An overview of query optimization in relational systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
RAL: An Algebra for Querying RDF
World Wide Web
Adapting to source properties in processing data integration queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Concept-based querying in mediator systems
The VLDB Journal — The International Journal on Very Large Data Bases
An efficient SQL-based RDF querying scheme
VLDB '05 Proceedings of the 31st international conference on Very large data bases
XSEED: Accurate and Fast Cardinality Estimation for XPath Queries
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
SPARQL query optimization on top of DHTs
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
The comparison between histogram method and index method in selectivity estimation
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
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An effective, accurate algorithm for cardinality estimation of queries on ontology models of data is presented. The algorithm relies on the decomposition of queries into query pattern paths, where each path produces a set of values for each variable within the result form of the query. In order to estimate the total number of result set parameters for each path, a set of statistics is compiled on the properties of the ontology. Experimental analysis has shown that the algorithm produces estimates with high accuracy and with high correlation to actual values. Thus, this algorithm can be used as the cornerstone of an effective optimization strategy for queries on diverse, heterogeneous data sources modeled as ontologies.