Data-Intensive Computing

The overarching theme of the Data-Intensive Computing research group concerns the computational methods and techniques required to analyze, manage, query, and process data in an efficient and meaningful way. A central and recurring focus in this work is the interaction with artificial intelligence and machine learning.

Within this broad theme, we conduct research on both the theoretical foundations and the development of efficient algorithms and concrete systems. We adopt a very broad interpretation of "data" and "data processing," encompassing relational, object-oriented, geometric, geographic, temporal, and graph-based databases; time series and sequential data; data on the web; personal data management; distributed and decentralized data; data mining; data management for machine learning; machine learning for data management; programs viewed as data; concurrency control; and more.

The group is also active in related areas of theoretical computer science (mathematical logic, complexity theory, algorithms), artificial intelligence (knowledge representation), and bioinformatics, including DNA computing. 

Algorithms, Complexity, Concurrency control, Data Engineering, Data management for machine learning (DB4ML) and machine learning for data management (ML4DB), Data mining, Data on the Web, Database systems, Database theory, Explainable AI and fairness, Geographical and temporal database systems, Logic

Stijn VANSUMMEREN

• Liese BEKKERS
• Erik BOLLEN
• Jeroen BOLLEN
• Sebastián BUGEDO BUGEDO
• Niels BYLOIS
• larissa CAPOBIANCO SHIMOMURA
• Len FEREMANS
• Mark GERARTS
• Maxime JAKUBOWSKI
• Arthur JANSEN
• Sarah KLEEST-MEISSNER
• Bart KUIJPERS
• Heba MOHAMED
• Thomas MUNOZ SERRANO
• Frank NEVEN
• Klara PAKHOMENKO
• Dante PINTO ARAYA
• Roel SPRIMONT
• Dore STAQUET
• Juno STEEGMANS
• Leandro TABARES MARTIN
• Olaf VAN BYLEN
• Jan VAN DEN BUSSCHE
• Brecht VANDEVOORT
• Stijn VANSUMMEREN

Publications

More info