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
CWI Database Architecture researchers awarded with best paper runner up award at SSDBM
Researchers of CWI's Database Architecture group have been awarded with a best paper runner up award at 29th International Conference on Scientific and Statistical Database Management (SSDBM 2017) in Chicago. The researchers received the award for their paper 'Multi Hypothesis CSV-parsing' by authors Till Döhmen, Hannes Mühleisen and Peter Boncz.
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.
Current events
CIDR 2023
- 2023-01-08T00:00:00+01:00
- 2023-01-11T23:59:59+01:00
CIDR 2023
Start: 2023-01-08 00:00:00+01:00 End: 2023-01-11 23:59:59+01:00
CIDR 2023
January 8-11, 2023 Amsterdam, Netherlands
Conference Officers
General Chairs
- Anastasia Ailamaki, EPFL
- Peter Boncz, CWI
Program Committee Chairs
- Fatma Ozcan, Google
- Thomas Neumann, TUM
- Carlo Curino, Microsoft
Local Arrangement Chair
- Stefan Manegold, CWI
Website Chair
- Ziawasch Abedjan, Leibniz University Hannover
Gong Show Master
- Andy Pavlo, CMU
Members
Associated Members
Publications
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Kohn, A, Moritz, D, Raasveldt, M, Mühleisen, H.F, & Neumann, T. (2022). DuckDB-Wasm: Fast analytical processing for the Web. In Proceedings of the VLDB Endowment (Vol. 15, pp. 3574–3577). doi:https://www.vldb.org/pvldb/vol15/p3574-kohn.pdf
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Kuiper, L.N, Raasveldt, M, & Mühleisen, H.F. (2022). Efficient external sorting in DuckDB. In Proceedings of BICOD 2021/2022 (pp. 40–45).
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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
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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Responsible Decentralized Data Architecture (RDDA)
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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