A Parallel N-Dimensional Space-Filling Curve Library and Its Application in Massive Point Cloud Management

Xuefeng Guan,Bo Cheng,Peter van Oosterom

doi:10.3390/ijgi7080327

Abstract

Because of their locality preservation properties, Space-Filling Curves (SFC) have been widely used in massive point dataset management. However, the completeness, universality, and scalability of current SFC implementations are still not well resolved. To address this problem, a generic n-dimensional (nD) SFC library is proposed and validated in massive multiscale nD points management. The library supports two well-known types of SFCs (Morton and Hilbert) with an object-oriented design, and provides common interfaces for encoding, decoding, and nD box query. Parallel implementation permits effective exploitation of underlying multicore resources. During massive point cloud management, all xyz points are attached an additional random level of detail (LOD) value l. A unique 4D SFC key is generated from each xyzl with this library, and then only the keys are stored as flat records in an Oracle Index Organized Table (IOT). The key-only schema benefits both data compression and multiscale clustering. Experiments show that the proposed nD SFC library provides complete functions and robust scalability for massive points management. When loading 23 billion Light Detection and Ranging (LiDAR) points into an Oracle database, the parallel mode takes about 10 h and the loading speed is estimated four times faster than sequential loading. Furthermore, 4D queries using the Hilbert keys take about 1~5 s and scale well with the dataset size.

Highlights

Space-Filling Curves (SFC) map a compact interval to a multidimensional space by passing through every point of the space
Since currently no complete and scalable nD Space-Filling Curve library is available for massive point indexing and query retrieval, we propose and implement a generic nD SFC library and apply it to manage massive multiscale Light Detection and Ranging (LiDAR) point clouds
The SFC Library is designed in consideration of object-oriented programming and encapsulates the SFC-related functions, encoding/decoding/query, into abstract classes

Summary

Introduction

Space-Filling Curves (SFC) map a compact interval to a multidimensional space by passing through every point of the space. They exhibit good locality preservation properties that make them useful for partitioning or reordering data and computations [1,2]. SFCs have been proven a useful solution for massive points management [11]. It first maps spatial data objects into one-dimensional (1D) values and indexes those values using a one-dimensional indexing technique, typically the B-tree. SFC-based indexing requires only mapping functions and incurs no additional efforts in current databases compared with conventional spatial index structures

Results

Discussion

Conclusion