The last decade has seen an exponential growth in the generation of a wide variety of data in an equally wide variety of settings (industrial, commercial, scientific, and personal). In order to successfully exploit this so-called Big-data wave (e.g., for competitive decision making), advanced analytic methods that turn raw data into meaningful and useful information are primordial and represent a major call to arms in Computer Science research. In particular, given the Velocity characteristic of Big Data, there is a growing need for so-called dynamic or reactive analytics methods that can support analytics in the presence of real-time updates.
This research project, graciously funded by the Wiener-Anspach foundation, aims to investigate fundamental and system challenges in supporting scalable analytics in the presence of real-time updates. The analytics problems envisioned cover wide areas of computer science and include database aggregate queries, probabilistic inference, matrix chain computation, and building statistical models. Our aim is to propose a uniform framework to dynamically solve this host of problem settings, focusing on contributing novel dynamic-algorithmic insights with attention to worst-case optimality, and the realisation of these algorithms in open-source software.
The project's duration is two years starting October 2016.
DFAQ is a joint research project between the Laboratory for Web and Information Technology at the Computer and Decision Engineering Department of the ULB and the Information Systems group at the Department of Computer Science of the University of Oxford. DFAQ is funded by the Wiener-Anspach foundation.