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
Current events
CIDR 2020
- 2020-01-12T00:00:00+01:00
- 2020-01-15T23:59:59+01:00
CIDR 2020
Start: 2020-01-12 00:00:00+01:00 End: 2020-01-15 23:59:59+01:00
CIDR 2020
January 12-15, 2020 Amsterdam, Netherlands
General information
The Conference on Innovative Data Systems Research (CIDR) is a systems-oriented conference, complementary in its mission to the mainstream database conferences like SIGMOD and VLDB, emphasizing the systems architecture perspective. CIDR was run every two years starting 2003, becoming the premier conference for practical data systems work. From 2020 on, the conference is organized every year, alternating between Amsterdam (even years) and the USA (odd years). The Amsterdam venue of the conference is the Mövenpick hotel on the waterfront of the Amsterdam city center.
CIDR will retain its characteristics, such as single-track keynote, paper and demo presentations by senior authors as well as evening sessions, including gong show, and plenty of time for interaction. CIDR gathers researchers and practitioners from both academia and industry to discuss the latest innovative and visionary ideas in the field. Papers are invited on novel approaches to data systems architecture and usage.
CIDR mainly encourages papers about innovative and risky data management system architecture ideas, systems-building experience and insight, resourceful experimental studies, provocative position statements. CIDR especially values innovation, experience-based insight, and vision. Authors are encouraged to submit their best systems work and are limited to submit at most two papers.
Conference Organization
General Chair
- Anastasia Ailamaki, EPFL
- Michael Stonebraker, MIT
Local Arrangement Chair
- Stefan Manegold CWI
Program Committee Chairs
- Peter Boncz, CWI
- Jignesh Patel, University of Wisconsin-Madison
- Thomas Neumann, TU Munich
Gong Show Organizer
- Andy Pavlo, CMU
Website Chair
- Ziawasch Abedjan, TU Berlin
Members
Associated Members
Publications
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Gubner, T.K, 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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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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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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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 CWI Research Grant
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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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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.