Volume XLI-B4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 545-549, 2016
https://doi.org/10.5194/isprs-archives-XLI-B4-545-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 545-549, 2016
https://doi.org/10.5194/isprs-archives-XLI-B4-545-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  14 Jun 2016

14 Jun 2016

PROVIDING R-TREE SUPPORT FOR MONGODB

Longgang Xiang1,2, Xiaotian Shao1,2, and Dehao Wang1,2 Longgang Xiang et al.
  • 1State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • 2Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China

Keywords: R-tree, Spatial index, MongoDB

Abstract. Supporting large amounts of spatial data is a significant characteristic of modern databases. However, unlike some mature relational databases, such as Oracle and PostgreSQL, most of current burgeoning NoSQL databases are not well designed for storing geospatial data, which is becoming increasingly important in various fields. In this paper, we propose a novel method to provide R-tree index, as well as corresponding spatial range query and nearest neighbour query functions, for MongoDB, one of the most prevalent NoSQL databases. First, after in-depth analysis of MongoDB’s features, we devise an efficient tabular document structure which flattens R-tree index into MongoDB collections. Further, relevant mechanisms of R-tree operations are issued, and then we discuss in detail how to integrate R-tree into MongoDB. Finally, we present the experimental results which show that our proposed method out-performs the built-in spatial index of MongoDB. Our research will greatly facilitate big data management issues with MongoDB in a variety of geospatial information applications.