Research on Storage and Processing of MongoDB for Laser Point Cloud under Distribution

DOI

10.2991/icmemtc-16.2016.299How to use a DOI?

Keywords

Distribution MongoDB Laser point cloud Sharding cluster Range based partitioning Hash based partitioning

Abstract

In recent years, the application of laser point cloud data has increased dramatically. How to efficiently store and fast process the data becomes an important research direction at present. Point cloud data contain a wealth of geographic information, belonging to the category of spatial data. Traditional relational databases are relatively weak in massive spatial data storage and processing, while the application of non-relational databases provides a new perspective of study for this fact. Sharding technology is a solution for database level extension. In this thesis, sharding cluster for MongoDB is established under distributed environment, while distributed storage, spatial query and MapReduce operation test for numerous laser-point cloud data will be implemented through scope sharding and Hash-based sharding, which completely reflects huge advantages of MongoDB under distribution in storage and processing for spatial data.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xudong Xu
AU  - Rui Guo
PY  - 2016/04
DA  - 2016/04
TI  - Research on Storage and Processing of MongoDB for Laser Point Cloud under Distribution
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 1559
EP  - 1564
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.299
DO  - 10.2991/icmemtc-16.2016.299
ID  - Xu2016/04
ER  -