Track and Know

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Track&Know will research, develop and exploit a new software framework that aims at increasing the efficiency of Big Data applications in the transport, mobility, motor insurance and health sectors. Stemming from industrial cases, Track&Know will develop user friendly toolboxes that will be readily applicable in the addressed markets, and will be also investigated in additional domains through liaison activities with running ICT-15 Lighthouse projects. Track&Know integrates multidisciplinary research teams from Mobility Data management, Complex Event Recognition, Geospatial Modelling, Complex Network Analysis, Transportation Engineering and Visual Analytics to develop new models and applications. Track&Know recognizes that Big Data penetration is not adequately developed in niche markets outside the traditional verticals (e.g. Finance) and so the Track&Know Toolboxes will be demonstrated in three real-world Pilots using datasets from niche market scenarios to validate efficiency improvements. Performance and impact benchmarks are elaborated and will be documented during pilots deployment. The Track&Know consortium is composed by complementary partners, coming from addressed research, technological and commercial domains, that have a proven track record of high quality research capacity. Thus, the carefully structured workplan, embodies a holistic approach towards meeting the Track&Know objectives and delivering market-relevant outcomes of significant exploitation potential.
Guidotti, R., A. Monreale, S. Matwin, and D. Pedreschi, "Black Box Explanation by Learning Image Exemplars in the Latent Feature Space", Machine Learning and Knowledge Discovery in Databases, Cham, Springer International Publishing, 2020//.
Guidotti, R., and G. Rossetti, "“Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network", Formal Methods. FM 2019 International Workshops, Cham, Springer International Publishing, 2020//.
Setzu, M., R. Guidotti, A. Monreale, and F. Turini, "Global Explanations with Local Scoring", Machine Learning and Knowledge Discovery in Databases, Cham, Springer International Publishing, 2020//.
Web Site
Start Date
1 January 2018
End Date
31 December 2020
European Commission
European Project
Istituto di Scienza e Tecnologie dell’Informazione, National Research Council of Italy (ISTI-CNR)