VLDB '07 Proceedings of the 33rd international conference on Very large data bases
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Generating XML structure using examples and constraints
Proceedings of the VLDB Endowment
SnipSuggest: context-aware autocompletion for SQL
Proceedings of the VLDB Endowment
Efficient reasoning about data trees via integer linear programming
Proceedings of the 14th International Conference on Database Theory
Generating, sampling and counting subclasses of regular tree languages
Proceedings of the 14th International Conference on Database Theory
Finding optimal probabilistic generators for XML collections
Proceedings of the 15th International Conference on Database Theory
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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.