Finding optimal probabilistic generators for XML collections

Serge Abiteboul*, Yael Amsterdamer, Daniel Deutch, Tova Milo, Pierre Senellart

*Corresponding author for this work

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


We study the problem of, given a corpus of XML documents and its schema, finding an optimal (generative) probabilistic model, where optimality here means maximizing the likelihood of the particular corpus to be generated. Focusing first on the structure of documents, we present an efficient algorithm for finding the best generative probabilistic model, in the absence of constraints. We further study the problem in the presence of integrity constraints, namely key, inclusion, and domain constraints. We study in this case two different kinds of generators. First, we consider a continuation-test generator that performs, while generating documents, tests of schema satisfiability; these tests prevent from generating a document violating the constraints but, as we will see, they are computationally expensive. We also study a restart generator that may generate an invalid document and, when this is the case, restarts and tries again. Finally, we consider the injection of data values into the structure, to obtain a full XML document. We study different approaches for generating these values.

Original languageEnglish
Title of host publicationDatabase Theory - ICDT 2012
Subtitle of host publication15th International Conference on Database Technology, Proceedings
Number of pages13
StatePublished - 2012
Event15th International Conference on Database Theory, ICDT 2012 - Berlin, Germany
Duration: 26 Mar 201229 Mar 2012

Publication series

NameACM International Conference Proceeding Series


Conference15th International Conference on Database Theory, ICDT 2012


  • Constraints
  • Generator
  • Probabilistic model
  • Schema
  • XML


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