Cache-Optimal Data-Structures for Hierarchical Methods on Adaptively Refined Space-Partitioning Grids

2006 ◽  
pp. 138-147 ◽  
Author(s):  
Miriam Mehl
Keyword(s):  
Data Structures ◽  
Space Partitioning

scholarly journals AN EXPERIMENTAL ANALYSIS OF SPATIAL INDEXING ALGORITHMS FOR REAL TIME SAFETY CRITICAL MAP APPLICATION

2021 ◽  
Vol V-4-2021 ◽  
pp. 41-48
Author(s):  
F. Çetin ◽  
M. O. Kulekci
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Data Structures ◽  
Spatial Indexing ◽  
Space Partitioning ◽  
Worst Case ◽  
Real Time System ◽  
Safety Critical ◽  
Range Search ◽  
Partitioning Technique

Abstract. This paper presents a study that compares the three space partitioning and spatial indexing techniques, KD Tree, Quad KD Tree, and PR Tree. KD Tree is a data structure proposed by Bentley (Bentley and Friedman, 1979) that aims to cluster objects according to their spatial location. Quad KD Tree is a data structure proposed by Berezcky (Bereczky et al., 2014) that aims to partition objects using heuristic methods. Unlike Bereczky’s partitioning technique, a new partitioning technique is presented based on dividing objects according to space-driven, in the context of this study. PR Tree is a data structure proposed by Arge (Arge et al., 2008) that is an asymptotically optimal R-Tree variant, enables data-driven segmentation. This study mainly aimed to search and render big spatial data in real-time safety-critical avionics navigation map application. Such a real-time system needs to efficiently reach the required records inside a specific boundary. Performing range query during the runtime (such as finding the closest neighbors) is extremely important in performance. The most crucial purpose of these data structures is to reduce the number of comparisons to solve the range searching problem. With this study, the algorithms’ data structures are created and indexed, and worst-case analyses are made to cover the whole area to measure the range search performance. Also, these techniques’ performance is benchmarked according to elapsed time and memory usage. As a result of these experimental studies, Quad KD Tree outperformed in range search analysis over the other techniques, especially when the data set is massive and consists of different geometry types.

scholarly journals Accelerating statistical human motion synthesis using space partitioning data structures

2017 ◽  
Vol 28 (3-4) ◽  
pp. e1780 ◽  
Author(s):  
Erik Herrmann ◽  
Martin Manns ◽  
Han Du ◽  
Somayeh Hosseini ◽  
Klaus Fischer
Keyword(s):  
Data Structures ◽  
Human Motion ◽  
Motion Synthesis ◽  
Space Partitioning ◽  
Human Motion Synthesis

scholarly journals Protein quantification across hundreds of experimental conditions

2009 ◽  
Vol 106 (37) ◽  
pp. 15544-15548 ◽  
Author(s):  
Zia Khan ◽  
Joshua S. Bloom ◽  
Benjamin A. Garcia ◽  
Mona Singh ◽  
Leonid Kruglyak
Keyword(s):  
Data Structures ◽  
Quantitative Data ◽  
Transcript Abundance ◽  
Protein Quantification ◽  
Protein Abundance ◽  
Proteomics Data ◽  
Space Partitioning ◽  
Experimental Conditions ◽  
Graph Theoretic ◽  
Quantitative Studies

Quantitative studies of protein abundance rarely span more than a small number of experimental conditions and replicates. In contrast, quantitative studies of transcript abundance often span hundreds of experimental conditions and replicates. This situation exists, in part, because extracting quantitative data from large proteomics datasets is significantly more difficult than reading quantitative data from a gene expression microarray. To address this problem, we introduce two algorithmic advances in the processing of quantitative proteomics data. First, we use space-partitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graph-theoretic algorithms with space-partitioning data structures to collect relative protein abundance data across hundreds of experimental conditions and replicates. We validate these algorithmic techniques by analyzing several datasets and computing both internal and external measures of quantification accuracy. We demonstrate the scalability of these techniques by applying them to a large dataset that comprises a total of 472 experimental conditions and replicates.

Structural Equations Modeling With Nonhierarchically Dependent Data Structures

2011 ◽  
Author(s):  
Paras D. Mehta ◽  
Steven M. Boker ◽  
Michael C. Neale
Keyword(s):  
Data Structures ◽  
Structural Equations ◽  
Dependent Data ◽  
Structural Equations Modeling

Specification scheme for the visualisation of data structures

1994 ◽  
Vol 9 (3) ◽  
pp. 127
Author(s):  
X.-B. Lu ◽  
F. Stetter
Keyword(s):  
Data Structures

Algorithms and Data Structures for External Memory

2006 ◽  
Author(s):  
Jeffrey Scott Vitter
Keyword(s):  
Data Structures ◽  
External Memory ◽  
Algorithms And Data Structures

Data Structures and Algorithms 3

1984 ◽  
Author(s):  
Kurt Mehlhorn
Keyword(s):  
Data Structures ◽  
Data Structures And Algorithms

The Social Furniture of Virtual Worlds

Disputatio ◽  
2019 ◽  
Vol 11 (55) ◽  
pp. 345-369
Author(s):  
Peter Ludlow
Keyword(s):  
Large Class ◽  
Data Structures ◽  
Virtual Worlds ◽  
Causal Powers ◽  
Virtual Objects ◽  
David Chalmers ◽  
The Social

AbstractDavid Chalmers argues that virtual objects exist in the form of data structures that have causal powers. I argue that there is a large class of virtual objects that are social objects and that do not depend upon data structures for their existence. I also argue that data structures are themselves fundamentally social objects. Thus, virtual objects are fundamentally social objects.

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