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
CWI en Databricks lanceren samenwerking in aanwezigheid van minister Kamp
Op 25 april 2017 lanceren CWI en het big data analyse- en data science software-bedrijf Databricks een nieuwe samenwerking. Dat gebeurt bij de Hannover Messe, in aanwezigheid van Minister Kamp van Economische Zaken.
CWI & Databricks: Big Data in Amsterdam
Amsterdam wants to play a leading international role in the development of data science research. In Big Data Amsterdam, Financieele Dagblad journalist Job Woudt interviews Amsterdam Data Science researchers on the functioning of the ecosystem where companies and knowledge institutions in Amsterdam collaborate in the area of Big Data.
Making a tedious search a breeze: parallel query execution in multi-core systems
When you request information from a huge database, you might want to grab a cup of coffee and sit back, because your request may take a while. How can we optimize searches for information, specifically in a multi-core processing unit? The answer may lie in parallel queries, which run simultaneously on different processors.
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).
Current events
New national bi-weekly seminar: The Dutch Seminar on Data Systems Design (DSDSD)
- 2021-03-26T16:15:00+01:00
- 2021-03-26T17:45:00+01:00
New national bi-weekly seminar: The Dutch Seminar on Data Systems Design (DSDSD)
Start: 2021-03-26 16:15:00+01:00 End: 2021-03-26 17:45:00+01:00
CWI has initiated a new national bi-weekly seminar called the Dutch Seminar on Data Systsems Design (DSDSD).
DSDSD is co-organized with TUE, TU Delft, and Uva and brings together researchers in data systems. The format is a 90-minute session with two talks, on Friday late afternoon; so DSDSD marks the start of weekend, but is time-wise also friendly for US based speakers. In each seminar, there is one invited speaker from abroad and one local speaker. There have been two seminars already, this Friday is the third.
The website is
https://dsdsd.da.cwi.nl
(here people can get on the mailing list, which distributes the zoom links) and DSDSD also tweets at https://twitter.com/dsdsdnl
We have had two succesful seminars so far, the first with Semih Salihoglu (University of Waterloo) and Hannes Muehleisen (CWI) and the second with Andy Pavlo (Carnegie Mellon University) and Peter Boncz. Friday is the third, with Viktor Leis (University of Erlangen) and Sam Ansmink (CWI). In these seminars, we see 60-70 attendees so far. In the following weeks there will be talks also by TUE and TU Delft.
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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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.), .
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Kipf, A, Vorona, D., Müller, J, Kipf, T, Radke, B, Leis, V, … Kemper, A. (2019). Estimating cardinalities with deep sketches. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 1937–1940). doi:10.1145/3299869.3320218
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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Data Mining on High Volume Simulation Output (DAMIOSO)
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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.