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L. Pappalardo and Simini, F., “Data-driven generation of spatio-temporal routines in human mobility”, Data Mining and Knowledge Discovery, 2017.
L. Pappalardo, Simini, F., Rinzivillo, S., Pedreschi, D., Giannotti, F., and Barabasi, A. - L., “Returners and explorers dichotomy in human mobility”, Nat Commun, vol. 6, 2015.
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.
L. Pappalardo, Simini, F., Rinzivillo, S., Pedreschi, D., and Giannotti, F., “Comparing General Mobility and Mobility by Car”, in Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC), 2013 BRICS Congress on, 2013.
V. Pansanella, Rossetti, G., and Milli, L., “From Mean-Field to Complex Topologies: Network Effects on the Algorithmic Bias Model”, in Complex Networks & Their Applications X, 2022.
C. Panigutti, Guidotti, R., Monreale, A., and Pedreschi, D., “Explaining multi-label black-box classifiers for health applications”, in International Workshop on Health Intelligence, 2019.
C. Panigutti, Tizzoni, M., Bajardi, P., Smoreda, Z., and Colizza, V., “Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models”, Royal Society open science, vol. 4, p. 160950, 2017.
C. Panigutti, Perotti, A., and Pedreschi, D., “Doctor XAI: an ontology-based approach to black-box sequential data classification explanations”, in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020.
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R. Ortale, Ritacco, E., Pelekis, N., Trasarti, R., Costa, G., Giannotti, F., Manco, G., Renso, C., and Theodoridis, Y., “The DAEDALUS framework: progressive querying and mining of movement data”, in GIS, 2008, p. 52.
R. Ortale, Ritacco, E., Pelekisy, N., Trasarti, R., Costa, G., Giannotti, F., Manco, G., Renso, C., and Theodoridis, Y., “DAEDALUS: A knowledge discovery analysis framework for movement data”, in SEBD, 2008, pp. 191-198.
R. Ong, Pinelli, F., Trasarti, R., Nanni, M., Renso, C., Rinzivillo, S., and Giannotti, F., “Traffic Jams Detection Using Flock Mining”, in ECML/PKDD (3), 2011, pp. 650-653.
R. Ong, Nanni, M., Renso, C., Wachowicz, M., and Pedreschi, D., “A Study on Parameter Estimation for a Mining Flock Algorithm ”, in Mining Complex Patterns Workshop, ECML PKDD 2013, 2013.
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E. Ntoutsi, Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. - E., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., and others, “Bias in data-driven artificial intelligence systems—An introductory survey”, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, p. e1356, 2020.
A. Rita Nogueira, Pugnana, A., Ruggieri, S., Pedreschi, D., and Gama, J., “Methods and tools for causal discovery and causal inference”, WIREs Data Mining Knowl. Discov., vol. 12, 2022.
M. Natilli, Fadda, D., Rinzivillo, S., Pedreschi, D., and Licari, F., “Analysis and Visualization of Performance Indicators in University Admission Tests”, in Formal Methods. FM 2019 International Workshops, Cham, 2020.
M. Natilli, Rossi, A., Trecroci, A., Cavaggioni, L., Merati, G., and Formenti, D., “The long-tail effect of the COVID-19 lockdown on Italians’ quality of life, sleep and physical activity”, Scientific Data, vol. 9, pp. 1–10, 2022.
M. Natilli, Monreale, A., Guidotti, R., and Pappalardo, L., “Exploring Students Eating Habits Through Individual Profiling and Clustering Analysis”, in ECML PKDD 2018 Workshops, 2018.
M. Natilli and Romano, M. Francesca, “The impact of wine and food tourism in Italy: an analysis of official statistical data at province level”, in First European Conference on Wine and Food Tourism, 2011.
F. Naretto, Monreale, A., and Giannotti, F., “Evaluating the Privacy Exposure of Interpretable Global and Local Explainers”, in Submitted at Journal of Artificial Intelligence and Law, 2023.
F. Naretto, Pellungrini, R., Monreale, A., Nardini, F. Maria, and Musolesi, M., “Predicting and Explaining Privacy Risk Exposure in Mobility Data”, in Discovery Science, Cham, 2020.
F. Naretto, Pellungrini, R., Fadda, D., and Rinzivillo, S., “EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories”, in Discovery Science , 2023.
F. Naretto, Pellungrini, R., Nardini, F. Maria, and Giannotti, F., “Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks”, in ECML PKDD 2020 Workshops, Cham, 2020.
M. Nanni, Raffaetà, A., Renso, C., and Turini, F., “Spatio-Temporal Data”, Spatio-Temporal Databases: Flexible Querying and Reasoning, p. 75, 2013.
M. Nanni, Andrienko, G., Barabasi, A. - L., Boldrini, C., Bonchi, F., Cattuto, C., Chiaromonte, F., Comandé, G., Conti, M., Coté, M., Dignum, F., Dignum, V., Domingo-Ferrer, J., Ferragina, P., Giannotti, F., Guidotti, R., Helbing, D., Kaski, K., Kertész, J., Lehmann, S., Lepri, B., Lukowicz, P., Matwin, S., Jiménez, D. Megías, Monreale, A., Morik, K., Oliver, N., Passarella, A., Passerini, A., Pedreschi, D., Pentland, A., Pianesi, F., Pratesi, F., Rinzivillo, S., Ruggieri, S., Siebes, A., Torra, V., Trasarti, R., van den Hoven, J., and Vespignani, A., “Give more data, awareness and control to individual citizens, and they will help COVID-19 containment”, 2021.
M. Nanni, Raffaetà, A., Renso, C., and Turini, F., “Deductive and Inductive Reasoning on Spatio-Temporal Data”, in INAP/WLP, 2004, pp. 98-115.

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