DaST Research

• 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.

• SIGMOD Research Highlights Award 2025 for information-theoretic cardinality estimation
• Gems of PODS 2024  "Recent Increments in Incremental View Maintenance"
• Factorised Databases workshop 2022
• ICDT 2022 Test of Time Award for factorised databases
• ICDT 2019 Best Paper Award for worst-case optimal dynamic algorithm for counting triangles
• Distinguished papers at: PODS 2024 (cardinality estimation), VLDB 2024 (foundation models for databases), ICDT 2023 (maintenance for queries with access patterns), OOPSLA 2022 (analytics over semiring dictionaries), PODS 2019 (functional aggregate queries with additive inequalities), PODS 2018 (in-database learning with sparse tensors), ICDT 2016 (declarative probabilistic programming), EDBT 2014 (clustering uncertain data)
• Honorable Mention to the 2021 SIGMOD Jim Gray Doctoral Dissertation Award for Maximilian Schleich
• Software LMFAO and F-IVM
• Keynotes on machine learning over relational data at VLDB 2020 and EDBT/ICDT 2019

• Cardinality Estimation with Guaranteed Upper Bounds
  Cardinality estimation approach by translating the problem into linear optimization with entropy maximization constrained by Shannon inequalities and ℓp-norms on join column degree sequences.

• Incremental View Maintenance
  Update/query time trade-offs and fine-grained complexity analysis for dynamic problems

• Adaptive Query Processing
  Adaptive query processing combining worst-case optimal join algorithms and reinforcement learning

• Fact Attribution in Database Query Answering
  Complexity analysis and algorithms for computing Shapley/Banzhaf values

• 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

• Real-Time Analytics over Fast and Continuously Evolving Relational Data
  Unified framework for maintaining a wide range of analytics over databases that leverages factorization for underlying queries, output data representation, and bulk updates

• The Complexity of Incremental View Maintenance
  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

• Scalable Distributed Systems for Agent-based Simulations

• Flexible Querying of Knowledge Graphs using LLMs

• 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