VA-Files vs. R*-Trees in Distance Join Queries

Corral, Antonio; D'Ermiliis, Alejandro; Manolopoulos, Yannis; Vassilakopoulos, Michael

doi:10.1007/11547686_12

Cited by 6 publications

(2 citation statements)

References 18 publications

(25 reference statements)

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“…However, these methods fail to handle high-dimensional closest pair search due to the curse of dimensionality. Corral et al [11] propose a join method based on the VA-file, which is an array structure rather than a tree structure. Angiulli et al [4] adopt the Z-curve to reduce the dimensionality and generate candidates in onedimensional spaces.…”

Section: High Dimensional Closest Pair Searchmentioning

confidence: 99%

PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search

et al. 2021

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Nearest neighbor (NN) search is inherently computationally expensive in high-dimensional spaces due to the curse of dimensionality. As a well-known solution, locality-sensitive hashing (LSH) is able to answer c-approximate NN (c-ANN) queries in sublinear time with constant probability. Existing LSH methods focus mainly on building hash bucket-based indexing such that the candidate points can be retrieved quickly. However, existing coarse-grained structures fail to offer accurate distance estimation for candidate points, which translates into additional computational overhead when having to examine unnecessary points. This in turn reduces the performance of query processing. In contrast, we propose a fast and accurate in-memory LSH framework, called PM-LSH, that aims to compute c-ANN queries on large-scale, high-dimensional datasets. First, we adopt a simple yet effective PM-tree to index the data points. Second, we develop a tunable confidence interval to achieve accurate distance estimation and guarantee high result quality. Third, we propose an efficient algorithm on top of the PM-tree to improve the performance of computing c-ANN queries.

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Section: High Dimensional Closest Pair Searchmentioning

confidence: 99%

PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search

et al. 2021

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“…It is also worth noting that similarity join methods [ 178 , 179 ] have been developed to find all paired objects that are closer than a user-specified distance. However, these techniques to process similarity join cannot be effectively applied to kNN Joins since it is hard to predict the search radius in kNN Joins [ 180 , 181 ].…”

Section: Knn Join Approachesmentioning

confidence: 99%

Survey on Exact kNN Queries over High-Dimensional Data Space

Ukey

Yang

et al. 2023

Sensors

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k nearest neighbours (kNN) queries are fundamental in many applications, ranging from data mining, recommendation system and Internet of Things, to Industry 4.0 framework applications. In mining, specifically, it can be used for the classification of human activities, iterative closest point registration and pattern recognition and has also been helpful for intrusion detection systems and fault detection. Due to the importance of kNN queries, many algorithms have been proposed in the literature, for both static and dynamic data. In this paper, we focus on exact kNN queries and present a comprehensive survey of exact kNN queries. In particular, we study two fundamental types of exact kNN queries: the kNN Search queries and the kNN Join queries. Our survey focuses on exact approaches over high-dimensional data space, which covers 20 kNN Search methods and 9 kNN Join methods. To the best of our knowledge, this is the first work of a comprehensive survey of exact kNN queries over high-dimensional datasets. We specifically categorise the algorithms based on indexing strategies, data and space partitioning strategies, clustering techniques and the computing paradigm. We provide useful insights for the evolution of approaches based on the various categorisation factors, as well as the possibility of further expansion. Lastly, we discuss some open challenges and future research directions.

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