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
OTM Confederated International Conferences" On the Move to Meaningful …, 2012
972012
Mining predictive process models out of low-level multidimensional logs
F Folino, M Guarascio, L Pontieri
International conference on advanced information systems engineering, 533-547, 2014
312014
Discovering high-level performance models for ticket resolution processes
F Folino, M Guarascio, L Pontieri
OTM Confederated International Conferences" On the Move to Meaningful …, 2013
172013
A data-driven prediction framework for analyzing and monitoring business process performances
A Bevacqua, M Carnuccio, F Folino, M Guarascio, L Pontieri
International Conference on Enterprise Information Systems, 100-117, 2013
162013
Mining multi-variant process models from low-level logs
F Folino, M Guarascio, L Pontieri
International Conference on Business Information Systems, 165-177, 2015
152015
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
152009
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
13*2015
Context-aware predictions on business processes: an ensemble-based solution
F Folino, M Guarascio, L Pontieri
International Workshop on New Frontiers in Mining Complex Patterns, 215-229, 2012
122012
Rule learning with probabilistic smoothing
G Costa, M Guarascio, G Manco, R Ortale, E Ritacco
International Conference on Data Warehousing and Knowledge Discovery, 428-440, 2009
122009
A cloud-based prediction framework for analyzing business process performances
E Cesario, F Folino, M Guarascio, L Pontieri
International Conference on Availability, Reliability, and Security, 63-80, 2016
112016
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
102019
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
102016
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
102016
A multi-view learning approach to the discovery of deviant process instances
A Cuzzocrea, F Folino, M Guarascio, L Pontieri
OTM Confederated International Conferences" On the Move to Meaningful …, 2015
102015
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
92013
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
52017
On the Discovery of Explainable and Accurate Behavioral Models for Complex Lowly-Structured Business Processes
F Folino, M Guarascio, L Pontieri
ICEIS (1) 1, 206-217, 2015
52015
A block mixture model for pattern discovery in preference data
N Barbieri, M Guarascio, G Manco
2010 IEEE International Conference on Data Mining Workshops, 1100-1107, 2010
52010
Adaptive Trace Abstraction Approach for Predicting Business Process Performances
A Bevacqua, M Carnuccio, F Folino, M Guarascio, L Pontieri
21st Italian Symposium on Advanced Database Systems, SEBD 2013, 437-444, 2013
42013
Deep Learning
M Guarascio, G Manco, E Ritacco
Academic Press, 2019
32019
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