DINCOMP

Hasselt University Data Science Institute

Data-Intensive
Computing

We study how to manage, query, and analyse data efficiently. Our research connects theoretical foundations with practical algorithms and systems, with a recurring focus on artificial intelligence and machine learning.

DINCOMP at UHasselt

Our research

From foundations to systems

Foundations & algorithms

Database theory, mathematical logic, complexity, and algorithms for working with data.

Data management & systems

Query processing, graph databases, data on the web, and distributed and decentralised data.

Data & machine learning

Data mining, explainable AI, and the interaction between data management and machine learning.

Watch & read

Recent talks

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TaDA Workshop · VLDB 2026

Research talk · 11:19

Hyperdimensional computing for structured querying on tabular data

Paper authors: Sebastián Bugedo, Stijn Vansummeren

Abstract · summary

Vector embeddings can retrieve similar table rows without revealing whether the nearest result actually satisfies a structured query. This work uses hyperdimensional computing, specifically holographic reduced representations, to encode rows and evaluate selection and projection in vector space. It derives expected similarity scores that support principled retrieval thresholds, including detection of queries with no matching rows. Experiments compare the approach with EmbDI on two real datasets and examine equality, non-equality and attribute projection.

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The Data Playbook · Dataminded

Conversation · 1:02:10

Can We Outsource Thinking? AI, Education, and the Future of Knowledge Work

In conversation: Frank Neven, Kris Peeters

Episode summary

Frank Neven and Kris Peeters discuss what AI can automate in data engineering, software development, research and education, and why understanding remains important when reasoning is delegated to AI. The conversation covers agentic systems, database research, structured data and semantics, human–AI collaboration and personal knowledge management. It considers how people can work effectively with AI while continuing to learn and judge its outputs.

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PODS 2026

Research talk · 14:08

A Logical View of GNN-Style Computation and the Role of Activation Functions

Paper authors: Pablo Barceló, Floris Geerts, Matthias Lanzinger, Klara Pakhomenko, Jan Van den Bussche

Abstract · summary

MPLang provides a declarative view of message-passing computation in graph neural networks. The study characterises the fragment without activation functions through walk-summed features and establishes conditions under which eventually constant activations have equal expressive power. With linear layers available, it proves that unbounded ReLU can express numerical queries that bounded, eventually constant activations cannot. These results clarify how the choice of activation function affects the computations a GNN can express.

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PODS 2026

Keynote · 1:06:56

Models are data too: Towards expressive querying of machine-learning models

Speaker: Jan Van den Bussche

Abstract · summary

Machine-learning workflows produce many models that need to be stored, managed and queried as data. This keynote explores how expressive, declarative query languages could support that task. It surveys existing approaches and open research questions, connecting model querying with the broader challenge of querying program code. The aim is to encourage database research on models as a distinct kind of data.

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The group

Our people

University profiles and member websites.

Professors

5 people

Emeriti

1 person

Postdocs

2 people

PhD students

8 people

Staff

1 person

Former members

9 people