On the complexity of managing probabilistic XML data
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On the expressiveness of probabilistic XML models
The VLDB Journal — The International Journal on Very Large Data Bases
Qualitative effects of knowledge rules and user feedback in probabilistic data integration
The VLDB Journal — The International Journal on Very Large Data Bases
Query evaluation over probabilistic XML
The VLDB Journal — The International Journal on Very Large Data Bases
Answering queries using views over probabilistic XML: complexity and tractability
Proceedings of the VLDB Endowment
Demonstrating ProApproX 2.0: a predictive query engine for probabilistic XML
Proceedings of the 21st ACM international conference on Information and knowledge management
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We demonstrate a system for querying probabilistic XML documents with simple XPath queries. A user chooses between a variety of query answering techniques, both exact and approximate, and observes the running behavior, pros, and cons, of each method, in terms of efficiency, precision of the result, and data model and query language supported.