Volume 12, Number 3

HISTSFC: Optimization for ND Massive Spatial Points Querying

  Authors

Haicheng Liu¹, Peter van Oosterom¹, Martijn Meijers¹, Xuefeng Guan², Edward Verbree¹, Mike Horhammer³,
¹Delft University of Technology, the Netherlands, ²Wuhan University, China and ³Oracle Inc, USA

  Abstract

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-of- the-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.

  Keywords

Point Clouds, Histogram, Space Filling Curve, Benchmark, nD