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
No vacancies currently.
News
Meet Turing Award winner Michael Stonebraker at ADS Meetup
In the wake of the Conference on Innovative Data Systems Research (CIDR), several of its high-profile participants will speak at a special public meetup “ADS Meets CIDR”.
CWI research collaboration leads to 100 million euro investment in Amsterdam
CWI’s research partner Databricks announced it will invest 100 million euros to expand its Amsterdam office. The company aims to grow to hundreds of R&D engineers. Databricks settled in Amsterdam nearly three years ago because of its research collaboration with CWI.
MonetDB Solutions secures investment from ServiceNow
MonetDB Solutions, a company formed by researchers at CWI, announces an investment from digital workflow company ServiceNow to help large enterprises drive digital transformation.
MonetDB Solutions ontvangt investering van ServiceNow
MonetDB Solutions, opgericht door onderzoekers van Centrum Wiskunde & Informatica (CWI), maakt vandaag bekend dat het een investering ontvangt van het Amerikaanse servicemanagement-bedrijf ServiceNow.
Members
Associated Members
Publications
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Sakr, S, Bonifati, A, Voigt, H, Iosup, A, Ammar, K, Angles Rojas, R, … Yoneki, E. (2021). The future is big graphs! A community view on graph processing systems. Communications of the ACM, 64(9). doi:10.1145/3434642
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Waudby, J, Steer, B.A, Karimov, K, Marton, J, Boncz, P.A, & Szárnyas, G. (2021). Towards testing ACID compliance in the LDBC Social Network Benchmark. In Proceedings of the 2020 Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2020 (pp. 1–17). doi:10.1007/978-3-030-84924-5_1
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Nerone, M, Holanda, P.T, de Almeida, E.C, & Manegold, S. (2021). Multidimensional adaptive & progressive indexes. In Proceedings of the IEEE International Conference on Data Engineering (ICDE). doi:10.1109/ICDE51399.2021.00060
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Gubner, T.K, Leis, V, & Boncz, P.A. (2021). Optimistically compressed Hash Tables & Strings in the USSR. ACM SIGMOD Record, 50(1).
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Holanda, P.T, & Manegold, S. (2021). Progressive Mergesort: Merging batches of appends into progressive indexes. In Proceedings of the International Conference on Extending Database Technology (pp. 481–486). doi:10.5441/002/edbt.2021.55
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Boncz, P.A. (2021). Technical perspective DIAMetrics: Benchmarking query engines at scale. SIGMOD Record, 50(1). doi:10.1145/3471485.3471491
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Ding, M, Chen, S, Makrynioti, K, & Manegold, S. (2021). Progressive join algorithms considering user preference.
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Makrynioti, K. (2020). Evaluation of query modifications in progressive data processing.
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Kruit, B.B, Boncz, P.A, & Urbani, J. (2020). Extracting N-ary Facts from Wikipedia Table Clusters. In International Conference on Information and Knowledge Management, Proceedings (pp. 655–664). doi:10.1145/3340531.3412027
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Ghit, B, Poggi, N, Rosen, J, Xin, R, & Boncz, P.A. (2020). SparkFuzz: Searching correctness regressions in modern query engines. In Proceedings of the Workshop on Testing Database Systems, DBTest 2020. doi:10.1145/3395032.3395327
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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Databricks CWI Research Agreement (Databricks)
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Databricks III
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Lokale Digitale Competentie centra (DCC) (DCC-NWO-I)
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Facebook Research Grant (Facebook)
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Research agreeement CWI - Databricks - vervolg contract (None)
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RelationalAI-CWI Research Grant Agreement (RelationalAI)
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Structure-aware Querying & Information Retrieval on Evolving Large Graphs (SQIREL-GRAPHS)
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Ursa-CWI Research Grant (Ursa)
Related partners
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Actian Corporation
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Databricks
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LIACS Institute
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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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WizeNoze B.V.
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Facebook
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Ursa Computing Inc.