Big Data Technologies


A plethora of applications make use of massive amounts of data, usually called big data that, besides being huge in volumes, include a variety of information collected from heterogeneous sources, usually at very high speed.  Specific techniques are needed to handle and to dig into big data so as to extract knowledge, and, by engineering solutions, transform this knowledge into value.
Dedicated data storage platforms are typically based on parallel approaches to access and process the data in an efficient way, possibly relying on distributed architectures to achieve scalability, reliability, robustness to faults. Algorithms are needed to efficiently mine and process the data and to ease the knowledge extraction from the data, through data representation and visualization, correlation detection, rule mining and machine learning.

ERC sectors 

  • PE6_2 Distributed systems, parallel computing, sensor networks, cyber-physical systems
  • PE6_3 Software engineering, programming languages and systems
  • PE6_6 Algorithms and complexity, distributed, parallel and network algorithms, algorithmic game theory
  • PE7_6 Communication systems, wireless technology, high-frequency technology
  • PE7_8 Networks, e.g. communication networks and nodes, Internet of Things, sensor networks, networks of robots


  • Distributed architectures
  • Parallel computing
  • Machine learning
  • Data visualization