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 Lectures on database research
This year’s edition of CWI Lectures revolved around database pioneer and CWI fellow Martin Kersten. Several international speakers discussed recent advances in their database research. Video recordings of the event are now available to view.
Snowflake’s co-founder Marcin Żukowski reflects on his time at CWI
“I knew it had amazing potential”, says Snowflake co-founder Marcin Żukowski, looking back on his time as a PhD researcher at CWI. In his PhD thesis, published in 2009, Żukowski described the blueprint of what would become two key technologies currently used by data storage and analytics provider Snowflake.
CWI PhD graduate founded record IPO company Snowflake
Data-warehouse company Snowflake went public this week, reaching an extraordinary market value of $70.4 billion, the largest IPO for a software company ever. Snowflake offers cloud-based data warehousing, whose data storage and query engine contain techniques pioneered in CWI’s Data Architectures group. One of the PhD graduates who developed that technology, former CWI researcher Marcin Żukowski, is co-founder of Snowflake.
Integrating data science and relational systems
Data scientists have largely overlooked relational database systems, even though these could greatly help their research. The systems did not gain traction, because combining them with databases proved to be slow and cumbersome. Now CWI researcher Mark Raasveldt bridges that gap. In his thesis, he proposes several novel techniques that make database management systems easier to use and more efficient.
Members
Associated Members
Publications
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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
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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.
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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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