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 & 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).
Current events
1st International Conference on Big Scientific Data Management (BigSDM2018)
- 2018-11-30T08:30:00+01:00
- 2018-12-01T18:30:00+01:00
1st International Conference on Big Scientific Data Management (BigSDM2018)
Start: 2018-11-30 08:30:00+01:00 End: 2018-12-01 18:30:00+01:00
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CALL FOR PAPERS
1st International Conference on Big Scientific Data Management (BigSDM2018)
http://bigsdm2018.csdata.org
Nov. 30 - Dec. 1, 2018, Beijing, China
Submission Deadline: September 15th, 2018 (Abstract)
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The 1st International Conference on Big Scientific Data Management (BigSDM 2018) will be held in Beijing, China from Nov. 30th to Dec 1st, 2018. The Conference is jointly organized by Chinese Academy of Sciences, Peking University, Tsinghua University, Renmin University of China, CWI and MonetDB Solutions.
The theme of BigSDM 2018 is data-driven scientific discovery. The conference aims at bring together international experts from natural and computer sciences to explore the life-cycle management of Big Scientific Data, to discuss major methodologies and key technologies in Big Data-driven discovery making, to exchange experience and knowledge about Big Scientific Data across various fields through specific cases, and to identify the challenges and opportunities in scientific discovery acceleration. We hope to, through the conference, gather ideas for fostering new ways of scientific thinking, and promote big data sciences at large.
The program of BigSDM 2018 features several keynote talks by famous experts in the important areas of big data sciences. We have invited Barry C. Barish, the winner of 2017 Nobel Prize in Physics, Huadong Guo who is an Academician of Chinese Academy of Sciences, and Alexander S. Szalay, the bloomberg distinguished professor from the Johns Hopkins University to share their cutting edge technology and views about the big scientific data management. In the meantime, we are about to invite the principal researchers from major scientific experiments, such as the Five- hundred-meter Aperture Spherical radio Telescope (FAST), and major data projects, such as 'CASEarth' in China to share their experiences in data-driven scientific discovery. 'CASEarth' is a CAS Strategic Pioneering Research and Development Programme, focusing on building a global big data network to study Earth and support research on climate change, as well as to predict and mitigate natural disasters. The other events include topic-specific forums and lightning talks.
BigSDM 2018 welcomes researchers, professionals, practitioners, and officers worldwide in related areas to submit papers and attend the conference.
Members
Associated Members
Publications
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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.
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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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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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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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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.
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.