Description
Leader of the group Database Architectures: Stefan Manegold.
We, the Database Architectures (DA) research group of CWI, are well known as a leading data systems research group, active in the broad area of analytical database management systems. Our research group has a strong international reputation in academia and industry for pioneering column store technology, fast compression methods, vectorized query execution, indexes for interactive data analysis, and analytical in-process database systems. We have spawned multiple spin-off companies (Data Distilleries, VectorWise, MonetDB Solutions, and DuckDB Labs). We pride ourselves on revealing the real problems in our discipline and coming up with revolutionary solutions that are frequently ahead of their time.
Vacancies
No vacancies currently.
News
Martin Kersten receives 2016 SIGMOD Systems Award
Martin Kersten is the recipient of the 2016 SIGMOD Systems Award for the design and implementation of MonetDB, a pioneering main-memory database system based on a columnar data organization.
CWI spin-off MonetDB Solutions helps building ExaNeSt platform
In a new EU project MonetDB Solutions helps to showcase the future generation ExaNeSt platform, which consists of up to 10 million of low-cost and low-energy ARM processors. The CWI spin-off will evaluate the platform with extremely compute-intensive big data analytics applications.
Efficient method to make changes in compressed database
PhD student Sándór Héman from CWI developed a new method to compress a large database, allowing for a much faster transport of data from storage to processor. Furthermore he developed efficient algorithms to make changes within such a compressed storage layout.
Tracing Stars with MonetDB in the Cloud
The Dutch database technology company MonetDB Solutions and CWI will develop new techniques to facilitate the storage and analysis of the vast data volumes from the Square Kilometre Array (SKA) telescope. The project will be funded by a grant in the AstroCompute in the Cloud program of the SKA Organization (SKAO) and Amazon Web Services (AWS).
Members
Associated Members
Publications
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Hinkel, G, Garcia-Dominguez, A, Schöne, R, Boronat, A, Tisi, M, Le Calvar, T, … Szárnyas, G. (2021). A cross-technology benchmark for incremental graph queries. Software and Systems Modeling. doi:10.1007/s10270-021-00927-5
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Holanda, P.T. (2021, September 21). Progressive indexes. SIKS Dissertation Series.
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Gubner, T.K, & Boncz, P.A. (2021). Charting the design space of query execution using VOILA. In Proceedings of the VLDB Endowment (pp. 1067–1097). doi:10.14778/3447689.3447709
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Gubner, T.K, & Boncz, P.A. (2021). Highlighting the performance diversity of analytical queries using VOILA. In Proceedings of the International Workshop on Accelerating Analytics and Data management Systems Using Modern Processor and Storage Architectures.
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Szárnyas, G, Bader, D.A, Davis, T.A, Kitchen, J, Mattson, T.G, McMillan, S, & Welch, E. (2021). LAGraph: Linear algebra, network analysis libraries, and the study of graph algorithms. In IEEE International Parallel and Distributed Processing Symposium Workshops (pp. 243–252). doi:10.1109/IPDPSW52791.2021.00046
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Mhedhbi, A, Lissandrini, M, Kuiper, L.N, Waudby, J, & Szárnyas, G. (2021). LSQB: A large-scale subgraph query benchmark. Proceedings of the ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), 8.1–8.11. doi:10.1145/3461837.3464516
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Kruit, B.B, Boncz, P.A, & Urbani, J. (2021). TAKCO: A platform for extracting novel facts from tables. In Companion of the World Wide Web Conference (pp. 705–707). doi:10.1145/3442442.3458611
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Lang, H, Beischl, A, Leis, V, Boncz, P.A, Neumann, T, & Kemper, A. (2020). Tree-Encoded Bitmaps. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 937–967). doi:10.1145/3318464.3380588
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Raasveldt, M. (2020, June 9). Integrating analytics with relational databases. SIKS Dissertation Series.
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Ghita, B, Gomes Tomé, D, & Boncz, P.A. (2020). White-box compression: Learning and exploiting compact table representations. In Proceedings of the Conference on Innovative Data Systems Research.
Software
MonetDB: high-performance query processing against very large databases
MonetDB is a relational database management system (DBMS) providing high performance on complex queries against large databases.
Current projects with external funding
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RelationalAI-CWI Research Agreement ()
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Actian Research Grant II (ACTIAN II)
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Cross-Industry Predictive Maintenance Optimization Platform (CIMPLO)
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Databricks CWI Research Agreement (Databricks)
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Databricks III
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Lokale Digitale Competentie centra (DCC) (DCC-NWO-I)
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Facebook Research Grant (Facebook)
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Velox Optimizations and supporting new file formats (Meta)
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Research agreeement CWI - Databricks - vervolg contract (None)
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RelationalAI-CWI Research Grant Agreement (RelationalAI)
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Structure-aware Querying & Information Retrieval on Evolving Large Graphs (SQIREL-GRAPHS)
Related partners
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Actian Corporation
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Databricks
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LIACS Institute
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Neo Technology AB
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OBI4wan B.V.
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RelationalAI
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Spinque
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WizeNoze B.V.
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Facebook
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Meta Platforms Inc