Scaling Probabilistic Databases

Probabilistic databases, which have been widely studied over the past years, lie at the expressive intersection of databases and probabilistic graphical models, thus aiming to provide efficient support for the evaluation of probabilistic queries over uncertain, relational data. Several Machine Learning approaches, on the one hand, have recently investigated the issue of distributed probabilistic inference but do not support relational data and SQL. Conventional database engines, on the other hand, do not handle probabilistic data and queries, nor any form of uncertain data management. With this project, we aim to fill this prevalent gap between the two fields of Databases and Machine Learning by scaling probabilistic databases to a distributed setting, which is a topic that so far has not been addressed in the literature. The proposed PhD dissertation topic provides a number of intriguing and challenging aspects, both from a theoretical and a systems-engineering perspective.