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F. Giannotti, Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., and Trasarti, R., “Unveiling the complexity of human mobility by querying and mining massive trajectory data”, VLDB J., vol. 20, pp. 695-719, 2011.
F. Giannotti, Manco, G., Pedreschi, D., and Turini, F., “Experiences with a Logic-Based Knowledge Discovery Support Environment”, in AI*IA, 1999, pp. 202-213.
F. Giannotti and Hermenegildo, M. V., “A Technique for Recursive Invariance Detection and Selective Program Specification”, in PLILP, 1991, pp. 323-334.
F. Giannotti, Manco, G., Pedreschi, D., and Turini, F., “Experiences with a Logic-based knowledge discovery Support Environment”, in 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 1999.
F. Giannotti, Pedreschi, D., Saccà, D., and Zaniolo, C., “Non-Determinism in Deductive Databases”, in DOOD, 1991, pp. 129-146.
F. Giannotti and Manco, G., “Making Knowledge Extraction and Reasoning Closer”, in PAKDD, 2000, pp. 360-371.
F. Giannotti and Latella, D., “Using Abstract Interpretation for Gate splitting in LOTOS Specifications”, in WSA, 1992, pp. 194-204.
F. Giannotti, Mazzoni, A., Puntoni, S., and Renso, C., “Synthetic generation of cellular network positioning data”, in GIS, 2005, pp. 12-20.
F. Giannotti, Gabrielli, L., Pedreschi, D., and Rinzivillo, S., “Understanding human mobility with big data”, in Solving Large Scale Learning Tasks. Challenges and Algorithms, Springer International Publishing, 2016, pp. 208–220.
F. Giannotti and Manco, G., “Declarative Knowledge Extraction with Interactive User-Defined Aggregates”, in FQAS, 2000, pp. 435-444.
F. Giannotti, Nanni, M., and Pedreschi, D., “Logic-Based Knowledge Discovery in Databases”, in EJC, 2000, pp. 279-283.
P. Gravino, Caminiti, S., Sirbu, A., Tria, F., Servedio, V. D. P., and Loreto, V., “Unveiling Political Opinion Structures with a Web-experiment”, in Proceedings of the 1st International Conference on Complex Information Systems, 2016.
P. Gravino, Sirbu, A., Becker, M., Servedio, V. D. P., and Loreto, V., “Experimental Assessment of the Emergence of Awareness and Its Influence on Behavioral Changes: The Everyaware Lesson”, in Participatory Sensing, Opinions and Collective Awareness, Springer, 2017, pp. 337–362.
V. Grossi, Romei, A., and Ruggieri, S., “A Case Study in Sequential Pattern Mining for IT-Operational Risk”, in ECML/PKDD (1), 2008, pp. 424-439.
V. Grossi, Guns, T., Monreale, A., Nanni, M., and Nijssen, S., “Partition-Based Clustering Using Constraint Optimization”, in Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach, Springer International Publishing, 2016, pp. 282–299.
V. Grossi, Monreale, A., Nanni, M., Pedreschi, D., and Turini, F., “Clustering Formulation Using Constraint Optimization”, in Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers, 2015.
V. Grossi, Pedreschi, D., and Turini, F., “Data Mining and Constraints: An Overview”, in Data Mining and Constraint Programming, Springer International Publishing, 2016, pp. 25–48.
V. Grossi, Romei, A., and Turini, F., “Survey on using constraints in data mining”, Data Mining and Knowledge Discovery, vol. 31, pp. 424–464, 2017.
B. Guidi, Michienzi, A., and Rossetti, G., “Towards the dynamic community discovery in decentralized online social networks”, Journal of Grid Computing, vol. 17, pp. 23–44, 2019.
B. Guidi, Michienzi, A., and Rossetti, G., “Dynamic community analysis in decentralized online social networks”, in European Conference on Parallel Processing, 2017.
R. Guidotti, Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., and Turini, F., “Factual and Counterfactual Explanations for Black Box Decision Making”, IEEE Intelligent Systems, 2019.
R. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., “Market Basket Prediction using User-Centric Temporal Annotated Recurring Sequences”, in 2017 IEEE International Conference on Data Mining (ICDM), 2017.
R. Guidotti, Soldani, J., Neri, D., Brogi, A., and Pedreschi, D., “Helping your docker images to spread based on explainable models”, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2018.
R. Guidotti, Monreale, A., Matwin, S., and Pedreschi, D., “Black Box Explanation by Learning Image Exemplars in the Latent Feature Space”, in Machine Learning and Knowledge Discovery in Databases, Cham, 2020.
R. Guidotti, Monreale, A., Rinzivillo, S., Pedreschi, D., and Giannotti, F., “Unveiling mobility complexity through complex network analysis”, Social Network Analysis and Mining, vol. 6, p. 59, 2016.

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