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
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).
CWI develops new tools for data exploration
PhD student Thibault Sellam from CWI developed new tools to explore large databases. It allows explorative users to find interesting information hidden inside a database, with little or no knowledge of the data beforehand.
Members
Associated Members
Publications
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Gubner, T.K, 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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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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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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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.
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Holanda, P.T. (2018). Progressive indices: Indexing without prejudice. In Proceedings of the VLDB 2018 PhD Workshop co-located with the 44th International Conference on Very Large Databases (VLDB 2018).
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Holanda, P.T, Nerone, M, de Almeida, E.C, & Manegold, S. (2018). Cracking KD-Tree: The first multidimensional adaptive indexing. In DATA 2018 (pp. 393–399). doi:10.5220/0006944203930399
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Lang, H, Kipf, A, Passing, L.K, Boncz, P.A, Neumann, T, & Kemper, A. (2018). Make the most out of your SIMD investments: Counter control flow divergence in compiled query pipelines. In 14th International Workshop on Data Management on New Hardware, DaMoN 2018. doi:10.1145/3211922.3211928
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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Actian CWI Research Grant
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RelationalAI-CWI Research Agreement
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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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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.