A Scalable, Distributed Infrastructure for Probabilistic Databases.

Probabilistic databases lie at the intersection of databases and probabilistic graphical models. Our past work in this field started at Stanford University more than 6 years ago with the development of the Trio probabilistic database system. Still today, probabilistic databases provide an emerging field of research with many interesting and yet unexplored aspects.With this proposal, we motivate for the exploration of a new, distributed and scalable, infrastructure for probabilistic databases. Rather than building a full-fledged database engine from scratch, we motivate for the specific investigation of how existing approaches (including our own prior works) can be adapted to a distributed setting in order to accelerate both the data management and the probabilistic inference via parallel query evaluations for an SQL-like environment.Currently, there exists no distributed probabilistic database system. Machine Learning approaches, on the one hand, have previously investigated distributed probabilistic inference but do not support SQL. Current distributed database engines, on the other hand, do not handle probabilistic inference or any form of uncertain data management. With this project, we aim to fill this gap between Databases and Machine Learning approaches that so far has not been investigated in the literature. We believe that the proposed topic provides a number of intriguing and challenging aspects for a PhD thesis, both from a theoretical and from a systems-engineering perspective.

Keywords:DATA MANAGEMENT, PROBABILISTIC DATABASES, INFORMATION TECHNOLOGY

Disciplines:Applied mathematics in specific fields, Artificial intelligence, Computer architecture and networks, Distributed computing, Information sciences, Information systems, Programming languages, Scientific computing, Theoretical computer science, Visual computing, Other information and computing sciences, Cognitive science and intelligent systems