FDB: Factorized Databases

Research positions (both PhD and postdoctoral) available in our research group! If you are intrigued by the kind of research we do, please consider applying. [ More Information ]

Our paper "Factorized Representations of Query Results: Size Bounds and Readability" (by Dan Olteanu and Jakub Závodný) received the ICDT 2022 Test of Time Award. This award recognizes a paper presented 10 years prior at the ICDT conference that has best met the "test of time" and had the highest impact in terms of research, methodology, conceptual contribution, or transfer to practice over the past decade.
News: St Cross College, University of Oxford; Department of Computer Science, University of Oxford

Maximilian Schleich was Highly Commended in the competition for the CPHC/BCS 2020 Distinguished Dissertation Award. Each year, BCS and the Conference of Professors and Heads of Computing (CPHC) award the best UK PhD/DPhil dissertations in computer science that stand out for their excellence.

Maximilian Schleich received an Honourable Mention for the 2021 SIGMOD Jim Gray Doctoral Dissertation Award for his DPhil dissertation entitled "Structure-Aware Learning over Multi-Relational Databases". The award recognizes excellent research by doctoral candidates in the database field.

LMFAO (Layered Multiple Functional Aggregate Optimisation) 1.0 is now released to the public. [ repository ]

The project moved from the University of Oxford to the University of Zurich.

Keynote on machine learning over relational data at VLDB 2020 in September 2020.

FBENCH 1.0 is now released to the public. [ repository ]

FIVM 1.0 is now released to the public. [ repository ]

Our paper entitled Counting Triangles under Updates in Worst-Case Optimal Time received the best paper award at ICDT 2019 .

Keynote on machine learning over relational data at ICDT 2019 in Lisbon in March 2019.

Efficient Algorithms

This course gives a unifying overview of the latest research in efficient computation over structured data, with applications spanning databases, artificial intelligence and machine learning, algorithms, and linear algebra. Besides their theoretical interest, algorithms overviewed in this course represent a key differentiator for commercial database and relational AI engines and thus essential knowledge for the future data system engineer.

Topics: (1) Unifying language and computation for: databases, constraint satisfaction problems, satisfiability, probabilistic graphical models, linear algebra matrix computation, gradients and cost functions for machine learning; (2) Commutative semirings; (3) Functional Aggregate Queries; (4) Decompositions; (5) Width measures; (6) Solving joins optimally; (7) Worst-case optimal size bounds for joins; (8) Solving SAT; (9) Solving functional aggregate queries; (10) Solving queries under updates.

From Joins to Aggregates and Optimization Problems

The course introduces recent development on solving a host of computational problems in the database. The unifying theme underlying this development is the use of the structure of the problem to avoid redundancy in data representation and computation.

slides part 1 part 2 part 3 , video