Fully distributed R-tree for efficient range query dissemination in peer-to-peer spatial data grid

2007 ◽  
Author(s):  
Dafei Yin ◽  
Bin Chen ◽  
Yu Fang ◽  
Zhou Huang
Keyword(s):  
Spatial Data ◽  
Range Query ◽  
Peer To Peer ◽  
Data Grid

Architecture design for spatial data grid

2006 ◽  
Author(s):  
Hua Liu ◽  
Deren Li ◽  
Weiying Yang ◽  
Tao Shang
Keyword(s):  
Spatial Data ◽  
Data Grid ◽  
Architecture Design

A fault tolerant peer-to-peer spatial data structure

2016 ◽  
Vol 10 (4) ◽  
pp. 874-886
Author(s):  
Pouya Bisadi ◽  
Zahra Mirikharaji ◽  
Bradford G. Nickerson
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
Fault Tolerant ◽  
Peer To Peer ◽  
Spatial Data Structure

Utility Based Query Dissemination in Spatial Data Grid

2007 ◽  
Author(s):  
Dafei Yin ◽  
Bin Chen ◽  
Zhou Huang ◽  
Xin Lin ◽  
Xin Lin ◽  
...  
Keyword(s):  
Spatial Data ◽  
Data Grid

scholarly journals A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects

2021 ◽  
Vol 10 (12) ◽  
pp. 814
Author(s):  
Xiangqiang Min ◽  
Dieter Pfoser ◽  
Andreas Züfle ◽  
Yehua Sheng
Keyword(s):  
Spatial Data ◽  
Main Idea ◽  
Range Query ◽  
Access Methods ◽  
Spatial Objects ◽  
Density Based Clustering ◽  
Node Distribution ◽  
Query Algorithm ◽  
Information Systems Use ◽  
Network Space

The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are not computed in unrestricted Euclidean space, but on a network. While the majority of access methods cannot trivially be extended to network space, existing network index structures partition the network space without considering the data distribution. This potentially results in inefficiency due to a very skewed node distribution. To improve range query processing on networks, this paper proposes a balanced Hierarchical Network index (HN-tree) to query spatial objects on networks. The main idea is to recursively partition the data on the network such that each partition has a similar number of spatial objects. Leveraging the HN-tree, we present an efficient range query algorithm, which is empirically evaluated using three different road networks and several baselines and state-of-the-art network indices. The experimental evaluation shows that the HN-tree substantially outperforms existing methods.

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