The research focuses on multiple (possibly interdisciplinary) aspects of
data management and information systems, also tackling the so called
"big data challenges".
It includes heterogeneous data integration,
multi-modal analysis, indexing, classification, and visualization of
large data volumes, multi-facet data exploration and information
retrieval, privacy-preserving data management and access control.
Large scale datasets are generated in various domains, including social networks, real-time sensor data, public open-data, bioinformatics. Dealing with large data opens challenging research problems, such as defining suitable representation models, developing scalable (possibly pseudo-optimal) data analysis algorithms, designing effective big data visualizations strategies, devising efficient memorization and indexing techniques.
The concept of network is pervasive in our society. Among the most well-known examples we can cite social networks, technological networks, the document networks, and biological networks. The objective of this research area is to develop mathematical frameworks that can assist the researchers of different disciplines to study the structure and the dynamics of complex networks.
