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Department of Informatics Data Systems and Theory

DaST Research

Research Mission

  • 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 Projects

  • Adaptive Query Processing
    Adaptive query processing combining worst-case optimal join algorithms and reinforcement learning
  • Foundations of Intersection Joins
    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
  • 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
  • Factorised Databases
    Principled approach to avoiding redundancy in the representation and computation of query results in relational databases

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