The Study and Design of QR*-Tree Spatial Indexing Structure

Abstract

In this study, we will discuss a fast spatial indexing structure called QR*-tree based on R*-tree and quad-tree. Now, R*-tree and R-tree are widely used in spatial database as a spatial indexing structure, But for each algorithm alone, it is not suitable for the huge data volume. The hybrid structure that we proposed is composed of many R*-trees based on space partitioned by quad-tree. Although it demands more storage space than R*-tree or quad-tree, it gains better performance in insertion, deletion, and searching especially, and the more the amount of spatial data is, the better performance the hybrid-tree has.

STAQR Tree indexing for Spatial Temporal data with Altitude

“…A hierarchical data structure of Quad tree contains spatial data in two-dimensional coordinates. A quad tree with root, every internal node contains four children (NE,NW,SW,SE) and every node of Quad tree represents a rectangular area [2]. These are knowledge structure which encodes two dimensional space into required exible cells.…”