Inference Optimization using Relational Algebra

S. Evers, M.M. Fokkinga, Peter M.G. Apers

Research output: Book/ReportReportProfessional

38 Downloads (Pure)

Abstract

Exact inference procedures in Bayesian networks can be expressed using relational algebra; this provides a common ground for optimizations from the AI and database communities. Specifically, the ability to accomodate sparse representations of probability distributions opens up the way to optimize for their cardinality instead of the dimensionality; we apply this in a sensor data model.
Original languageUndefined
Place of PublicationEnschede
PublisherDatabases (DB)
Number of pages13
Publication statusPublished - Aug 2009

Publication series

NameCTIT Technical Report Series
PublisherCentre for Telematics and Information Technology, University of Twente
No.TR-CTIT-09-38
ISSN (Print)1381-3625

Keywords

  • Probabilistic inference
  • IR-68553
  • Bayesian Networks
  • EWI-16524
  • METIS-265240
  • Relational algebra
  • Sensor data

Cite this

Evers, S., Fokkinga, M. M., & Apers, P. M. G. (2009). Inference Optimization using Relational Algebra. (CTIT Technical Report Series; No. TR-CTIT-09-38). Enschede: Databases (DB).