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Department of Informatics Data Systems and Theory



  • Understand computational challenges for data processing, and
  • Design simple and scalable solutions towards these challenges.

Theoretical research includes the development of novel data processing algorithms and languages along with the analysis of their complexities.
Systems research is on building data systems in academia and industry based on well-understood theory.

Research Highlights

Current research projects


Factorised Databases

Principled approach to avoiding redundancy in the representation and computation of query results in relational databases


Machine Learning over Relational Data

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


Dynamic Analytical Workloads

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


Adaptive Dynamic Query Processing

Trade-offs in static and dynamic evaluation of conjunctive queries


Optimal Joins over Interval Data

Charting the tractability of Boolean conjunctive queries with intersection joins via equivalence to disjunctions of Boolean conjunctive queries with equality joins


In-Database Linear Algebra

Efficient and numerically accurate QR. SVD and PCA decompositions of matrices defined by joins over relational data

Past research projects

  • Probabilistic Databases
    • Book
    • SPROUT: Probabilistic Query Processing
    • MayBMS: Probabilistic Data Models and Query Languages
    • PPDL: Declarative Probabilistic Programming with Datalog
    • ENFrame: Probabilistic Programming
    • Pigora: Probabilistic Data Integration
  • Datalog Engines

Join us

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 ]