Performance evaluation of SpatialHadoop for big web mapping data

Maleki, Erfan Farhangi; Azadani, Mozhgan Nasr; Ghadiri, Nasser

doi:10.1109/icwr.2016.7498447

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Unlike these systems, SpatialHadoop [25,26] is developed to insert spatial data inside the essence of Hadoop. SpatialHadoop represents a Hadoop framework suited for spatial operations.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Partitioning Technique in SpatialHadoop

Shehab

Riad

Aboul-Fotouh

2018

IJICIS

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SpatialHadoop is a Hadoop framework supporting spatial information handling in light of MapReduce programming worldview. A huge number of studies leads to that SpatialHadoop outperforms the traditional Hadoop in both overseeing and handling spatial data operations. Indexing at SpatialHadoop makes it better than Hadoop. However, the design of a proficient and powerful indexing technique is stay as a major challenge. This paper presents a novel partitioning technique in SpatialHadoop. It has a better performance compared to other partitioning techniques. The proposed technique performance has been studied in several cases utilizing a real datasets on a spatial range and k-Nearest-Neighbour (kNN) queries. The experimental results have demonstrated the efficiency of the proposed technique.

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“…Unlike these systems, SpatialHadoop [25,26] is developed to insert spatial data inside the essence of Hadoop. SpatialHadoop represents a Hadoop framework suited for spatial operations.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Partitioning Technique in SpatialHadoop

Shehab

Riad

Aboul-Fotouh

2018

IJICIS

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“…Because of this issue, an expansion of Hadoop, called SpatialHadoop, has been developed. SpatialHadoop [12,13] is a Hadoop system that is suited for spatial operations. It adds spatial constructs and geospatial information into the Hadoop core functionality.…”

Section: Introductionmentioning

confidence: 99%

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

Elashry¹,

Shehab²,

Riad³

et al. 2018

IJGI

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Among spatial information applications, SpatialHadoop is one of the most important systems for researchers. Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique in SpatialHadoop. The 2DPR-Tree employs a top-down approach that effectively reduces the number of partitions accessed to answer the query, which in turn improves the query performance. The results were evaluated in different scenarios using synthetic and real datasets. This paper aims to study the quality of the generated index and the spatial query performance. Compared to other state-of-the-art methods, the proposed 2DPR-Tree improves the quality of the generated index and the query execution time.

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