A probabilistic relational model and algebra
ACM Transactions on Database Systems (TODS)
A probabilistic relational algebra for the integration of information retrieval and database systems
ACM Transactions on Information Systems (TOIS)
ProbView: a flexible probabilistic database system
ACM Transactions on Database Systems (TODS)
On supporting containment queries in relational database management systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Storing and querying ordered XML using a relational database system
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Holistic twig joins: optimal XML pattern matching
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
ICDT '03 Proceedings of the 9th International Conference on Database Theory
Indexing and Querying XML Data for Regular Path Expressions
Proceedings of the 27th International Conference on Very Large Data Bases
Extension of the Relational Algebra to Probabilistic Complex Values
FoIKS '00 Proceedings of the First International Symposium on Foundations of Information and Knowledge Systems
Structural Joins: A Primitive for Efficient XML Query Pattern Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A Prime Number Labeling Scheme for Dynamic Ordered XML Trees
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
BLAS: an efficient XPath processing system
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
PEPX: a query-friendly probabilistic XML database
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Efficient structural joins on indexed XML documents
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
ProTDB: probabilistic data in XML
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Matching twigs in probabilistic XML
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Query efficiency in probabilistic XML models
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Incorporating constraints in probabilistic XML
ACM Transactions on Database Systems (TODS)
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
Querying and updating probabilistic information in XML
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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Recently there is a growing interest in the data model and query processing for probabilistic XML data. There are many potential applications of probabilistic data, and the XML data model is suitable to represent hierarchical information and data uncertainty of different levels naturally. However, the previously proposed probabilistic XML data models and query processing techniques separate finding data matches with evaluating the probabilities of results. Therefore, they should repeatedly access the data and need to get full data of paths given in queries to calculate the probabilities of results. In this paper, we propose an extended interval-based labeling scheme for the probabilistic XML data tree and an efficient query processing procedure using the labeling scheme. Against previous researches, our method accesses only the labels of data specified in queries and finds data matches simultaneously with evaluating the probability of each data match. Also, we present an extended probabilistic XML query model with the predicates for the values of probabilities and a lightweight index for those probabilities in order to eliminate unnecessary access to data that will not be included in results. Experimental results show that our approach is efficient in probabilistic XML query processing and our index scheme significantly improves the performance of query processing when the predicates for the values of probabilities are given.