Auto-completion learning for XML

Serge Abiteboul 1, 2 Yael Amsterdamer 3 Tova Milo 3 Pierre Senellart 4
1 DAHU - Verification in databases
LSV - Laboratoire Spécification et Vérification [Cachan], ENS Cachan - École normale supérieure - Cachan, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8643
Abstract : 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.
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https://hal.inria.fr/hal-00765552
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Submitted on : Friday, December 14, 2012 - 5:51:30 PM
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Serge Abiteboul, Yael Amsterdamer, Tova Milo, Pierre Senellart. Auto-completion learning for XML. SIGMOD Conference, May 2012, Scottsdale, United States. pp.669-672. ⟨hal-00765552⟩

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