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F. Giannotti, Nanni, M., Pinelli, F., and Pedreschi, D., “Trajectory pattern mining”, in KDD, 2007, pp. 330-339.
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 and Pedreschi, D., Mobility, Data Mining and Privacy - Geographic Knowledge Discovery. Springer, 2008.
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, Nanni, M., Pedreschi, D., and Pinelli, F., “Trajectory pattern analysis for urban traffic”, in Second International Workshop on Computational Transportation Science, SEATTLE, USA, 2009, pp. 43-47.
F. Giannotti, Pedreschi, D., and Turini, F., “Mobility, Data Mining and Privacy the Experience of the GeoPKDD Project”, in PinKDD, 2008, pp. 25-32.
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.
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, 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.
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., and Pedreschi, D., “The AI black box Explanation Problem”, ERCIM NEWS, pp. 12–13, 2019.
R. Guidotti, Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D., “A survey of methods for explaining black box models”, ACM computing surveys (CSUR), vol. 51, p. 93, 2018.
R. Guidotti, Rossetti, G., and Pedreschi, D., “Audio Ergo Sum”, in Federation of International Conferences on Software Technologies: Applications and Foundations, 2016.
R. Guidotti, Monreale, A., and Cariaggi, L., “Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers”, in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2019.
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.

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