Algorithms for Big Data


In many application contexts huge volumes of data are produced which are used in the economic-financial, political, social and even institutional fields. Often the data is stored in huge distributed clouds and is sometimes generated according to a continuous flow, so large as to make complete storage unfeasible. In many cases the data pertains to entities in close relationship with each other and gives rise to massive networks of connections. Familiar examples for such networks are biological and social networks, distribution networks, and the Web graph. Furthermore, the fact that the data is stored in systems managed by third parties poses integrity problems, which have not been considered in the classical IT literature in terms of both their type and scale.

This scenario poses unprecedented algorithmic challenges, which are being considered by a vast audience of researchers. In the last decade, this effort has produced many innovations on both the methodological and technological level. This course aims at transferring to the students some of the most important methodological tools originated from the research on Big Data algorithms. These methodological tools are presented within challenging application contexts.

PhD course of the Doctoral School in Computer Engineering and Automation at Roma Tre University

Instructor: Giordano Da Lozzo

Guests: Giuseppe Di Battista, Maurizio Patrignani, Maurizio Pizzonia, and Fabrizio Frati