Abstract
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.
Highlights
In the past ten years, big data has received widespread attention
To introduce skyline queries based on the orthogonal query range (i.e., ORSQ), we firstly introduce the orthogonal range [11]
As far as we know, there is not much research to solve the why-not question in skyline queries
Summary
In the past ten years, big data has received widespread attention. Storage [1], processing [2] and analysis, they generate value. In intelligent communication systems, users use wireless sensors [3,4,5,6,7] and mobile devices to collect data, and analyze the data to provide decision-making basis for programs. The wireless sensor is a type of wireless data communication collector which integrates data acquisition, data management, data communication and, other functions. We will research a new problem, which is related to data analysis
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