Abstract
Recently, the skyline query has attracted interest in a wide range of applications from recommendation systems to computer networks. The skyline query is useful to obtain the dominant data points from the given dataset. In the low-dimensional dataset, the skyline query may return a small number of skyline points. However, as the dimensionality of the dataset increases, the number of skyline points also increases. In other words, depending on the data distribution and dimensionality, most of the data points may become skyline points. With the emergence of big data applications, where the data distribution and dimensionality are a significant problem, obtaining representative skyline points among resulting skyline points is necessary. There have been several methods that focused on extracting representative skyline points with various success. However, existing methods have a problem of re-computation when the global threshold changes. Moreover, in certain cases, the resulting representative skyline points may not satisfy a user with multiple preferences. Thus, in this paper, we propose a new representative skyline query processing method, called representative skyline cluster (RSC), which solves the problems of the existing methods. Our method utilizes the hierarchical agglomerative clustering method to find the exact representative skyline points, which enable us to reduce the re-computation time significantly. We show the superiority of our proposed method over the existing state-of-the-art methods with various types of experiments.
Highlights
In data mining and data analysis, a multi-criteria analysis is a conventional method to address the problems of real-life applications from recommendation systems to decision making
We propose a new representative query processing method, In this paper, we propose a new representative skyline query processing method, which solves the problems of existing methods
We propose a method for selecting the representative skyline points that uses the hierarchical agglomerative clustering structure
Summary
In data mining and data analysis, a multi-criteria analysis is a conventional method to address the problems of real-life applications from recommendation systems to decision making. One of the commonly used multi-criteria analysis methods is the skyline query. The skyline query enables obtaining of interesting and dominant data points from the given dataset [1]. Assume that a system has information regarding the various hotels in a particular location with attributes, including price and distance (i.e., distance from the beach). The user would prefer hotels with a low price and those closest to the beach. The skyline query returns the hotels that satisfy user preferences. The skyline query is extensively studied in many fields [2]
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