scholarly journals Inverse Space Filling Curve Partitioning Applied to Wide Area Graphs

2020 ◽  
Author(s):  
Cyprien Gottstein ◽  
Philippe Raipin Parvedy ◽  
Michel Hurfin ◽  
Thomas Hassan ◽  
Thierry Coupaye
Keyword(s):  
Graph Partitioning ◽  
Wide Area ◽  
Geometric Graphs ◽  
Space Filling ◽  
Space Filling Curve ◽  
Scale Free ◽  
Recent Developments ◽  
Curve Partitioning ◽  
Scale Free Graphs ◽  
Filling Curve

The most recent developments in graph partitioning research often consider scale-free graphs. Instead we focus on partitioning geometric graphs using a less usual strategy: Inverse Spacefilling Partitioning (ISP). ISP relies on a space filling curve to partition a graph and was previously applied to graphs essentially generated from Meshes. We extend ISP to apply it to a new context where the targets are now Wide Area Graphs. We provide an extended comparison with two state-of-the-art graph partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to better understand and identify the use cases for which the ISP partitioning solution is best suited. Experimentations show that in favourable contexts, edge-cuts can be drastically reduced, going from more 34% using FENNEL to less than 1% using ISP.

scholarly journals Partitioning Wide Area Graphs Using a Space Filling Curve

2021 ◽  
Vol 11 (1) ◽  
pp. 13-31
Author(s):  
Cyprien Gottstein ◽  
Philippe Raipin Parvedy ◽  
Michel Hurfin ◽  
Thomas Hassan ◽  
Thierry Coupaye
Keyword(s):  
Graph Partitioning ◽  
Wide Area ◽  
Space Filling ◽  
Space Filling Curve ◽  
Scale Free ◽  
Recent Developments ◽  
Actual Shape ◽  
Scale Free Graphs ◽  
Graph Management ◽  
Filling Curve

Graph structure is a very powerful tool to model system and represent their actual shape. For instance, modelling an infrastructure or social network naturally leads to graph. Yet, graphs can be very different from one another as they do not share the same properties (size, connectivity, communities, etc.) and building a system able to manage graphs should take into account this diversity. A big challenge concerning graph management is to design a system providing a scalable persistent storage and allowing efficient browsing. Mainly to study social graphs, the most recent developments in graph partitioning research often consider scale-free graphs. As we are interested in modelling connected objects and their context, we focus on partitioning geometric graphs. Consequently our strategy differs, we consider geometry as our main partitioning tool. In fact, we rely on Inverse Space-filling Partitioning, a technique which relies on a space filling curve to partition a graph and was previously applied to graphs essentially generated from Meshes. Furthermore, we extend Inverse Space-Filling Partitioning toward a new target we define as Wide Area Graphs. We provide an extended comparison with two state-of-the-art graph partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to better understand and identify the use cases for which the ISP partitioning solution is best suited. Experimentations show that in favourable contexts, edge-cuts can be drastically reduced, going from more 34% using FENNEL to less than 1% using ISP.

Region of Interest Based MRI Brain Image Compression Using Peano Space Filling Curve

2016 ◽  
Vol 11 (2) ◽  
pp. 114-120 ◽  
Author(s):  
C. Peter Devadoss ◽  
Balasubramanian Sankaragomathi ◽  
Thirugnanasambantham Monica
Keyword(s):  
Image Compression ◽  
Region Of Interest ◽  
Space Filling ◽  
Brain Image ◽  
Space Filling Curve ◽  
Mri Brain ◽  
Filling Curve ◽  
Mri Brain Image

A Space Filling Curve

1983 ◽  
Vol 90 (4) ◽  
pp. 283
Author(s):  
Liu Wen
Keyword(s):  
Space Filling ◽  
Space Filling Curve ◽  
Filling Curve

scholarly journals Enhancing Quad Tree for Spatial Index Using Space Filling Curves

2020 ◽  
Vol 38 (1B) ◽  
pp. 15-25
Author(s):  
Ali A. Hussain ◽  
Rehab F. Hassan
Keyword(s):  
Spatial Databases ◽  
Space Filling ◽  
Space Filling Curve ◽  
Effective Implementation ◽  
Quad Tree ◽  
Implementation Time ◽  
Spatial Indexes ◽  
Space Requirements ◽  
Filling Curve ◽  
And Storage

Spatial indexes, such as those based on the Quad Tree, are important in spatial databases for the effective implementation of queries with spatial constraints, especially when queries involve spatial links. The quaternary trees are a very interesting subject, given the fact that they give the ability to solve problems in a way that focuses only on the important areas with the highest density of information. Nevertheless, it is not without the disadvantages because the search process in the quad tree suffers from the problem of repetition when reaching the terminal node and return to the behavior of another way in the search and lead to the absorption of large amounts of time and storage. In this paper, the quad tree was improved by combining it with one of the space filling curve types, resulting in reduced storage space requirements and improved implementation time

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