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 ]


April 2022

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

July 2021

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.

May 2020

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


May 2021, University of Zurich

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.

November 2018, University of Warsaw

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

Research Projects ( 26-minute video overview )

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Principles of Factorized Databases

Principled approach to avoiding redundancy in the representation and computation of query results in relational databases. Worst-case optimal computation of compressed, factorized representations for join query results.

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In-Database Analytics

Scalable techniques for machine learning over databases that exploit the relational structure, push the learning task inside the database query engine, and factorise its computation.

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Incremental Maintenance for Analytics

Unified framework for maintaining a wide range of analytics over databases that leverages factorization for underlying queries, output data representation, and bulk updates.

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Adaptive Query Processing based on Degree Information

We investigate trade-offs in static and dynamic evaluation of queries with arbitrary free variables. In the static setting, the trade-off is between the time to partially compute the query result and the delay needed to enumerate its tuples. In the dynamic setting, we additionally consider the time needed to update the query result under single-tuple inserts or deletes to the database.

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Ahmet Kara

Ahmet earned his PhD from the University of Dortmund in the area of Logics and Automata on data words and data trees and their applications in formal system verification. He contributes to the principles of factorized computation and its incremental maintenance.

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Nadia Knorozova

Nadia completed a BA in Computer Science from the University of Minnesota - Twin Cities, followed by an MSc in Computer Science at the University of Oxford. She also worked as software engineer in industry, for three years before starting her MSc and then again three years before starting her PhD at UZH in February 2021.

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Antonia Kormpa

Antonia earned her MSc in Computer Science and BSc in Computer Engineering and Informatics from University of Patras, Greece. She is a second year PhD student at the University of Oxford.

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Dan Olteanu

Dan earned his PhD from the University of Munich. He was a professor of Computer Science at the University of Oxford since 2007 before joining the University of Zurich in 2020. He is the head of the FDB project.

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Cédric Renggli

Cédric earned his PhD from ETH Zurich in 2022 on the topic of Building Data-Centric Systems for Machine Learning Development and Operations. He joined the group in autumn 2022.

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Nils Vortmeier

Nils earned his PhD from TU Dortmund University in 2019 on the topic of dynamic descriptive complexity theory. He joined the group in autumn 2020.

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Haozhe Zhang

Haozhe earned his MSc in Computer Science from Oxford. He is currently a third year PhD student and contributes to incremental maintenance of analytical workloads.

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Djordje Zivanovic

Djordje Zivanovic earned his MSc in Electrical engineering and Computer Science and BSc in Electrical engineering from University of Belgrade, Serbia. He is a second year PhD student and contributes to in-database linear algebra.