Description
Leader of the group Database Architectures: Stefan Manegold.
We, the Database Architectures (DA) research group of CWI, are well known as a top data systems research group, active in the broad area of data (management) systems and infrastructure for supporting data science. Our research group has a strong international reputation in academia and industry for pioneering column store technology, fast compression methods, vectorized query execution, on-line query-driven indexing (cracking), adaptive caching, and integration of statistical languages and analysis in database management systems.
We develop, distribute and maintain the MonetDB open-source system, and we have spawned multiple spin-off companies, including Data Distilleries, VectorWise and MonetDB Solutions. Our team also operates a self-built cluster, SciLens, that – unlike many other computer clusters – is bandwidth-optimized and thus better suited as a data-science infrastructure. 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 appointed ACM Fellow
CWI fellow Martin Kersten has been appointed as one of the 2016 fellows of the Association of Computing Machinery (ACM).
CWI ontwikkelt nieuwe technieken voor verkennen data
CWI develops new tools for data exploration
PhD student Thibault Sellam from CWI developed new tools to explore large databases. It allows explorative users to find interesting information hidden inside a database, with little or no knowledge of the data beforehand.
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.
Members
Associated Members
Publications
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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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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.
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Kipf, A, Lang, H, Pandey, V.N, Persa, R.A, Anneser, C, Zacharatou, E.T, … Kemper, A. (2020). Adaptive main-memory indexing for high-performance point-polygon joins. In Advances in Database Technology - EDBT (pp. 347–358). doi:10.5441/002/edbt.2020.31
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Kruit, B.B, He, H., H, & Urbani, J. (2020). Tab2Know: Building a Knowledge Base from Tables in Scientific Papers. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence. doi:10.1007/978-3-030-62419-4_20
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Gubner, T.K, Gomes Tomé, D, Lang, H, & Boncz, P.A. (2019). Fluid co-processing: GPU Bloom-filters for CPU joins. In Proceedings of the 15th International Workshop on Data Management on New Hardware (pp. 9:1–9:10). doi:10.1145/3329785.3329934
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De Leo, D, & Boncz, P.A. (2019). Fast concurrent reads and updates with PMAs. In Proceedings of the Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) 2019. doi:10.1145/3327964.3328497
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Boncz, P.A, Manegold, S, Ailamaki, A, Deshpande, A, & Kraska, T (Eds.). (2019). Proceedings of the 2019 International Conference on Management of Data. In P.A Boncz, S Manegold, A Ailamaki, A Deshpande, & T Kraska (Eds.), .
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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Lokale Digitale Competentie centra (DCC) (DCC-NWO-I)
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Facebook Research Grant (Facebook)
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Research agreeement CWI - Databricks - vervolg contract (None)
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Process mining for multi-objective online control (PROMIMOOC)
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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)
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Ursa-CWI Research Grant (Ursa)
Related partners
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Actian Corporation
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BMW Munich
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Databricks
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LIACS Institute
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MonetDB B.V.
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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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Tata Steel
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WizeNoze B.V.
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Facebook
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Ursa Computing Inc.