| 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 | 132 | 2012 |
| Mining predictive process models out of low-level multidimensional logs F Folino, M Guarascio, L Pontieri Advanced Information Systems Engineering: 26th International Conference …, 2014 | 44 | 2014 |
| Mining multi-variant process models from low-level logs F Folino, M Guarascio, L Pontieri Business Information Systems: 18th International Conference, BIS 2015 …, 2015 | 24 | 2015 |
| 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 | 24 | 2014 |
| 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 | 23 | 2021 |
| 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 | 22 | 2013 |
| 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 | 21 | 2016 |
| 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 | 20 | 2019 |
| 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 | 20 | 2016 |
| 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 | 19 | 2020 |
| 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 | 19 | 2013 |
| 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 | 19 | 2009 |
| 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 | 15 | 2013 |
| Deep learning M Guarascio, G Manco, E Ritacco Academic Press, 2019 | 14 | 2019 |
| 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 | 14 | 2016 |
| 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 | 14 | 2015 |
| Rule learning with probabilistic smoothing G Costa, M Guarascio, G Manco, R Ortale, E Ritacco Data Warehousing and Knowledge Discovery: 11th International Conference …, 2009 | 13 | 2009 |
| Knowledge discovery in databases M Guarascio, G Manco, E Ritacco Encyclopedia of Bioinformatics and Computational Biology: ABC of …, 2018 | 11 | 2018 |
| 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 | 8 | 2017 |
luigi pontieriResearcher, CNR, ICARVerified email at icar.cnr.it
