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
Snowflake’s co-founder Marcin Żukowski reflects on his time at CWI
“I knew it had amazing potential”, says Snowflake co-founder Marcin Żukowski, looking back on his time as a PhD researcher at CWI. In his PhD thesis, published in 2009, Żukowski described the blueprint of what would become two key technologies currently used by data storage and analytics provider Snowflake.
CWI PhD graduate founded record IPO company Snowflake
Data-warehouse company Snowflake went public this week, reaching an extraordinary market value of $70.4 billion, the largest IPO for a software company ever. Snowflake offers cloud-based data warehousing, whose data storage and query engine contain techniques pioneered in CWI’s Data Architectures group. One of the PhD graduates who developed that technology, former CWI researcher Marcin Żukowski, is co-founder of Snowflake.
Integrating data science and relational systems
Data scientists have largely overlooked relational database systems, even though these could greatly help their research. The systems did not gain traction, because combining them with databases proved to be slow and cumbersome. Now CWI researcher Mark Raasveldt bridges that gap. In his thesis, he proposes several novel techniques that make database management systems easier to use and more efficient.
Stefan Manegold wins 2020 SIGMOD Contributions Award
CWI researcher Stefan Manegold has been awarded the 2020 SIGMOD Contributions Award. The award recognizes his innovative work in the data management community to encourage scientific reproducibility.
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.