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Applied Data Science and Visual Analytics

Data Science aims at discovering patterns and models of human behavior across the various social dimensions, extracting multi-dimensional patterns and models from a vast variety of social data.
Data Science may have a potentially high impact and may generate enormous value to society. It can create new opportunities to understand complex aspects, such as mobility behaviors, economic and financial crises, the spread of epidemics, the diffusion of opinions and so on.

Mobility Data Mining for Science of Cities

The large diffusion of localization technologies and location-based services is leading to the production of large and diversified traces of human mobility, containing the potential to infer models of unprecedented precision and depth. They can be key enablers of many applications, from monitoring urban traffic features to reconstruct inter-city mobility demands and region-scale structures, which could help in making modern urban spaces more sustainable, efficient and comfortable for citizens.

Social Network Analysis and Network Science

The main research track of KDDLab in the field of Complex Networks is Multidimensional Network Analysis. Traditionally, Complex Network Analysis has been monodimensional: researchers focused their attention to network with a single kind of relation represented. KDDLab is pushing the research over multidimensional networks, i.e. network with multiple kind of relations, since they are a better model to represent the complexity in reality (transportation, infrastructure and social networks are often multidimensional).

Ethical, Trustworthy, Interactive AI

In the era of Big Data person-specific data are increasingly collected, stored and analyzed by modern organizations.
These data typically describe different dimensions of the daily social life and are the heart of a knowledge society, where the understanding of complex social phenomena is sustained by the knowledge extracted from the miners of big data across the various social dimensions by using data mining, machine learning and AI technologies.

Analytical Platforms and Infrastructures for Social Mining

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.