Massive spatial data network service architecture based on double-cluster

2009 ◽  
Author(s):  
Zhongmin Li ◽  
Lu Gao
Keyword(s):  
Spatial Data ◽  
Network Service ◽  
Service Architecture ◽  
Data Network ◽  
Massive Spatial Data ◽  
Double Cluster

Parallel Queries of Cluster-Based k Nearest Neighbor in MapReduce

2016 ◽  
pp. 163-182
Author(s):  
Wei Yan
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Nearest Neighbor ◽  
Programming Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
Data Intensive ◽  
Parallel Queries ◽  
Massive Spatial Data ◽  
Nearest Neighbor Queries

Parallel queries of k Nearest Neighbor for massive spatial data are an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every point in another dataset R, is a useful tool widely adopted by many applications including knowledge discovery, data mining, and spatial databases. In cloud computing environments, MapReduce programming model is a well-accepted framework for data-intensive application over clusters of computers. This chapter proposes a parallel method of kNN queries based on clusters in MapReduce programming model. Firstly, this chapter proposes a partitioning method of spatial data using Voronoi diagram. Then, this chapter clusters the data point after partition using k-means method. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on k-means clusters using MapReduce programming model. Finally, extensive experiments evaluate the efficiency of the proposed approach.

Research on Multiple Spatial Data Gathering Mechanism Based on Geo-Information Browser

2013 ◽  
Vol 765-767 ◽  
pp. 1287-1290
Author(s):  
Ken Chen ◽  
Fang Wang ◽  
Fang Miao ◽  
Fu Chao Cheng
Keyword(s):  
Spatial Data ◽  
Data Exchange ◽  
Data Gathering ◽  
Basic Research ◽  
Information Gathering ◽  
Digital Earth ◽  
Organization And Management ◽  
And Performance ◽  
S Model ◽  
Massive Spatial Data

The spatial data presented several characteristics of mass, multiple, isomerism and multiple tenses, its organization and management mechanism is an important direction of research for Digital Earth. The management of grave emergency with regards to a series of spatial and non-spatial data concerning gathering and handling, having put a higher demand forward the ability of information gathering mechanism on client. The current existing client access mechanism such as C/S model lacks of unified data exchange standards, similarly, B/S model cannot handle the spatial data effectively. It is also difficulty to display for complex and massive spatial data in visualized and real-time. That efficiency depends entirely on the network environment and performance of storage equipment. In order to realize the massive spatial data unified dispatching and efficient sharing based on the principle of Information-gathering and Service-polymerization. We put forward a concept of Spatial-data-cloud which based on G/S model, supported by HGML as the standard and criterion of spatial data exchange, presentation, organization, storage and management. It could also be set up a new work mechanism which use Geo-information browser polymerization multiple and massive complex spatial and non-spatial data. This will provide us a lightweight client called Geo-information browser with which is by the principle Information-gathering and Service-polymerization. It provides emergency management for technical supports such as intelligent decision support, comprehensive research and judgment, and rapid disposal etc. It is the development of basic research of a novel model of Digital Earth.

Research on the Metadata Storage Mode and Efficiency of Distributed File System Based on HGML

2010 ◽  
Vol 40-41 ◽  
pp. 221-227 ◽  
Author(s):  
Fang Miao ◽  
Fu Chao Cheng ◽  
Wen Hui Yang ◽  
Li Tan
Keyword(s):  
Spatial Data ◽  
File System ◽  
Distributed File System ◽  
Entire System ◽  
The Core ◽  
Different Characteristics ◽  
Massive Spatial Data ◽  
Access Efficiency

In the G / S mode, in order to meet the storage demands of massive spatial data, the requirements of the distributed file system (DFS) on back-end servers are extremely high. As one of the core tasks of DFS, the metadata storage is the necessary premise which ensures the reliability and efficiency of the entire system. This paper introduces a metadata storage mode based on HGML, and then designs and implements two solutions, which are scattered storage and integrated storage. According to the different characteristics of the two solutions, access efficiency of the metadata has been tested respectively. The result shows that the new metadata storage mode can basically satisfy the storage demands of massive spatial data.

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