SoBigData: Social Mining and Big Data Ecosystem

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One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities promise to let us scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and scale. There is an urgent need to harness these opportunities for scientific advancement and for the social good. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open infrastructure, where big data and social mining research can be carried out. To this end, SoBigData proposes to create the Social Mining & Big Data Ecosystem: a research infrastructure (RI) providing an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life, as recorded by “big data”. Building on several established national infrastructures, SoBigData will open up new research avenues in multiple research fields, including mathematics, ICT, and human, social and economic sciences, by enabling easy comparison, re-use and integration of state-of-the-art big social data, methods, and services, into new research. It will not only strengthen the existing clusters of excellence in social data mining research, but also create a pan-European, inter-disciplinary community of social data scientists, fostered by extensive training, networking, and innovation activities. In addition, as an open research infrastucture, SoBigData will promote repeatable and open science. Although SoBigData is primarily aimed at serving the needs of researchers, the openly available datasets and open source methods and services provided by the new research infrastructure will also impact industrial and other stakeholders (e.g. government bodies, non-profit organisations, funders, policy makers).
S\^\irbu, A., G. Andrienko, N. Andrienko, C. Boldrini, M. Conti, F. Giannotti, R. Guidotti, S. Bertoli, J. Kim, C. Ioana Muntean, et al., "Human migration: the big data perspective", International Journal of Data Science and Analytics, pp. 1–20, 2020.
Pedreschi, D., F. Giannotti, R. Guidotti, A. Monreale, S. Ruggieri, and F. Turini, "Meaningful explanations of Black Box AI decision systems", Proceedings of the AAAI Conference on Artificial Intelligence, 2019.
Panigutti, C., R. Guidotti, A. Monreale, and D. Pedreschi, "Explaining multi-label black-box classifiers for health applications", International Workshop on Health Intelligence: Springer, 2019.
Pappalardo, L., P. Cintia, P. Ferragina, E. Massucco, D. Pedreschi, and F. Giannotti, "PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach", ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 5, pp. 1–27, 2019.
Milli, L., G. Rossetti, D. Pedreschi, and F. Giannotti, "Diffusive Phenomena in Dynamic Networks: a data-driven study", International Conference on Complex Networks CompleNet, Boston March 5-8 2018, Springer, 2018.
Guidotti, R., J. Soldani, D. Neri, and A. Brogi, "Explaining successful docker images using pattern mining analysis", Federation of International Conferences on Software Technologies: Applications and Foundations: Springer, Cham, 2018.
Pollacci, L., R. Guidotti, G. Rossetti, F. Giannotti, and D. Pedreschi, "The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis", International Conference on Smart Objects and Technologies for Social Good: Springer, 2018.


Fosca Giannotti, coordinator of SoBigData, has been chosen by KDnuggets as one of the 19 inspiring women who l

image via Pixabay, CC0 Creative Commons

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The video, from minute 41, shows Fosca Giannotti's talk at the GARR 2017 Conference.

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A short introduction to the SoBigData EU project (in italian).

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Start Date
15 June 2015
End Date
31 December 2019
Istituto di Scienza e Tecnologie dell’Informazione, National Research Council of Italy (ISTI-CNR)