Cost models for join queries in spatial databases

Theodoridis, Yannis; Stefanakis, Emmanuel; Sellis, Timos

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