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M. Nanni and Trasarti, R., Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract), in SEBD, 2010, pp. 126-133.
R. Trasarti, Giannotti, F., Nanni, M., Pedreschi, D., and Renso, C., A Query Language for Mobility Data Mining, IJDWM, vol. 7, pp. 24-45, 2011.
F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., Query Answering in Nondeterministic, Nonmonotonic Logic Databases, in FQAS, 1998, pp. 175-187.
L. Pappalardo and Cintia, P., Quantifying the relation between performance and success in soccer, Advances in Complex Systems, p. 1750014, 2017.
P. Eirinakis, Ruggieri, S., Subramani, K., and Wojciechowski, P., On quantified linear implications, Annals of Mathematics and Artificial Intelligence, vol. 71, pp. 301–325, 2014.
L. Milli, Monreale, A., Rossetti, G., Giannotti, F., Pedreschi, D., and Sebastiani, F., Quantification Trees, in 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013, 2013, pp. 528–536.
L. Milli, Monreale, A., Rossetti, G., Pedreschi, D., Giannotti, F., and Sebastiani, F., Quantification in Social Networks, in International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), Paris, France, 2015.
A. Raffaetà, Renso, C., and Turini, F., Qualitative Spatial Reasoning in a Logical Framework, in AI*IA, 2003, pp. 78-90.
A. Raffaetà, Renso, C., and Turini, F., Qualitative Reasoning in a Spatio-Temporal Language, in SEBD, 2002, pp. 105-118.
P
F. Bonchi and Giannotti, F., Pushing Constraints to Detect Local Patterns, in Local Pattern Detection, 2004, pp. 1-19.
B. Furletti, Bellandi, A., Romei, A., and Grossi, V., PUSHING CONSTRAINTS IN ASSOCIATION RULE MINING: AN ONTOLOGY-BASED APPROACH , in IADIS International Conference WWW/Internet 2007, 2007.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., The pursuit of hubbiness: Analysis of hubs in large multidimensional networks, J. Comput. Science, vol. 2, pp. 223-237, 2011.
S. Rinzivillo, Gabrielli, L., Nanni, M., Pappalardo, L., Pedreschi, D., and Giannotti, F., The purpose of motion: Learning activities from Individual Mobility Networks, in International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014, 2014.
A. Sirbu, Giannotti, F., Pedreschi, D., and Kertész, J., Public opinion and Algorithmic bias, ERCIM News, 2019.
L. Pappalardo, Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., and Giannotti, F., A public data set of spatio-temporal match events in soccer competitions, Scientific data, vol. 6, pp. 1–15, 2019.
F. Pratesi, Monreale, A., Trasarti, R., Giannotti, F., Pedreschi, D., and Yanagihara, T., PRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems, Transactions on Data Privacy, vol. 11, 2018.
P. Mascellani and Pedreschi, D., Proving termination of Prolog programs, in GULP-PRODE (1), 1994, pp. 46-61.
K. R. Apt and Pedreschi, D., Proving Termination of General Prolog Programs, in TACS, 1991, pp. 265-289.
D. Pedreschi, A Proof Method for Runtime Properties of Prolog Programs, in ICLP, 1994, pp. 584-598.
B. Bertolino, Mancarella, P., Meo, L., Nini, L., Pedreschi, D., and Turini, F., A Progress Report on the LML Project, in FGCS, 1988, pp. 675-684.
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.
M. Coscia, Pennacchioli, D., and Giannotti, F., Product assortment and customer mobility, EPJ Data Science, vol. 4, pp. 1–18, 2015.
P. Mukala, Cerone, A., and Turini, F., Process mining event logs from FLOSS data: state of the art and perspectives, in International Conference on Software Engineering and Formal Methods, 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. Marrella, Monreale, A., Kloepper, B., and Krueger, M. W., Privacy-Preserving Outsourcing of Pattern Mining of Event-Log Data-A Use-Case from Process Industry, in Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on, 2016.

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