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M. Nanni, Trasarti, R., and Giannotti, F., Movement Behaviour Recognition for Water Activities, in Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, {PAP} 2017, Held in Conjunction with {ECML} {PKDD} 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers, 2017.
M. Nanni, Trasarti, R., Furletti, B., Gabrielli, L., Van Der Mede, P., De Bruijn, J., de Romph, E., and Bruil, G., MP4-A Project: Mobility Planning For Africa, in In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013), Cambridge, USA, 2013.
M. Nanni, Kuijpers, B., Körner, C., May, M., and Pedreschi, D., Spatiotemporal Data Mining, in Mobility, Data Mining and Privacy, 2008, pp. 267-296.
M. Nanni, Trasarti, R., Monreale, A., Grossi, V., and Pedreschi, D., Driving Profiles Computation and Monitoring for Car Insurance CRM, Journal ACM Transactions on Intelligent Systems and Technology (TIST), vol. 8, pp. 14:1–14:26, 2016.
M. Nanni and Pedreschi, D., Time-focused clustering of trajectories of moving objects, J. Intell. Inf. Syst., vol. 27, pp. 267-289, 2006.
M. Nanni, Raffaetà, A., Renso, C., and Turini, F., Spatio-Temporal Data, Spatio-Temporal Databases: Flexible Querying and Reasoning, p. 75, 2013.
M. Nanni, Trasarti, R., Cintia, P., Furletti, B., Renso, C., Gabrielli, L., Rinzivillo, S., and Giannotti, F., Mobility Profiling, in Data Science and Simulation in Transportation Research, IGI Global, 2014, pp. 1-29.
M. Nanni, Trasarti, R., Renso, C., Giannotti, F., and Pedreschi, D., Advanced knowledge discovery on movement data with the GeoPKDD system, in EDBT, 2010, pp. 693-696.
M. Nanni, Trasarti, R., Renso, C., Giannotti, F., and Pedreschi, D., Advanced knowledge discovery on movement data with the GeoPKDD system, in EDBT, 2010, pp. 693-696.
M. Nanni, Raffaetà, A., Renso, C., and Turini, F., \newblock{A Declarative Framework for Reasoning on Spatio-temporal Data}, 2004.
M. Nanni, Nijssen, S., O'Sullivan, B., Paparrizou, A., Pedreschi, D., and Simonis, H., The Inductive Constraint Programming Loop, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach, vol. 10101, p. 303, 2017.
M. Nanni and Trasarti, R., Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract), in SEBD, 2010, pp. 126-133.
M. Nanni, Kotthoff, L., Guidotti, R., O'Sullivan, B., and Pedreschi, D., ICON Loop Carpooling Show Case, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach, vol. 10101, p. 310, 2017.
M. Nanni and Trasarti, R., K-BestMatch Reconstruction and Comparison of Trajectory Data, in ICDM Workshops, 2009, pp. 610-615.
M. Nanni and Trasarti, R., K-BestMatch Reconstruction and Comparison of Trajectory Data, in ICDM Workshops, 2009, pp. 610-615.
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P. Mukala, Cerone, A., and Turini, F., An exploration of learning processes as process maps in FLOSS repositories. 2015.
P. Mukala, Cerone, A., and Turini, F., Mining learning processes from FLOSS mailing archives, in Conference on e-Business, e-Services and e-Society, 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.
P. Mukala, Cerone, A., and Turini, F., Ontolifloss: Ontology for learning processes in FLOSS communities, in International Conference on Software Engineering and Formal Methods, 2014.
P. Mukala, Cerone, A., and Turini, F., An abstract state machine (ASM) representation of learning process in FLOSS communities, in International Conference on Software Engineering and Formal Methods, 2014.
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.
D. Morelli, Rossi, A., Cairo, M., and Clifton, D. A., Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations, Sensors, vol. 19, p. 3163, 2019.
D. Montesi, Renso, C., and Turini, F., Using Temporary Integrity Constraints to Optimize Databases, in FAPR, 1996, pp. 430-435.
A. Monreale, Pedreschi, D., Pensa, R. G., and Pinelli, F., Anonymity preserving sequential pattern mining, Artif. Intell. Law, vol. 22, pp. 141–173, 2014.
A. Monreale, Wang, H. Wendy, Pratesi, F., Rinzivillo, S., Pedreschi, D., Andrienko, G., and Andrienko, N., Privacy-Preserving Distributed Movement Data Aggregation, in Geographic Information Science at the Heart of Europe, D. Vandenbroucke, Bucher, B., and Crompvoets, J., Eds. Springer International Publishing, 2013, pp. 225-245.

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