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
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).
Hannes Mühleisen in media about insecure wifi in trains
Hannes Mühleisen, a postdoc researcher at CWI, recently found that he could see too much information from the wifi network in NS trains, which are passing close by his home - a houseboat in Amsterdam near Central Station. The network information is not encrypted.
Veni grants for Daniel Dadush and Hannes Mühleisen
The Netherlands Organisation for Scientific Research (NWO) has awarded Veni grants to Daniel Dadush and Hannes Mühleisen of CWI. The funding allows these researchers, who have recently obtained their PhD, to conduct independent research and develop their ideas for a period of three years.
Big Data at High Performance
Computer hardware systems have evolved from monolithic machines in which each component performed one specific task, to complex systems with a wide range of heterogeneous components such as spinning disks, SSDs, RAM, and CPUs.
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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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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Ontwikkelaars A1 spreken zich uit tegen killer robots - DeIngenieur.nl - 19-07-18. (2018). Ontwikkelaars A1 spreken zich uit tegen killer robots - DeIngenieur.nl - 19-07-18.
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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
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.