Eclipse: Generalizing kNN and Skyline
Jinfei Liu, Li Xiong, Qiuchen Zhang, Jian Pei, and Jun Luo

TL;DR
This paper introduces the eclipse operator, a flexible and customizable generalization of kNN and skyline queries, enabling user preferences and controlling output size, with efficient algorithms demonstrated on real and synthetic datasets.
Contribution
The paper proposes the eclipse operator that unifies and extends kNN and skyline queries, allowing user customization and offering efficient algorithms for query processing.
Findings
Eclipse generalizes kNN and skyline queries.
Efficient algorithms outperform baseline methods.
Experimental results validate effectiveness and efficiency.
Abstract
nearest neighbor (NN) queries and skyline queries are important operators on multi-dimensional data points. Given a query point, NN query returns the nearest neighbors based on a scoring function such as a weighted sum of the attributes, which requires predefined attribute weights (or preferences). Skyline query returns all possible nearest neighbors for any monotonic scoring functions without requiring attribute weights but the number of returned points can be prohibitively large. We observe that both NN and skyline are inflexible and cannot be easily customized. In this paper, we propose a novel \emph{eclipse} operator that generalizes the classic NN and skyline queries and provides a more flexible and customizable query solution for users. In eclipse, users can specify rough and customizable attribute preferences and control the number of returned points. We…
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Taxonomy
TopicsData Management and Algorithms · Data Mining Algorithms and Applications · Time Series Analysis and Forecasting
