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
Martin Kersten receives 2016 SIGMOD Systems Award
Martin Kersten is the recipient of the 2016 SIGMOD Systems Award for the design and implementation of MonetDB, a pioneering main-memory database system based on a columnar data organization.
CWI spin-off MonetDB Solutions helps building ExaNeSt platform
In a new EU project MonetDB Solutions helps to showcase the future generation ExaNeSt platform, which consists of up to 10 million of low-cost and low-energy ARM processors. The CWI spin-off will evaluate the platform with extremely compute-intensive big data analytics applications.
Efficient method to make changes in compressed database
PhD student Sándór Héman from CWI developed a new method to compress a large database, allowing for a much faster transport of data from storage to processor. Furthermore he developed efficient algorithms to make changes within such a compressed storage layout.
Tracing Stars with MonetDB in the Cloud
The Dutch database technology company MonetDB Solutions and CWI will develop new techniques to facilitate the storage and analysis of the vast data volumes from the Square Kilometre Array (SKA) telescope. The project will be funded by a grant in the AstroCompute in the Cloud program of the SKA Organization (SKAO) and Amazon Web Services (AWS).
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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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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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
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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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