A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
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A probabilistic relational model and algebra
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ProbView: a flexible probabilistic database system
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Lore: a database management system for semistructured data
ACM SIGMOD Record
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ACM Transactions on Database Systems (TODS)
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
ACM Transactions on Database Systems (TODS)
Probabilistic question answering on the web
Proceedings of the 11th international conference on World Wide Web
Strong Conditional Independence for Credal Sets
Annals of Mathematics and Artificial Intelligence
The Management of Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
Database Support for Problematic Knowledge
EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
The Theory of Probabilistic Databases
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Parametric Approach to Deductive Databases with Uncertainty
LID '96 Proceedings of the International Workshop on Logic in Databases
Semistructured Probabilistic Databases
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Stereo Depth Estimation: A Confidence Interval Approach
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Annotated XML: queries and provenance
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Loopy Propagation in a Probabilistic Description Logic
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
On the expressiveness of probabilistic XML models
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
Aggregate queries for discrete and continuous probabilistic XML
Proceedings of the 13th International Conference on Database Theory
Capturing continuous data and answering aggregate queries in probabilistic XML
ACM Transactions on Database Systems (TODS)
AN EFFICIENT REPRESENTATION MODEL OF DISTANCE DISTRIBUTION BETWEEN UNCERTAIN OBJECTS
Computational Intelligence
ELCA evaluation for keyword search on probabilistic XML data
World Wide Web
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Interest in XML databases has been expanding rapidly over the last few years. In this paper, we study the problem of incorporating probabilistic information into XML databases. We propose the Probabilistic Interval XML (PIXML for short) data model in this paper. Using this data model, users can express probabilistic information within XML markups. In addition, we provide two alternative formal model-theoretic semantics for PIXML data. The first semantics is a “global” semantics which is relatively intuitive, but is not directly amenable to computation. The second semantics is a “local” semantics which supports efficient computation. We prove several correspondence results between the two semantics. To our knowledge, this is the first formal model theoretic semantics for probabilistic interval XML. We then provide an operational semantics that may be used to compute answers to queries and that is correct for a large class of probabilistic instances.