Large Spatial Database Indexing with aX-tree-猫眼课题宝

DOI：

10.32628/cseit1833236

发表时间：

2018-03

期刊：

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其他

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作者：

Grace L. Samson;L. Joan;Mistura M. Usman;Aminat A. Showole;Hadeel Jazzaa Hadeel-Hadeel-Jazzaa-Hadeel-115196734

空间数据库针对基于其几何空间存储的数据的管理进行了优化。研究人员通过高度的可扩展性，提出了几种空间索引结构，以达到这一效果。在这些索引结构中，有一种是X树。现有的X树及其变体是为动态环境设计的，具有处理插入和删除的能力。尽管如此，X树的检索性能下降，随着维数的增加，并带来了较差的最坏情况下的性能比顺序扫描。本文提出了一种新的静态空间数据库X树填充技术，通过谨慎的填充，提高了空间利用率。这种新的改进结构产生了两个基本优点：它减少了索引的空间开销，并产生了更好的响应时间，因为aX树具有更高的扇出，因此树总是更短。提出了一种新的超级节点构造模型和一种改进的str批量装载技术的有效排样方法。研究表明，该系统的性能优于许多现有的空间索引结构

Spatial databases are optimized for the management of data stored based on their geometric space. Researchers through high degree scalability have proposed several spatial indexing structures towards this effect. Among these indexing structures is the X-tree. The existing X-trees and its variants are designed for dynamic environment, with the capability for handling insertions and deletions. Notwithstanding, the X-tree degrades on retrieval performance as dimensionality increases and brings about poor worst-case performance than sequential scan. We propose a new X-tree packing techniques for static spatial databases which performs better in space utilization through cautious packing. This new improved structure yields two basic advantage: It reduces the space overhead of the index and produces a better response time, because the aX-tree has a higher fan-out and so the tree always ends up shorter. New model for super-node construction and effective method for optimal packing using an improved str bulk-loading technique is proposed. The study reveals that proposed system performs better than many existing spatial indexing structures

Large Spatial Database Indexing with aX-tree

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