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
PhD Students in the areas of Big Data Management and Analysis Architectures and Data Science Engineering Technologies
Three fully funded PhD positions are available to work under the direction of prof. Stefan Manegold on big data management technology with a particular focus on hardware-conscious data structures and algorithms in distributed and cloud environments as well as the integration of data mining and machine learning into large-scale data management systems.
News
Meet Turing Award winner Michael Stonebraker at ADS Meetup
In the wake of the Conference on Innovative Data Systems Research (CIDR), several of its high-profile participants will speak at a special public meetup “ADS Meets CIDR”.
CWI research collaboration leads to 100 million euro investment in Amsterdam
CWI’s research partner Databricks announced it will invest 100 million euros to expand its Amsterdam office. The company aims to grow to hundreds of R&D engineers. Databricks settled in Amsterdam nearly three years ago because of its research collaboration with CWI.
MonetDB Solutions secures investment from ServiceNow
MonetDB Solutions, a company formed by researchers at CWI, announces an investment from digital workflow company ServiceNow to help large enterprises drive digital transformation.
MonetDB Solutions ontvangt investering van ServiceNow
MonetDB Solutions, opgericht door onderzoekers van Centrum Wiskunde & Informatica (CWI), maakt vandaag bekend dat het een investering ontvangt van het Amerikaanse servicemanagement-bedrijf ServiceNow.
Members
Associated Members
Publications
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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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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
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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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Raasveldt, M, & Mühleisen, H.F. (2019). [Demo] DuckDB: An embeddable analytical database. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 1981–1984). doi:10.1145/3299869.3320212
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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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Kersten, M.L, Koutsourakis, P, Manegold, S, & Zhang, Y. (2019). SQALPEL: A database performance platform. In CIDR 2019 - 9th Biennial Conference on Innovative Data Systems Research.
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Lang, H, Neumann, T, Kemper, A, & Boncz, P.A. (2019). Performance-Optimal Filtering:Bloom Overtakes Cuckoo at High Throughput. Proceedings of the VLDB Endowment, 502–515. doi:10.14778/3303753.3303757
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Mühleisen, H.F, Bertram, A, & Kallen, M-J. (2018). Database-inspired optimizations for statistical analysis. Journal of Statistical Software, 87(4). doi:10.18637/jss.v087.i04
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Kyzirakos, K, Savva, D, Vlachopoulos, I, Vasileiou, A, Karalis, N, Koubarakis, M, & Manegold, S. (2018). GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings. Journal of Web Semantics. doi:10.1016/j.websem.2018.08.003
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Pham, M.-D. (2018, September 6). Emergent relational schemas for RDF (No. 2018-19). SIKS Dissertation Series.
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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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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Structure-aware Querying & Information Retrieval on Evolving Large Graphs (SQIREL-GRAPHS)
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