Due to recent application push, there is high demand in industry to extend database systems to perform efficient and scalable in-database analytics based on probabilistic graphical models (PGMs). We discuss issues in supporting in-database PGM methods and present techniques to achieve a deep integration of the PGM methods into the relational data model as well as the query processing and optimization engine. This is an active research area and the techniques discussed are being further developed and evaluated.
Authors:
Daisy Zhe Wang, Yang Chen, Christan Grant, Kun Li
Bibtex:
@article{,
author = "Daisy Zhe Wang, Yang Chen, Christian Grant, Kun Li",
title = "Efficient In-Database Analytics with Graphical Models",
journal = "IEEE Data Engineering Bulletin",
year = "2014"
}
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