XIRQL: a query language for information retrieval in XML documents
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Querying and ranking XML documents
Journal of the American Society for Information Science and Technology - XML
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
XRANK: ranked keyword search over XML documents
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A Probabilistic XML Approach to Data Integration
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Structure and content scoring for XML
VLDB '05 Proceedings of the 31st international conference on Very large data bases
On the complexity of managing probabilistic XML data
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ProTDB: probabilistic data in XML
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
XSEarch: a semantic search engine for XML
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Ranking queries on uncertain data: a probabilistic threshold approach
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Query efficiency in probabilistic XML models
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Incorporating constraints in probabilistic XML
Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sliding-window top-k queries on uncertain streams
Proceedings of the VLDB Endowment
Efficiently Answering Probabilistic Threshold Top-k Queries on Uncertain Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient Processing of Top-k Queries in Uncertain Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Twiglist: make twig pattern matching fast
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Querying and updating probabilistic information in XML
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Top-K probabilistic closest pairs query in uncertain spatial databases
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
A hybrid algorithm for finding top-k twig answers in probabilistic XML
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Evaluating probabilistic spatial-range closest pairs queries over uncertain objects
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Matching top-k answers of twig patterns in probabilistic XML
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Keywords filtering over probabilistic XML data
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Bayesian network-based probabilistic XML keywords filtering
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
ELCA evaluation for keyword search on probabilistic XML data
World Wide Web
Querying and ranking incomplete twigs in probabilistic XML
World Wide Web
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Twig queries have been extensively studied as a major fragment of XPATH queries to query XML data. In this paper, we study PXML-RANK query, (Q, k), which is to rank top-k probabilities of the answers of a twig query Q in probabilistic XML (PXML) data. A new research issue is how to compute top-k probabilities of answers of a twig query Q in PXML in the presence of containment (ancestor/descendant) relationships. In the presence of the ancestor/descendant relationships, the existing dynamic programming approaches to rank top-k probabilities over a set of tuples cannot be directly applied, because any node/edge in PXML may have impacts on the top-k probabilities of answers. We propose new algorithms to compute PXML-RANK queries efficiently and give conditions under which a PXML-RANK query can be processed efficiently without enumeration of all the possible worlds. We conduct extensive performance studies using both real and large benchmark datasets, and confirm the efficiency of our algorithms.