A Simple Data Structure for Optimal Two-Sided 2D Orthogonal Range Queries

Grez, Alejandro; Calì, Andrea; Ugarte, Martín

doi:10.1007/978-3-030-27629-4_7

Cited by 1 publication

(2 citation statements)

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“…To introduce skyline queries based on the orthogonal query range (i.e., ORSQ), we firstly introduce the orthogonal range [11]. Queries based on the orthogonal range are retrieved within the range R, where R = R 1 ∩ .…”

Section: Introductionmentioning

confidence: 99%

“…An example of the MRN Algorithm.Complexity analysis: The complexity of MRN is mainly consists of sorting skylineW in step 6 (i.e., O(N * log 2 N)), finding the position of the abscissa of w in ox in step 7 (i.e., O(log 2 N)) and traversing SP(R) part in steps[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Suppose there are n elements in SP(R) and k elements in SP(R) part .…”

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confidence: 99%

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Researching Why-Not Questions in Skyline Query Based on Orthogonal Range

Sun

Liang

et al. 2020

Electronics

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This paper aims to answer “why-not” questions in skyline queries based on the orthogonal query range (i.e., ORSQ). These queries retrieve skyline points within a rectangular query range, which improves query efficiency. Answering why-not questions in ORSQ can help users analyze query results and make decisions. We discuss the causes of why-not questions in ORSQ. Then, we outline how to modify the why-not point and the orthogonal query range so that the why-not point is included in the result of the skyline query based on the orthogonal range. When the why-not point is in the orthogonal range, we show how to modify the why-not point and narrow the orthogonal range. We also present how to expand the orthogonal range when the why-not point is not in the orthogonal range. We effectively combine query refinement and data modification techniques to produce meaningful answers. The experimental results demonstrate that the proposed algorithms have high-quality explanations for why-not questions in ORSQ in the real and synthetic datasets.

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Section: Introductionmentioning

confidence: 99%