Model for the prediction of R-tree performance

Theodoridis, Yannis; Sellis, Timos

dc.contributor.author	Theodoridis, Yannis	en
dc.contributor.author	Sellis, Timos	en
dc.date.accessioned	2014-03-01T02:41:09Z
dc.date.available	2014-03-01T02:41:09Z
dc.date.issued	1996	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30393
dc.subject	Analytical Model	en
dc.subject	Cost Model	en
dc.subject	Indexation	en
dc.subject	Query Optimization	en
dc.subject	Range Query	en
dc.subject	Spatial Database	en
dc.subject	Uniform Distribution	en
dc.subject	Relative Error	en
dc.subject.other	Database systems	en
dc.subject.other	Errors	en
dc.subject.other	Estimation	en
dc.subject.other	Mathematical models	en
dc.subject.other	Numerical analysis	en
dc.subject.other	Optimization	en
dc.subject.other	Performance	en
dc.subject.other	Data set	en
dc.subject.other	Query optimizer	en
dc.subject.other	R tree	en
dc.subject.other	Range query	en
dc.subject.other	Data structures	en
dc.title	Model for the prediction of R-tree performance	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/237661.237705	en
heal.identifier.secondary	http://dx.doi.org/10.1145/237661.237705	en
heal.publicationDate	1996	en
heal.abstract	In this paper we present an analytical model that predicts the performance of R-trees (and its variants) when a range query needs to be answered. The cost model uses knowledge of the dataset only, i.e., the proposed formula that estimates the number of disk accesses is a function of data properties, namely, the amount of data and their density in the work space. In other words, the proposed model is applicable even before the construction of the R-tree index, a fact that makes it a useful tool for dynamic spatial databases. Several experiments on synthetic and real datasets show that the proposed analytical model is very accurate, the relative error being usually around 10%-15%, for uniform and non-uniform distributions. We believe that this error is involved with the gap between efficient R-tree variants, like the R*-tree, and an optimum, not implemented yet, method. Our work extends previous research concerning R-tree analysis and constitutes a useful tool for spatial query optimizers that need to evaluate the cost of a complex spatial query and its execution procedure.	en
heal.publisher	ACM, New York, NY, United States	en
heal.journalName	Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems - PODS	en
dc.identifier.doi	10.1145/237661.237705	en
dc.identifier.spage	161	en
dc.identifier.epage	171	en