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
Hardman new chair Amsterdam Data Science, Boncz joins Supervisory Board
As of January 2018, CWI researcher Lynda Hardman is the new chair of the management team of Amsterdam Data Science. Peter Boncz, CWI researcher and part of the CWI management team, will join the ADS Supervisory Board.
CWI Database Architecture researchers awarded with best paper runner up award at SSDBM
Researchers of CWI's Database Architecture group have been awarded with a best paper runner up award at 29th International Conference on Scientific and Statistical Database Management (SSDBM 2017) in Chicago. The researchers received the award for their paper 'Multi Hypothesis CSV-parsing' by authors Till Döhmen, Hannes Mühleisen and Peter Boncz.
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.
Members
- George Anastasiou
- Jayme Bird
- Peter Boncz
- Georgiana Ciocirdel
- Richard Gankema
- Tim Gubner
- Pedro Holanda
- Laura Ionel
- Radu Jica
- Martin Kersten
- Benno Kruit
- Dean De Leo
- Stefan Manegold
- Tina Marjanov
- Sjoerd Mullender
- Hannes Mühleisen
- Niels Nes
- Mark Raasveldt
- Marta Radziszewska
- Nina Stijepovic
- Diego Tomé
- Mihai Varga
Associated Members
Publications
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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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Kipf, A, Lang, H, Pandey, V.N, Persa, R, Boncz, P.A, Neumann, T, & Kemper, A. (2018). Adaptive geospatial joins for modern hardware.
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Boncz, P.A, Anatiotis, A.-C, & Kläbe, S. (2017). JCC-H: Adding Join Crossing Correlations with skew to TPC-H. In Performance Evaluation and Benchmarking for the Analytics Era (pp. 103–119). doi:10.1007/978-3-319-72401-0_8
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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).
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Sidirourgos, E, & Mühleisen, H.F. (2017). Scaling column imprints using advanced vectorization. Presented at the Thirteenth International Workshop on Data Management on New Hardware. doi:10.1145/3076113.3076120
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Rozenberg, E, & Boncz, P.A. (2017). Faster across the PCIe bus: A GPU library for lightweight decompression including support for patched compression schemes. In Proceedings of the International Workshop on Data Management on New Hardware. doi:10.1145/3076113.3076122
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CWI gaat samenwerking aan met Databricks - executive-people.nl - 29-04-2017. (2017). CWI gaat samenwerking aan met Databricks - executive-people.nl - 29-04-2017.
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Database-onderzoek en vooruitlopen op de toekomst - computable.nl - 25-04-2017. (2017). Database-onderzoek en vooruitlopen op de toekomst - computable.nl - 25-04-2017.
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CWI en Databricks gaan samenwerken - engineerslonline.nl - 25-04-2017. (2017). CWI en Databricks gaan samenwerken - engineerslonline.nl - 25-04-2017.
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Database-onderzoek en voorlopen op de toekomst - Computable - 31-03-2017. (2017). Database-onderzoek en voorlopen op de toekomst - Computable - 31-03-2017.
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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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)
Related partners
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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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Tata Steel