(A)kNN Query Processing on the Cloud: A Survey

Nodarakis, Nikolaos; Rapti, Angeliki; Sioutas, Spyros; Tsakalidis, Athanasios K.; Tsolis, Dimitrios; Tzimas, Giannis; Panagis, Yannis

doi:10.1007/978-3-319-57045-7_3

Cited by 4 publications

(2 citation statements)

References 54 publications

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“…Instances of such researches incorporate: (1) Range query [11][12][13], the input dataset is investigated, and each object is analyzed with respect to the query range. (2) k Nearest Neighbour (kNN) query [12,14,15], locates the k closest objects, utilizing distance metrics, from a specific object. (3) All Nearest Neighbour (ANN) query [9,16], having N objects and process to know which is the closest neighbor for each one of those N objects, objects are divided by their Z-values to find out the result.…”

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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Section: Related Workmentioning

confidence: 99%

An Efficient Partitioning Technique in SpatialHadoop

Shehab

Riad

Aboul-Fotouh

2018

IJICIS

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“…Each block that needs to be processed is assigned to a map function that searches its index to get the matching records [12]. The kNN query [14,18] takes a query point Q and an integer k to find the k closest points to Q in the input dataset. In Hadoop, the kNN query checks all the input points in the input dataset, finds the distances between them and Q, and then the top-k points are returned as the result [7].…”

Section: The Overall Architecture Of Spatialhadoopmentioning

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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An Enhanced Partitioning Approach in SpatialHadoop for Handling Big Spatial Data

Shehab

Elashry

Aboul-Fotouh

et al. 2023

Int J Comput Intell Syst

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SpatialHadoop could handle spatial data operations in a low partitioning execution time compared to the traditional Hadoop. However, developing an efficient and an accurate partitioning algorithm is still a research field opened to many researchers. Confidently, this paper proposes a Minimum Boundary Rectangle-aware Priority R-Tree (MBR-aware PR-Tree) as an enhanced partitioning algorithm applicable at SpatialHadoop. Compared to state-of-art partitioning algorithms, our proposed algorithm outperforms them in terms of query execution time, file size, number of partitions, indexing time, and number of returned objects. The experimental results show superiority of our algorithm which have been confirmed for both spatial range query and k-nearest-neighbour query through evaluating the performance in different scenarios using a real dataset.

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(A)kNN Query Processing on the Cloud: A Survey

Cited by 4 publications

References 54 publications

An Efficient Partitioning Technique in SpatialHadoop

An Efficient Partitioning Technique in SpatialHadoop

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

An Enhanced Partitioning Approach in SpatialHadoop for Handling Big Spatial Data

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