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
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).
Members
Associated Members
Publications
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Raasveldt, M, & Mühleisen, H.F. (2020). Data Management for Data Science - Towards Embedded Analytics. In Proceedings of the Conference on Innovative Data Systems Research.
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Holanda, P.T, Raasveldt, M, Manegold, S, & Mühleisen, H.F. (2019). Progressive Indexes: Indexing for interactive data analysis. Proceedings of the VLDB Endowment, 12(13), 2366–2378. doi:10.14778/3358701.3358705
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Kruit, B.B, Boncz, P.A, & Urbani, J. (2019). Extracting novel facts from tables for Knowledge Graph completion. In Proceedings of ISWC 2019 (pp. 364–381). doi:10.1007/978-3-030-30793-6_21
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Lang, H, Passing, L.K, Kipf, A, Boncz, P.A, Neumann, T, & Kemper, A. (2019). Make the most out of your SIMD investments: counter control flow divergence in compiled query pipelines. VLDB Journal. doi:10.1007/s00778-019-00547-y
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Holanda, P.T, & Mühleisen, H.F. (2019). Relational queries with a tensor processing unit. In Proceedings of the 15th International Workshop on Data Management on New Hardware. doi:10.1145/3329785.3329932
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Uta, A, Ghit, B, Dave, A, & Boncz, P.A. (2019). [Demo] Low-latency spark queries on updatable data. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 2009–2012). doi:10.1145/3299869.3320227
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De Leo, D, & Boncz, P.A. (2019). Packed Memory Arrays – Rewired. In Proceedings of the IEEE International Conference on Data Engineering (ICDE) (pp. 830–841). doi:10.1109/ICDE.2019.00079
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Raasveldt, M, Holanda, P.T, & Manegold, S. (2019). DevUDf: Increasing UDF development efficiency through IDE integration. It works like a PyCharm!. In Proceedings of the International Conference on Extending Database Technology (pp. 558–561). doi:10.5441/002/edbt.2019.55
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Kipf, A, Kipf, T, Radke, B, Leis, V, Boncz, P.A, & Kemper, A. (2019). Learned cardinalities: Estimating correlated joins with deep learning. In CIDR 2019 - 9th Biennial Conference on Innovative Data Systems Research.
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Bereta, K, Koubarakis, M, Manegold, S, Stamoulis, G, & Demir, B. (2018). From big data to big information and big knowledge: The case of Earth observation data. In Proceedings of the International Conference on Information and Knowledge Management (CIKM) (pp. 2293–2294). doi:10.1145/3269206.3274270
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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Min.VWS Bestrijding Covid-19
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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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The SciLens-II Infrastructure, Big Data at work (Scilens-II)
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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
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Ministerie van Volksgezondheid, Welzijn en Sport