Auto-completion learning for XML

Serge Abiteboul, Yael Amsterdamer, Tova Milo, Pierre Senellart

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


Editing an XML document manually is a complicated task. While many XML editors exist in the market, we argue that some important functionalities are missing in all of them. Our goal is to makes the editing task simpler and faster. We present ALEX (Auto-completion Learning Editor for XML), an editor that assists the users by providing intelligent auto-completion suggestions. These suggestions are adapted to the user needs, simply by feeding ALEX with a set of example XML documents to learn from. The suggestions are also guaranteed to be compliant with a given XML schema, possibly including integrity constraints. To fulfill this challenging goal, we rely on novel, theoretical foundations by us and others, which are combined here in a system for the first time.

Original languageEnglish
Title of host publicationSIGMOD '12 - Proceedings of the International Conference on Management of Data
Number of pages4
StatePublished - 2012
Event2012 ACM SIGMOD International Conference on Management of Data, SIGMOD '12 - Scottsdale, AZ, United States
Duration: 21 May 201224 May 2012

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data


Conference2012 ACM SIGMOD International Conference on Management of Data, SIGMOD '12
Country/TerritoryUnited States
CityScottsdale, AZ


  • auto-completion
  • editor
  • learning
  • schema
  • xml

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems


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