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
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).
Hannes Mühleisen in media about insecure wifi in trains
Hannes Mühleisen, a postdoc researcher at CWI, recently found that he could see too much information from the wifi network in NS trains, which are passing close by his home - a houseboat in Amsterdam near Central Station. The network information is not encrypted.
Veni grants for Daniel Dadush and Hannes Mühleisen
The Netherlands Organisation for Scientific Research (NWO) has awarded Veni grants to Daniel Dadush and Hannes Mühleisen of CWI. The funding allows these researchers, who have recently obtained their PhD, to conduct independent research and develop their ideas for a period of three years.
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.