The growing number of connected devices, with more features and higher transmission speeds, generate large amounts of data of different nature, e.g., pictures, video, text and binary. Extracting knowledge from such data requires a range of tools and techniques, from scalable storage systems, to machine learning algorithms that uncover previously unknown patterns and derive useful models, supporting fault detection, automatic diagnosis, prediction and optimization processes, among others. The use of highly scalable big data repositories, parallel processing power of HPC clusters and GPUs, advanced machine learning algorithms, algorithms for dimensionality reduction and other techniques to provide insightful visualisation to users, contribute to cope with the ubiquitous digitalisation of processes, workflows and data in the modern society.
This research topic includes the following topics/technologies:
Advanced data analytics - Big data
Parallel and ubiquitous computing
The on-going R&D projects in this research topic are SPEET, DA.RE, SAFe.