2DPR-Tree: Two-Dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop

Elashry, Ahmed; Shehab, Abdulaziz; Riad, A. M.; Aboul-Fotouh, Ahmed

doi:10.3390/ijgi7050179

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…Accordingly, the experimental analyses have been conducted to provide the performances of their proposed approach in comparison to several spatial access methods. Elashry et al presented a new partitioning technique based on the PR Tree algorithm and comparisons of KD Tree, Quad Tree, and PR Tree with each other in SpatialHadoop (Elashry et al, 2018). Sayar et al present a study that compares the algorithms of KD Tree and Quad Tree concerning the feasibility and efficiency of using these partitioning techniques (Sayar et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

An Experimental Analysis of Spatial Indexing Algorithms for Real Time Safety Critical Map Application

Çetin

Külekçi

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Related Workmentioning

confidence: 99%

An Experimental Analysis of Spatial Indexing Algorithms for Real Time Safety Critical Map Application

Çetin

Külekçi

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The article of Ahmed Elashry et al [22] describes "2DPR-Tree: Two-dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop". Among spatial information applications, SpatialHadoop is one of the most important systems for researchers.…”

Section: Hci and Gis In This Issuementioning

confidence: 99%

Guest Editor’s Editorial “Cognitive Aspects of Human-Computer Interaction for GIS”

Fritsch

2019

IJGI

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“…Notably, the abovementioned spatial indices are non-distributed indices. Many researchers have attempted to develop a distributing spatial index, e.g., Mouza et al [37], Malensek et al [38], Elashry et al [39], and Xia et al [40]. Usually, a distributing spatial index originates from a non-distributed spatial index, e.g., the distributing quad-tree [41], distributing R-tree [40,42,43], distributing QR-tree [44], and distributing Hilbert TGS Rtree [45].…”

Section: Introductionmentioning

confidence: 99%

QRB-tree Indexing: Optimized Spatial Index Expanding upon the QR-tree Index

Wei

et al. 2021

IJGI

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Support for region queries is crucial in geographic information systems, which process exact queries through spatial indexing to filter features and subsequently refine the selection. Although the filtering step has been extensively studied, the refinement step has received little attention. This research builds upon the QR-tree index, which decomposes space into hierarchical grids, registers features to the grids, and builds an R-tree for each grid, to develop a new QRB-tree index with two levels of optimization. In the first level, a bucket is introduced in every grid in the QR-tree index to accelerate the loading and search steps of a query region for the grids within the query region. In the second level, the number of candidate features to be eliminated is reduced by limiting the features to those registered to the grids covering the corners of the query region. Subsequently, an approach for determining the maximal grid level, which significantly affects the performance of the QR-tree index, is proposed. Direct comparisons of time costs with the QR-tree index and geohash index show that the QRB-tree index outperforms the other two approaches for rough queries in large query regions and exact queries in all cases.

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2DPR-Tree: Two-Dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop

Cited by 10 publications

References 38 publications

An Experimental Analysis of Spatial Indexing Algorithms for Real Time Safety Critical Map Application

An Experimental Analysis of Spatial Indexing Algorithms for Real Time Safety Critical Map Application

Guest Editor’s Editorial “Cognitive Aspects of Human-Computer Interaction for GIS”

QRB-tree Indexing: Optimized Spatial Index Expanding upon the QR-tree Index

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