RELAX-DN, short for Relaxed Semantics Across the Data Analytics Stack, is a Doctoral Network (DN) project that has received funding from the European Union’s HORIZON-MSCA-2021-DN-01 call under the Marie Skłodowska-Curie grant agreement 101072456.

The project has a duration of four years starting 1 March 2023. Twelve Doctoral Candidates (DC) perform research in the project, together with their academic and industrial supervisors.

Main objectives

  1. To develop the principles of robust data analytic algorithms in the face of uncertain, inaccurate and/or biased data. The targets are
  • (i) to understand the interplay between imperfections of the data and imperfections of the computation in order to tune one within the boundaries allowed by the other;
  • (ii) to design methodologies and algorithms to ensure robust decision-making, in particular by ensuring qualitative properties such as uncertainty, reproducibility, and explainability.
  1. To develop new algorithms for indexing and summarisation building upon the data attributes explored in WP1. The targets are
  • (i) to develop algorithms and indexing structures that explore application-tailored trade-offs between accuracy, speed and performance;
  • (ii) to investigate algorithms for compression, summarisation and approximation based on controlling the precision and quality of data.
  1. To develop new algorithms and new coordination and synchronisation models to support asynchronous and incremental AI and ML and large data processing for better performance, better freshness and without loss of accuracy, when compared to contemporary barrier-based synchronous approaches that suffer from scalability.
  2. To develop and organize a bespoke network-wide joint training programme.
  3. To develop and implement the communication strategy, including social media and web presence, traditional media announcements, identifying and taking part in outreach activities, exploitation of results, managing open source software and open data.