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F. Giannotti, Manco, G., Nanni, M., and Pedreschi, D., Query Answering in Nondeterministic, Nonmonotonic Logic Databases, in FQAS, 1998, pp. 175-187.
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., On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases, in CSL, 1998, pp. 58-72.
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, 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, 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.
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 and Manco, G., Querying Inductive Databases via Logic-Based User-Defined Aggregates, in PKDD, 1999, pp. 125-135.
F. Giannotti, Lakshmanan, L. V. S., Monreale, A., Pedreschi, D., and Wang, H. Wendy, Privacy-Preserving Mining of Association Rules From Outsourced Transaction Databases, IEEE Systems Journal, 2013.
F. Giannotti, Gabrielli, L., Pedreschi, D., and Rinzivillo, S., Understanding human mobility with big data, in Solving Large Scale Learning Tasks. Challenges and Algorithms, Springer International Publishing, 2016, pp. 208–220.
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, 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, 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.
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, Mobility Ranking - Human Mobility Analysis Using Ranking Measures, 2013.
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, Monreale, A., Matwin, S., and Pedreschi, D., Black Box Explanation by Learning Image Exemplars in the Latent Feature Space, in Machine Learning and Knowledge Discovery in Databases, Cham, 2020.

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