Fosca Giannotti Fosca Giannotti, KDD LAB – Knowledge Discovery and Data Mining Lab Istituto di Scienza e Tecnologie dell’Informazione del CNR

Big Data Analytics and Social Mining
Big Data at a societal scale provide a powerful microscope, which together with Social Mining – the ability of discovering knowledge from these data – can help us understand and forecast many complex and hidden socio-economic phenomena, from the diffusion of information, innovation and crises to the unequal distribution of resources and opportunities. Scientific research is being revolutionized by this new wave, and policy making is next in line, because big data and social mining are providing novel means for measuring and monitoring well-being in our societies – more realistically, beyond the GDP, more precisely, continuously, everywhere. Sensed data are low-level and semantically poor because they expose the raw details of the measurements allowed by the ICT infrastructure that generates them. The big size of data does not always overcome semantic deficiency when modelling complex phenomena. Social mining is about making sense of Big Data by extracting meaningful information from large, messy and noisy data (originally produced for other purposes than analysis) and it is a complex process that requires high-level analytics, modeling and reasoning techniques. The seminar discusses the novel questions that big data and social mining allow to raise and answer, how a new paradigm for scientific exploration and policy making is emerging. We will focus on the complexity of the underlying analytical process on the base of concrete projects that use BigData:

Students will be encouraged to study other experiences from literature to the aim of discussing the analytical challenges (and solutions) that researchers from different disciplines pose to social mining research.

BIOGRAPHICAL SKETCH:

Fosca Giannotti (female) is Research Director at ISTI-CNR, Pisa. She serves as general chair (2012-2015) of the Steering Committee of the European Association for Machine Learning and Knowledge Discovery. She has spent more than three years visiting various Research Institutions in US and EU. She has been involved in many European research projects since 1990, and coordinated the GeoPKDD FP6 FET-Open project (www.geopkdd.eu) awarded in 2010 as one of the top results achieved within the FET-Open program and presented at the European parliament in Strasbourg. She is the author of more than 200 publications and served in the scientific committee of the main conferences in the area of Databases and Data Mining. She chaired ECML/PKDD 2004, the European Conf. on Machine Learning and Knowledge Discovery in Data Bases, and ICDM 2008, the IEEE Int. Conf. on Data Mining. Her current research interests include spatio-temporal data mining, privacy preserving data mining, social network analysis and data mining query languages.

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