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F. Giannotti, Gozzi, C., and Manco, G., “Characterizing Web User Accesses: A Transactional Approach to Web Log Clustering”, in ITCC, 2002, p. 312.
F. Giannotti, Greco, S., Saccà, D., and Zaniolo, C., “Programming with Non-Determinism in Deductive Databases”, Ann. Math. Artif. Intell., vol. 19, pp. 97-125, 1997.
F. Giannotti, Nanni, M., Pedreschi, D., and Samaritani, F., “WebCat: Automatic Categorization of Web Search Results”, in SEBD, 2003, pp. 507-518.
F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., “Datalog++: a Basis for Active Object.Oriented Databases”, in SEBD, 1997, pp. 325-340.
F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., “Datalog++: A Basis for Active Object-Oriented Databases”, in DOOD, 1997, pp. 283-301.
F. Giannotti, Manco, G., and Wijsen, J., “Logical Languages for Data Mining”, in Logics for Emerging Applications of Databases, 2003, pp. 325-361.
F. Giannotti, Manco, G., and Pedreschi, D., “A Deductive Data Model for Representing and Querying Semistructured Data”, in APPIA-GULP-PRODE, 1997, pp. 129-140.
F. Giannotti and Pedreschi, D., “Datalog with Non-Deterministic Choice Computers NDB-PTIME”, J. Log. Program., vol. 35, pp. 79-101, 1998.
F. Giannotti, Mazzoni, A., Puntoni, S., and Renso, C., “Synthetic generation of cellular network positioning data”, in GIS, 2005, pp. 12-20.
F. Giannotti, Manco, G., and Turini, F., “Specifying Mining Algorithms with Iterative User-Defined Aggregates”, IEEE Trans. Knowl. Data Eng., vol. 16, pp. 1232-1246, 2004.
F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., “Query Answering in Nondeterministic, Nonmonotonic Logic Databases”, in FQAS, 1998, pp. 175-187.
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.
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.
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.
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, Romei, A., and Ruggieri, S., “A Case Study in Sequential Pattern Mining for IT-Operational Risk”, in ECML/PKDD (1), 2008, pp. 424-439.
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, Coscia, M., Pedreschi, D., and Pennacchioli, D., “Going Beyond GDP to Nowcast Well-Being Using Retail Market Data”, in Advances in Network Science, Springer International Publishing, 2016, pp. 29–42.
R. Guidotti and Berlingerio, M., “Where Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services”, in Complex Networks VII, Springer International Publishing, 2016, pp. 65–78.

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