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L. Gabrielli, Furletti, B., Giannotti, F., Nanni, M., and Rinzivillo, S., “Use of Mobile Phone Data to Estimate Visitors Mobility Flows”, in Software Engineering and Formal Methods, vol. 8938, Springer International Publishing, 2015, pp. 214-226.
L. Gabrielli, Furletti, B., Giannotti, F., Nanni, M., and Rinzivillo, S., “Use of mobile phone data to estimate visitors mobility flows”, in Proceedings of MoKMaSD, 2014.
L. Gabrielli, Fadda, D., Rossetti, G., Nanni, M., Piccinini, L., Pedreschi, D., Giannotti, F., and Lattarulo, P., “Discovering Mobility Functional Areas: A Mobility Data Analysis Approach”, in International Workshop on Complex Networks, 2018.
D. Gambetta, Mauro, G., and Pappalardo, L., “Mobility Constraints in Segregation Models”, Scientific Reports, vol. 13, p. 12087, 2023.
A. Gargantini, Riccobene, E., and Rinzivillo, S., “Using Spin to Generate Tests from ASM Specifications”, in Abstract State Machines, 2003, pp. 263-277.
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.
F. Giannotti, Trasarti, R., Bontcheva, K., and Grossi, V., “SoBigData: Social Mining & Big Data Ecosystem”, in Companion of the The Web Conference 2018 on The Web Conference 2018, 2018.
F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., “On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases”, in CSL, 1998, pp. 58-72.
F. Giannotti, Manco, G., and Turini, F., “Towards a Logic Query Language for Data Mining”, in Database Support for Data Mining Applications, 2004, pp. 76-94.
F. Giannotti, Manco, G., Nanni, M., Pedreschi, D., and Turini, F., “Integration of Deduction and Induction for Mining Supermarket Sales Data”, in SEBD, 1999, pp. 117-131.
F. Giannotti and Manco, G., “Querying Inductive Databases via Logic-Based User-Defined Aggregates”, in PKDD, 1999, pp. 125-135.
F. Giannotti, Matteucci, A., Pedreschi, D., and Turini, F., “Symbolic Evaluation with Structural Recursive Symbolic Constants”, Sci. Comput. Program., vol. 9, pp. 161-177, 1987.
F. Giannotti, Jeansoulin, R., and Theodoridis, Y., “Beyond Current Technology: The Perspective of Three EC GIS Projects”, in DEXA Workshop, 1999, p. 510.
F. Giannotti and Pedreschi, D., “Declarative Semantics for Pruning Operators in Logic Programming”, in LPNMR, 1990, pp. 27-37.
F. Giannotti, Naretto, F., and Bodria, F., “Explainable for Trustworthy AI”, in Human-Centered Artificial Intelligence - Advanced Lectures, 18th European Advanced Course on AI, ACAI 2021, Berlin, Germany, October 11-15, 2021, extended and improved lecture notes, 2021.
F. Giannotti and Manco, G., “Querying inductive Databases via Logic-Based user-defined aggregates”, in APPIA-GULP-PRODE, 1999, pp. 605-620.
F. Giannotti, Nanni, M., Pedreschi, D., Pinelli, F., Renso, C., Rinzivillo, S., and Trasarti, R., “Mobility data mining: discovering movement patterns from trajectory data”, in Computational Transportation Science, 2010, pp. 7-10.

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