Highly scalable memory-efficient parallel LDA in a shared-nothing MPP database
US9317809B1 · kind B1 · utility
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Key dates
| Filing date | Sep 25, 2013 |
| Grant date | Apr 19, 2016 |
| Priority date | — |
| Expiry date | Apr 5, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Latent Dirichlet allocation (LDA) analysis on a dataset is performed on an MPP relational database by distributing subsets of said dataset to a plurality of segments of the MPP database, and performing LDA analysis in parallel on the respective subsets on the plurality of segments using Gibbs sampling. An object library on each segment provides executable objects of user defined functions that can be called by an SQL query when the query requires functionality provided by an object.
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