A Two-Tier Spatial Index for Non-flat Spatial Data Broadcasting on Air

2014 ◽  
Vol E97.B (12) ◽  
pp. 2809-2818 ◽  
Author(s):  
SeokJin IM ◽  
HeeJoung HWANG
Keyword(s):  
Spatial Data ◽  
Spatial Index ◽  
Data Broadcasting

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549 ◽  
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

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.

scholarly journals PROVIDING R-TREE SUPPORT FOR MONGODB

2016 ◽  
Vol XLI-B4 ◽  
pp. 545-549
Author(s):  
Longgang Xiang ◽  
Xiaotian Shao ◽  
Dehao Wang
Keyword(s):  
Spatial Data ◽  
Relational Databases ◽  
Spatial Index ◽  
Geospatial Information ◽  
Nosql Databases ◽  
Document Structure ◽  
Depth Analysis ◽  
Novel Method ◽  
Tree Index ◽  
Management Issues

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.

A Mobile Storage System for Massive Spatial Data

2014 ◽  
Vol 962-965 ◽  
pp. 2730-2734
Author(s):  
Zhen Song ◽  
Jian Chen ◽  
Jiu Yan Ye
Keyword(s):  
Mobile Devices ◽  
Data Storage ◽  
Spatial Data ◽  
Storage System ◽  
Hierarchical Classification ◽  
Spatial Index ◽  
Index Structure ◽  
Real Time Visualization ◽  
Spatial Data Organization ◽  
Low Efficiency

This paper aims at the solution to problems of large amounts of data storage, low efficiency calculate and poor user experience which actually exists in GIS application on mobile devices. In order to solve these issues we use GIS technologies including spatial data organization, map browser, and spatial index. We focused on the research of how to effectively utilize system resources and rational and efficient out organize the spatial data. What’s more, we improved the R-tree indexing algorithm to establish a rapid spatial index structure and used hierarchical classification techniques to optimize the efficiency of the real-time visualization of spatial data for mobile devices.

scholarly journals Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data

2019 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Mengyu Ma ◽  
Ye Wu ◽  
Luo Chen ◽  
Jun Li ◽  
Ning Jing
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Large Scale ◽  
Optimization Method ◽  
Spatial Index ◽  
Overlay Analysis ◽  
Analysis Model ◽  
Time Buffer ◽  
Real World Datasets ◽  
Processing Architecture

Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of conventional data-oriented methods expand rapidly with data volumes. In this paper, we present HiBO, a visualization-oriented buffer-overlay analysis model which is less sensitive to data volumes. In HiBO, the core task is to determine the value of pixels for display. Therefore, we introduce an efficient spatial-index-based buffer generation method and an effective set-transformation-based overlay optimization method. Moreover, we propose a fully optimized hybrid-parallel processing architecture to ensure the real-time capability of HiBO. Experiments on real-world datasets show that our approach is capable of handling ten-million-scale spatial data in real time. An online demonstration of HiBO is provided (http://www.higis.org.cn: 8080/hibo).

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