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
Members
Associated Members
Publications
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Gubner, T.K, Leis, V, & Boncz, P.A. (2021). Optimistically compressed Hash Tables & Strings in the USSR. ACM SIGMOD Record, 50(1).
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Ding, M, Chen, S, Makrynioti, K, & Manegold, S. (2021). Progressive join algorithms considering user preference.
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Makrynioti, K. (2020). Evaluation of query modifications in progressive data processing.
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Kruit, B.B, Boncz, P.A, & Urbani, J. (2020). Extracting N-ary Facts from Wikipedia Table Clusters. In International Conference on Information and Knowledge Management, Proceedings (pp. 655–664). doi:10.1145/3340531.3412027
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Ghit, B, Poggi, N, Rosen, J, Xin, R, & Boncz, P.A. (2020). SparkFuzz: Searching correctness regressions in modern query engines. In Proceedings of the Workshop on Testing Database Systems, DBTest 2020. doi:10.1145/3395032.3395327
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Fuchs, P, Boncz, P.A, & Ghit, B. (2020). Edgeframe: Worst-case optimal joins for graph-pattern matching in spark. In Proceedings of the 3rd ACM SIGMOD Joint International Workshop on Graph Data Management Experiences and Systems and Network Data Analytics, GRADES-NDA 2020. doi:10.1145/3398682.3399162
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Gubner, T.K, Leis, V, & Boncz, P.A. (2020). Efficient query processing with Optimistically Compressed Hash Tables & Strings in the USSR. In Proceedings of the IEEE International Conference on Data Engineering (ICDE) (pp. 301–312). doi:10.1109/ICDE48307.2020.000
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Raasveldt, M, & Mühleisen, H.F. (2020). Data Management for Data Science - Towards Embedded Analytics. In Proceedings of the Conference on Innovative Data Systems Research.
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Gomes Tomé, D, & Boncz, P.A. (2020). Redesigning query engines for white-box compression. In CEUR Workshop Proceedings.
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Holanda, P.T, Raasveldt, M, Manegold, S, & Mühleisen, H.F. (2019). Progressive Indexes: Indexing for interactive data analysis. Proceedings of the VLDB Endowment, 12(13), 2366–2378. doi:10.14778/3358701.3358705
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.