Volume 12, Number 3

HISTSFC: Optimization for ND Massive Spatial Points Querying


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


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.


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