scholarly journals Inverse Space-Filling Curve Partitioning of a Global Ocean Model

2007 ◽  
Author(s):  
John M. Dennis
Keyword(s):  
Ocean Model ◽  
Global Ocean ◽  
Space Filling ◽  
Space Filling Curve ◽  
Curve Partitioning ◽  
Filling Curve

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.

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 Onion Curve: A Space Filling Curve with Near-Optimal Clustering

2018 ◽  
Author(s):  
Pan Xu ◽  
Cuong Nguyen ◽  
Srikanta Tirthapura
Keyword(s):  
Space Filling ◽  
Space Filling Curve ◽  
Filling Curve

Space-Filling Curve

2009 ◽  
pp. 2675-2675
Keyword(s):  
Space Filling ◽  
Space Filling Curve ◽  
Filling Curve

scholarly journals Secure and Efficient Data Retrieval Process based on Hilbert Space Filling Curve

2014 ◽  
Vol 91 (4) ◽  
pp. 36-40
Author(s):  
N. S.JeyaKarthikka ◽  
S. Bhaggiaraj ◽  
V. Sumathy
Keyword(s):  
Hilbert Space ◽  
Data Retrieval ◽  
Retrieval Process ◽  
Space Filling ◽  
Space Filling Curve ◽  
Efficient Data ◽  
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

The LBF R-Tree

2010 ◽  
pp. 1-27
Author(s):  
Todd Eavis
Keyword(s):  
Hilbert Space ◽  
Data Warehousing ◽  
Experimental Results ◽  
Data Sets ◽  
Space Filling ◽  
Fully Integrated ◽  
Space Filling Curve ◽  
Filling Curve ◽  
Common User ◽  
Tree Performance

In multi-dimensional database environments, such as those typically associated with contemporary data warehousing, we generally require effective indexing mechanisms for all but the smallest data sets. While numerous such methods have been proposed, the R-tree has emerged as one of the most common and reliable indexing models. Nevertheless, as user queries grow in terms of both size and dimensionality, R-tree performance can deteriorate significantly. Moreover, in the multi-terabyte spaces of today’s enterprise warehouses, the combination of data and indexes ? R-tree or otherwise ? can produce unacceptably large storage requirements. In this chapter, the authors present a framework that addresses both of these concerns. First, they propose a variation of the classic R-tree that specifically targets data warehousing architectures. Their new LBF R-tree not only improves performance on common user-defined range queries, but gracefully degrades to a linear scan of the data on pathologically large queries. Experimental results demonstrate a reduction in disk seeks of more than 50% relative to more conventional R-tree designs. Second, the authors present a fully integrated, block-oriented compression model that reduces the storage footprint of both data and indexes. It does so by exploiting the same Hilbert space filling curve that is used to construct the LBF R-tree itself. Extensive testing demonstrates compression rates of more than 90% for multi-dimensional data, and up to 98% for the associated indexes.

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