Efficient $$k$$ k -closest pair queries in general metric spaces

Gao, Yunjun; Chen, Lu; Li, Xinhan; Yao, Bin; Chen, Gang

doi:10.1007/s00778-015-0383-4

Cited by 21 publications

(16 citation statements)

References 44 publications

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“…With this approximate method we can easily adjust the balance between execution time of the KCPQ algorithm and the accuracy of the final result. Notice that this α-allowance technique can be easily transformed to the ε-approximate technique with α = 1/(1 + ε) [10].…”

Section: Computing β By Local Approximate Methodsmentioning

confidence: 99%

“…For this reason, we will consider them as candidates for application in our problem. Since ε ≥ 0 values are unlimited, according to the conclusions of [61,10], it is not easy to adjust the β value (upper bound of the distance value of K-th closest pair). For this reason, here we will choose the α-allowance technique, where α is a bounded positive real number (0 ≤ α ≤ 1).…”

Section: Computing β By Local Approximate Methodsmentioning

confidence: 99%

“…Several different approaches have been proposed, aiming to improve the performance of DBJQs by proposing efficient algorithms [8][9][10][11]. However, all these approaches focus on methods that are executed in a centralized environment.…”

Section: Introductionmentioning

confidence: 99%

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Efficient large-scale distance-based join queries in spatialhadoop

et al. 2017

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Efficient processing of Distance-Based Join Queries (DBJQs) in spatial databases is of paramount importance in many application domains. The most representative and known DBJQs are the K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ). These types of join queries are characterized by a number of desired pairs (K) or a distance threshold (ε) between the components of the pairs in the final result, over two spatial datasets. Both are expensive operations, since two spatial datasets are combined with additional constraints. Given the increasing volume of spatial data originating from multiple sources and stored in distributed servers, it is not always efficient to perform DBJQs on a centralized server. For this reason, this paper addresses the problem of computing DBJQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports efficient processing of spatial queries in a cloud-based setting. We propose novel algorithms, based on plane-sweep, to perform efficient parallel DBJQs on large-scale spatial datasets in SpatialHadoop. We evaluate the performance of the proposed algorithms in several situations with large real-world as well as synthetic datasets. The experiments demon-A preliminary partial version of this work appeared in [1].

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Section: Computing β By Local Approximate Methodsmentioning

confidence: 99%

Section: Computing β By Local Approximate Methodsmentioning

confidence: 99%

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Efficient large-scale distance-based join queries in spatialhadoop

et al. 2017

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“…Previous studies like [5,12] employ the eD-Index, but they focus only in range joins. Another study [7] proposed pruning techniques on the M-Tree to improve the detection of closest pairs. Our proposal departs from that once we detect closest pairs extending similarity join operators.…”

Section: Related Workmentioning

confidence: 99%

“…Similarity joins are becoming important database operators in several scenarios, such as near-duplicate detection, string matching and data mining support [7,10]. Those operators receive two relations T 1 and T 2 and return pairs of tuples t[T 1 ], t [T 2 ] that meet a similarity predicate.…”

Section: Introductionmentioning

confidence: 99%

Similarity Joins and Beyond: An Extended Set of Binary Operators with Order

Carvalho

Santos

Oliveira

et al. 2015

Similarity Search and Applications

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Abstract. Similarity joins are troublesome database operators that often produce results much larger than the user really needs or expects. In order to return the similar elements, similarity joins also require sorting during the retrieval process, although order is a concept not supported in the relational model. This paper proposes a solution to solve those two issues extending the similarity join concept to a broader set of binary operators, which aims at retrieving the most similar pairs and embedding the sorting operation only as an internal processing step, so as to comply with the relational theory. Additionally, our extension allows to explore another useful condition not previously considered in the similarity retrieval: the negation of predicates. Experiments performed on real and synthetic data show that our operators are fast enough to be used in real applications and scale well both for multidimensional and non-dimensional metric data.

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Solving k-Closest Pairs in High-Dimensional Data

Aumüller,

Ceccarello

2023

Lecture Notes in Computer Science

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Efficient $$k$$ k -closest pair queries in general metric spaces

Cited by 21 publications

References 44 publications

Efficient large-scale distance-based join queries in spatialhadoop

Efficient large-scale distance-based join queries in spatialhadoop

Similarity Joins and Beyond: An Extended Set of Binary Operators with Order

Solving k-Closest Pairs in High-Dimensional Data

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