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Cost models for join queries in spatial databases

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dc.contributor.author Theodoridis, Yannis en
dc.contributor.author Stefanakis, Emmanuel en
dc.contributor.author Sellis, Timos en
dc.date.accessioned 2014-03-01T02:41:32Z
dc.date.available 2014-03-01T02:41:32Z
dc.date.issued 1998 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/30515
dc.subject Analytical Model en
dc.subject Cost Model en
dc.subject Multidimensional Data en
dc.subject Query Optimization en
dc.subject Spatial Data Structure en
dc.subject Spatial Database en
dc.subject Data Base Management System en
dc.subject.other Data structures en
dc.subject.other Indexing (of information) en
dc.subject.other Mathematical models en
dc.subject.other Optimization en
dc.subject.other Trees (mathematics) en
dc.subject.other Join queries en
dc.subject.other Multidimensional data en
dc.subject.other R trees en
dc.subject.other Spatial databases en
dc.subject.other Query languages en
dc.title Cost models for join queries in spatial databases en
heal.type conferenceItem en
heal.identifier.primary 10.1109/ICDE.1998.655810 en
heal.identifier.secondary http://dx.doi.org/10.1109/ICDE.1998.655810 en
heal.publicationDate 1998 en
heal.abstract The join query is one of the fundamental operations in Data Base Management Systems (DBMSs). Modern DBMSs should be able to support non-traditional data, including spatial objects, in an efficient manner. Towards this goal, spatial data structures can be adopted in order to support the execution of join queries on sets of multidimensional data. This paper introduces analytical models that estimate the cost (in terms of node or disk accesses) of join queries involving two multidimensional indexed data sets using R-tree-based structures. In addition, experimental results are presented, which show the accuracy of the analytical estimations when compared to actual runs on both synthetic and real data sets. It turns out that the relative error rarely exceeds 15% for all combinations, a fact that makes the proposed cost models useful tools for efficient spatial query optimization. en
heal.publisher IEEE Comp Soc, Los Alamitos, CA, United States en
heal.journalName Proceedings - International Conference on Data Engineering en
dc.identifier.doi 10.1109/ICDE.1998.655810 en
dc.identifier.spage 476 en
dc.identifier.epage 483 en


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