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
International innovation award to Martin Kersten
Martin Kersten, Research fellow at the Centrum Wiskunde & Informatica (CWI) is awarded the 2014 SIGMOD Edgar F. Codd Innovations Award, the most prestigious prize for researchers who have made innovative and significant contributions to database systems and databases.
Database Indexes for the Big Data Era
Researchers of Centrum Wiskunde & Informatica (CWI) in Amsterdam have developed new methods to index huge databases. These space efficient indexes are lightweight to build and maintain and grow less than linear with the size of the data sets.
CWI in Europees onderzoek precisielandbouw
Steeds vaker worden aardobservatiedata door organisaties als ESA en NASA publiek beschikbaar gesteld. In combinatie met andere gegevens kan deze data tot nieuwe waardevolle toepassingen leiden.
CWI in European research project precision farming
Increasingly, remote sensing data is made publicly available by organizations such as ESA and NASA. Based on these data, and in combination with other sources, new valuable applications can be created.
Members
Associated Members
Publications
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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.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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Tomé, D.G, Kepe, T.R, Alves, M.A.Z, & de Almeida, E.C. (2018). Near-Data Filters: Taking Another Brick from the Memory Wall.
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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
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Angles Rojas, R, Arenas, M, Barceló, P, Boncz, P.A, Fletcher, G, Gutierrez, C, … Voigt, H. (2018). G-CORE a core for future graph query languages. In Proceedings of the ACM SIGMOD International Conference on Management of data.
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Raasveldt, M. (2018). MonetDBLite: An embedded analytical database. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 1837–1838). doi:10.1145/3183713.3183722
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Gubner, T.K. (2018). Designing an adaptive VM that combines vectorized and JIT execution on heterogeneous hardware. In 2018 IEEE 34th International Conference on Data Engineering, ICDE 2018. doi:10.1109/ICDE.2018.00215
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Holanda, P.T.T, Raasveldt, M, & Kersten, M.L. (2017). Don’t Keep My UDFs Hostage - Exporting UDFs For Debugging Purposes. In Proceedings Brazilian Symposium on Databases.
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Raasveldt, M, & Mühleisen, H.F. (2017). Don't hold my data hostage - A case for client protocol redesign. In Proceedings of the VLDB Endowment (pp. 1022–1033).
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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Capturing the Laws of Data Nature
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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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LAD: Layered Astronomical Databases (LAD)
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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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Spinque
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Tata Steel
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WizeNoze B.V.