On probabilistic fixpoint and Markov chain query languages

Daniel Deutch, Christoph Koch, Tova Milo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We study highly expressive query languages such as datalog, fixpoint, and while-languages on probabilistic databases. We generalize these languages such that computation steps (e.g. datalog rules) can fire probabilistically. We define two possible semantics for such query languages, namely inflationary semantics where the results of each computation step are added to the current database and noninflationary queries that induce a random walk in-between database instances. We then study the complexity of exact and approximate query evaluation under these semantics.

Original languageEnglish
Title of host publicationPODS'10 - Proceedings of the 29th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems
Pages215-225
Number of pages11
DOIs
StatePublished - 2010
Event29th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2010 - Indianapolis, IN, United States
Duration: 6 Jun 201011 Jun 2010

Publication series

NameProceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems

Conference

Conference29th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2010
Country/TerritoryUnited States
CityIndianapolis, IN
Period6/06/1011/06/10

Keywords

  • Markov chains
  • probabilistic fixpoint

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