Scalable Data Processing in Networked Systems

PI: Anders Rantzer (LU). Co-PIs: Richard Pates (LU), Claudio Altafini (LiU), Anders Hansson (LiU).

This project aims to develop scalable algorithms to monitor, predict, and analyze data from large-scale dynamic networks. In line with ELLIIT focus theme Big Data and Network Science, the objectives are:

  1. To exploit structural properties (such as graph properties, symmetries and conservation laws) in large dynamic networks to for scalable estimation and filtering with noisy and unreliable data.
  2. To develop tools to support network operators monitoring the behaviour of networked systems, through scalable algorithms with provable performance guarantees.

They will build on classically established tools for small systems and focus on techniques for extension to large-scale settings. The theory will be supported by applications to emerging areas such as the operation of energy networks with increasing levels of renewable penetration, congestion predictions in city traffic and information propagation in social networks.

Project number: A2