文章基本信息

标题：HISTSFC: Optimization for ND Massive Spatial Points Querying
本地全文：下载
作者：Haicheng Liu ; Peter van Oosterom ; Martijn Meijers 等
期刊名称：International Journal of Database Management Systems
印刷版ISSN：0975-5985
电子版ISSN：0975-5705
出版年度：2020
卷号：12
期号：3
页码：7-28
DOI：10.5121/ijdms.2020.12302
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Space Filling Curve (SFC) mapping-based clustering and indexing works effectively for point clouds management and querying. It maps both points and queries into a one-dimensional SFC space so that B - tree could be utilized. Based on the basic structure, this paper develops a generic HistSFC approach which utilizes a histogram tree recording point distribution for efficient querying. The goal is to resolve the issue of skewed data querying. Besides, the paper proposes an agile method to compute a continuous Level of Detail (cLoD), and integrates it into HistSFC to support smooth rendering of massive points. Results indicate that for range queries, HistSFC decreases the False Positive Rate (FPR) of selection by maximally 80%, compared to previous approaches. It also performs significantly faster than the state-ofthe-art Oracle SDO_PC solution. With improved performance on visualization and k Nearest Neighbour (kNN) search, HistSFC can therefore be used as a new standard solution.
关键词：Point Clouds;Histogram;Space Filling Curve;Benchmark;nD