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A. Rossi, Pappalardo, L., Cintia, P., F Iaia, M., Fernàndez, J., and Medina, D., “Effective injury forecasting in soccer with GPS training data and machine learning”, PloS one, vol. 13, p. e0201264, 2018.
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. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., “Efficient breadth-first mining of frequent pattern with monotone constraints”, Knowl. Inf. Syst., vol. 8, pp. 131-153, 2005.
K. S. Boeg, Ira Assent,, and Magnani, M., “Efficient GPU-based skyline computation”, in DAMON@SIGMOD 2013, 2013.
F. Giannotti, Nanni, M., and Pedreschi, D., “Efficient Mining of Temporally Annotated Sequences”, in SDM, 2006.
A. Baroni, Conte, A., Patrignani, M., and Ruggieri, S., “Efficiently Clustering Very Large Attributed Graphs”, arXiv preprint arXiv:1703.08590, 2017.
P. Contucci, Panizzi, E., Ricci-Tersenghi, F., and Sirbu, A., “Egalitarianism in the rank aggregation problem: a new dimension for democracy”, Quality & Quantity, pp. 1–16, 2015.
A. Sirbu, Crane, M., and Ruskin, H. J., “EGIA–Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach”, in Complex Networks V, Springer International Publishing, 2014, pp. 217–229.
P. Mukala, Cerone, A., and Turini, F., “An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities”, Education and Information Technologies, vol. 22, pp. 3207–3229, 2017.
P. Cintia, Pappalardo, L., and Pedreschi, D., “"Engine Matters": {A} First Large Scale Data Driven Study on Cyclists' Performance”, in 13th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, TX, USA, December 7-10, 2013, 2013.
A. Raffaetà, Turini, F., and Renso, C., “Enhancing GISs for spatio-temporal reasoning”, in ACM-GIS, 2002, pp. 42-48.
S. Ruggieri, “Enumerating Distinct Decision Trees”, in International Conference on Machine Learning, 2017.
R. Guidotti and Coscia, M., “On the Equivalence Between Community Discovery and Clustering”, in International Conference on Smart Objects and Technologies for Social Good, 2017.
A. Rossi, Pedreschi, D., Clifton, D. A., and Morelli, D., “Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts”, Sensors, vol. 20, p. 7122, 2020.
V. Voukelatou, Pappalardo, L., Gabrielli, L., and Giannotti, F., “Estimating countries’ peace index through the lens of the world news as monitored by GDELT”, in 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020.
F. Lillo and Ruggieri, S., “Estimating the Total Volume of Queries to a Search Engine”, IEEE Transactions on Knowledge and Data Engineering, pp. 1-1, 2021.
N. Forgó, Hänold, S., van den Hoven, J., Krügel, T., Lishchuk, I., Mahieu, R., Monreale, A., Pedreschi, D., Pratesi, F., and van Putten, D., “An ethico-legal framework for social data science”, 2020.
S. Citraro and Rossetti, G., “Eva: Attribute-Aware Network Segmentation”, in International Conference on Complex Networks and Their Applications, 2019.
R. Cazabet, Boudebza, S., and Rossetti, G., “Evaluating community detection algorithms for progressively evolving graphs”, arXiv preprint arXiv:2007.08635, 2020.
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.
M. Berlingerio, Coscia, M., Giannotti, F., Monreale, A., and Pedreschi, D., “Evolving networks: Eras and turning points”, Intell. Data Anal., vol. 17, pp. 27–48, 2013.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., “ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint”, in ICDM, 2003, pp. 11-18.
F. Turini, Baglioni, M., Furletti, B., and Rinzivillo, S., “Examples of Integration of Induction and Deduction in Knowledge Discovery”, in Reasoning, Action and Interaction in AI Theories and Systems, 2006, pp. 307-326.
F. Turini, Baglioni, M., Furletti, B., and Rinzivillo, S., “Examples of Integration of Induction and Deduction in Knowledge Discovery”, in Reasoning, Action and Interaction in AI Theories and Systems, vol. 4155, 2006, pp. 307-326.
F. Bonchi, Giannotti, F., Mazzanti, A., and Pedreschi, D., “Exante: A Preprocessing Method for Frequent-Pattern Mining”, IEEE Intelligent Systems, vol. 20, pp. 25-31, 2005.

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