process deviances
F Folino, M Guarascio, L Pontieri
Proceedings of the Symposium on Applied Computing, 765-770, 2017
82017
The system can't perform the operation now. Try again later.
Articles 1–20