Monochromatic and bichromatic reverse nearest neighbor queries on land surfaces

Yan, Da; Zhao, Zhou; Ng, Wilfred

doi:10.1145/2396761.2396880

Cited by 11 publications

(28 citation statements)

References 21 publications

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“…Even though it is equipped with the lower and upper bound computations for pruning, it is found in [10,12] that the MR3 method does not return accurate k-NN results, especially as k gets larger.…”

Section: Multi-resolution Range Ranking Methodsmentioning

confidence: 99%

“…In academia, several recent studies [5,8,[10][11][12] focus on the challenges presented by terrain data. For example, they consider "terrain" versions of some well-known spatial queries such as shortest path queries, k-nearest neighbor queries, and reverse nearest neighbor queries.…”

Section: Introductionmentioning

confidence: 99%

“…Articles from this volume were invited to present their results at The 40th International Conference on Very Large Data Bases, September 1st -5th 2014, Hangzhou, China. queries, and surface reverse nearest neighbor queries [12]). Given a source point s and a destination point t on a terrain, a shortest surface path query returns the shortest surface path from s to t on the surface.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by this observation, several existing studies [5,8,[10][11][12] propose efficient methods to find lower and upper bounds of the shortest surface distance. These can then be used to avoid some of the expensive surface distance computations inherent in spatial queries.…”

Section: Introductionmentioning

confidence: 99%

“…Intuitively, each mapped path is a surface path on the terrain with the constraint that the path must pass through only the vertices and the edges of the Delaunay graph. as the upper bound of the shortest surface distance [5,8,10,12] is that this bound is tight and is computationally cheap compared to the shortest surface distance computation.…”

Section: Introductionmentioning

confidence: 99%

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Finding shortest paths on terrains by killing two birds with one stone

et al. 2013

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With the increasing availability of terrain data, e.g., from aerial laser scans, the management of such data is attracting increasing attention in both industry and academia. In particular, spatial queries, e.g., k-nearest neighbor and reverse nearest neighbor queries, in Euclidean and spatial network spaces are being extended to terrains. Such queries all rely on an important operation, that of finding shortest surface distances. However, shortest surface distance computation is very time consuming. We propose techniques that enable efficient computation of lower and upper bounds of the shortest surface distance, which enable faster query processing by eliminating expensive distance computations. Empirical studies show that our bounds are much tighter than the best-known bounds in many cases and that they enable speedups of up to 43 times for some well-known spatial queries.

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Section: Multi-resolution Range Ranking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Finding shortest paths on terrains by killing two birds with one stone

et al. 2013

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Finding optimal region for bichromatic reverse nearest neighbor in two- and three-dimensional spaces

et al. 2015

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Online Data Center Traffic Classification Based on Inter-Flow Correlations

et al. 2020

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Today, increasing attention is being paid to Data Center (DC) traffic classification since these infrastructures have become the heart of a variety of time-sensitive and data-intensive service platforms. Classification provides the required tools for better understanding traffic patterns in order to ensure high Quality of Service (QoS) performances and solve scalability problems. Unfortunately, existing classification algorithms cannot deal efficiently with two critical challenges in DC traffic: inter-class imbalance and critical time constraints. In this paper, we propose a novel correlation-based algorithm following a cost-sensitive approach combined with a Bagged Random Forest (BRF) ensemble algorithm, to address the inter-class imbalance problem while meeting time requirements in a data center environment. In this strategy, a new method based on Reverse k-Nearest Neighbors (RkNN) is proposed to capture the rebalancing weights expressing inter-flow correlations, in order to perform an online classification approach. We demonstrate the efficiency of the algorithm by comparing its performance to several existing methods from data level, algorithm level, and cost-sensitive strategies on four real-world datasets. The results reveal that the proposed algorithm outperforms most approaches in the different datasets in terms of precision, recall, F1 measure, AUC and Kappa, as opposed to other algorithms that result in either high precision with low recall and low precision and high recall causing congestion or resource over provisioning.

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Monochromatic and bichromatic reverse nearest neighbor queries on land surfaces

Cited by 11 publications

References 21 publications

Finding shortest paths on terrains by killing two birds with one stone

Finding shortest paths on terrains by killing two birds with one stone

Finding optimal region for bichromatic reverse nearest neighbor in two- and three-dimensional spaces

Online Data Center Traffic Classification Based on Inter-Flow Correlations

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