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G. Amato, Candela, L., Castelli, D., Esuli, A., Falchi, F., Gennaro, C., Giannotti, F., Monreale, A., Nanni, M., Pagano, P., Pappalardo, L., Pedreschi, D., Pratesi, F., Rabitti, F., Rinzivillo, S., Rossetti, G., Ruggieri, S., Sebastiani, F., and Tesconi, M., “How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science”, in A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, S. Flesca, Greco, S., Masciari, E., and Saccà, D., Eds. Cham: Springer International Publishing, 2018, pp. 287 - 306.
G. Amato, Giannotti, F., and Mainetto, G., “Data Sharing Analysis for a Database Programming Lanaguage via Abstract Interpretation”, in VLDB, 1993, pp. 405-415.
G. Amato, Giannotti, F., and Mainetto, G., “Static Analysis of Transactions: an Experiment of Abstract Interpretation Usage”, in FMLDO, 1993, pp. 19-29.
C. Alfonsi, Scarabottolo, N., Pedreschi, D., and Simi, M., “IT4PS: information technology for problem solving”, in ITiCSE, 2004, p. 241.
M. Aldinucci, Ruggieri, S., and Torquati, M., “Decision tree building on multi-core using FastFlow”, Concurrency and Computation: Practice and Experience, vol. 26, pp. 800–820, 2014.
F. D. C. Albuquerque, Casanova, M. A., de Carvalho, M. T. M., de Macêdo, J. A. F., and Renso, C., “A Proactive Ap- plication to Monitor Truck Fleets”, in Mobile Data Management Conference, 2013, 2013.
A. Albano, Giannotti, F., Orsini, R., and Pedreschi, D., “The Type System of Galileo”, in Data Types and Persistence (Appin), Informal Proceedings, 1985, pp. 175-195.
A. Albano, Giannotti, F., Orsini, R., and Pedreschi, D., “The Type System of Galileo”, in Data Types and Persistence (Appin), 1985, pp. 101-119.
O. Abul, Atzori, M., Bonchi, F., and Giannotti, F., “Hiding Sequences”, in SEBD, 2007, pp. 233-241.
O. Abul, Atzori, M., Bonchi, F., and Giannotti, F., “Hiding Sensitive Trajectory Patterns”, in ICDM Workshops, 2007, pp. 693-698.
O. Abul, Atzori, M., Bonchi, F., and Giannotti, F., “Hiding Sequences”, in ICDE Workshops, 2007, pp. 147-156.
, “Fair Federated Learning methodology based on Multi-Objective Optimization”, Submitted at JAIR, 2023.

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