On the connections between relational and XML probabilistic data models

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

Abstract

A number of uncertain data models have been proposed, based on the notion of compact representations of probability distributions over possible worlds. In probabilistic relational models, tuples are annotated with probabilities or formulae over Boolean random variables. In probabilistic XML models, XML trees are augmented with nodes that specify probability distributions over their children. Both kinds of models have been extensively studied, with respect to their expressive power, compactness, and query efficiency, among other things. Probabilistic database systems have also been implemented, in both relational and XML settings. However, these studies have mostly been carried out independently and the translations between relational and XML models, as well as the impact for probabilistic relational databases of results about query complexity in probabilistic XML and vice versa, have not been made explicit: we detail such translations in this article, in both directions, study their impact in terms of complexity results, and present interesting open issues about the connections between relational and XML probabilistic data models.

Original languageEnglish
Title of host publicationBig Data - 29th British National Conference on Databases, BNCOD 2013, Proceedings
Pages121-134
Number of pages14
DOIs
Publication statusPublished - 15 Jul 2013
Event29th British National Conference on Databases, BNCOD 2013 - Oxford, United Kingdom
Duration: 8 Jul 201310 Jul 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7968 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th British National Conference on Databases, BNCOD 2013
Country/TerritoryUnited Kingdom
CityOxford
Period8/07/1310/07/13

Keywords

  • XML
  • probabilistic data
  • relational data

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