2014Unpublished venueRequires access

Comparative Analysis of R-Tree and R -Tree in Spatial Database

S. Srividhya, S.R. Lavanya

Open publisher page 2 citations

Abstract

A spatial preference query ranks the objects based on the quality of features in their spatial neighborhood. There are several indexing techniques used in the past for storage and retrieval of spatial data. In this approach, suitable indexing techniques has been applied for multi dimensional spatial objects. A spatial model has been designed using R Tree and R+-Tree, with real estate dimensional spaces using spatial objects where data is organized in the form of three dimensional grid. The experimental results proved that R+-Tree algorithm outperforms R-Tree in ranking the spatial objects when compared to R- tree algorithm.

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What this paper is about

A spatial preference query ranks the objects based on the quality of features in their spatial neighborhood. There are several indexing techniques used in the past for storage and retrieval of spatial data. In this approach, suitable indexing techniques has been applied for multi dimensional spatial objects. A spatial model has been designed using R Tree and R+-Tree, with real estate dimensional spaces using spatial objects where data is organized in the form of three dimensional grid. The experimental results proved that R+-Tree algorithm outperforms R-Tree in ranking the spatial objects when compared to R- tree algorithm.

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Available abstract

A spatial preference query ranks the objects based on the quality of features in their spatial neighborhood. There are several indexing techniques used in the past for storage and retrieval of spatial data. In this approach, suitable indexing techniques has been applied for multi dimensional spatial objects. A spatial model has been designed using R Tree and R+-Tree, with real estate dimensional spaces using spatial objects where data is organized in the form of three dimensional grid. The experimental results proved that R+-Tree algorithm outperforms R-Tree in ranking the spatial objects when compared to R- tree algorithm.

Key concepts: Search engine indexing, R-tree, Computer science, Tree (set theory), Ranking (information retrieval), Spatial analysis, Data mining, Spatial database

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