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).
Rossetti, G., L. Pappalardo, and S. Rinzivillo, "A novel approach to evaluate community detection algorithms on ground truth", 7th Workshop on Complex Networks, Dijon, France, Springer-Verlag, 2016.
Sirbu, A., and O. Babaoglu, "Predicting System-level Power for a Hybrid Supercomputer", 2016 International Conference on High Performance Computing Simulation (HPCS), Innsbruck, Austria, IEEE, 07/2016.
Monreale, A., and H. Wendy Wang, "Privacy-Preserving Outsourcing of Data Mining", 40th IEEE Annual Computer Software and Applications Conference, {COMPSAC} Workshops 2016, Atlanta, GA, USA, June 10-14, 2016, Atlanta, GA, USA, IEEE Computer Society, 2016.
Guidotti, R., G. Rossetti, and D. Pedreschi, "Audio Ergo Sum", Federation of International Conferences on Software Technologies: Applications and Foundations: Springer, 2016.
Sirbu, A., and O. Babaoglu, "Power Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer", 22nd International European Conference on Parallel and Distributed Computing, Euro-Par 2016, vol. LNCS 9833, Grenoble, France, Springer LNCS, 2016.
Rossetti, G., L. Pappalardo, R. Kikas, D. Pedreschi, F. Giannotti, and M. Dumas, "Community-centric analysis of user engagement in Skype social network", International conference on Advances in Social Network Analysis and Mining, Paris, France, IEEE, 2015.
Milli, L., A. Monreale, G. Rossetti, D. Pedreschi, F. Giannotti, and F. Sebastiani, "Quantification in Social Networks", International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), Paris, France, IEEE, 2015.
Rossetti, G., R. Guidotti, D. Pennacchioli, D. Pedreschi, and F. Giannotti, "Interaction Prediction in Dynamic Networks exploiting Community Discovery", International conference on Advances in Social Network Analysis and Mining, ASONAM 2015, Paris, France, IEEE, 2015.


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

Mettere a disposizione di tutti i dati, in formato aperto, sulle decisioni di investimento pubblico utilizzando il CUP (Codice Unico di Progetto) che da oltre 10 anni identifica le decisioni di investimento pubblico, una chiave univoca in grado di co

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

The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring.

Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services.

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

C’è una buona notizia in materia di Big Data che arriva da Bruxelles.

In 2000 mobile phone users accounted for 12% of the world’s population. By the end of 2014, this figure had reached 96%, i.e., 6.8 billion people.

Nel 2000 gli utenti della telefonia mobile erano il 12% della popolazione mondiale. A fine 2014 sono il 96%, ovvero 6,8 miliardi di persone.

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