F-IVM: Learning over Fast-Evolving Relational Data

Milos Nikolic, Haozhe Zhang, Ahmet Kara, Dan Olteanu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

F-IVM is a system for real-time analytics such as machine learning applications over training datasets defined by queries over fast evolving relational databases. We will demonstrate F-IVM for three such applications: model selection, Chow-Liu trees, and ridge linear regression.
Original languageEnglish
Title of host publicationProceedings of the 2020 ACM International Conference on Management of Data
PublisherACM
Number of pages4
DOIs
Publication statusPublished - 31 May 2020
Event2020 ACM SIGMOD/PODS International Conference on Management of Data - Portland, United States
Duration: 14 Jun 202019 Jun 2020
https://sigmod2020.org/

Conference

Conference2020 ACM SIGMOD/PODS International Conference on Management of Data
Abbreviated titleSIGMOD/PODS 2020
Country/TerritoryUnited States
CityPortland
Period14/06/2019/06/20
Internet address

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