Efficient Computation of Top-K Skyline Objects in Data Set With Uncertain Preferences

Efficient Computation of Top-K Skyline Objects in Data Set With Uncertain Preferences

Nitesh Sukhwani, Venkateswara Rao Kagita, Vikas Kumar, Sanjaya Kumar Panda
Copyright: © 2021 |Pages: 13
DOI: 10.4018/IJDWM.2021070104
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Abstract

Skyline recommendation with uncertain preferences has drawn AI researchers' attention in recent years due to its wide range of applications. The naive approach of skyline recommendation computes the skyline probability of all objects and ranks them accordingly. However, in many applications, the interest is in determining top-k objects rather than their ranking. The most efficient algorithm to determine an object's skyline probability employs the concepts of zero-contributing set and prefix-based k-level absorption. The authors show that the performance of these methods highly depends on the arrangement of objects in the database. In this paper, the authors propose a method for determining top-k skyline objects without computing the skyline probability of all the objects. They also propose and analyze different methods of ordering the objects in the database. Finally, they empirically show the efficacy of the proposed approaches on several synthetic and real-world data sets.
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In recent years, a vast number of research studies have been conducted in skyline computation. A few of the most notable proposals are divide and conquer (Borzsonyi, Kossmann, & Stocker, 2001), nearest neighbours (Kossmann, Ramsak, & Rost, 2002), branch-and-bound-skyline (Papadias et al., 2003), bitmap (Tan, Eng, & Ooi, 2001), index (Tan, Eng, & Ooi, 2001), etc. However, these earlier proposals are not suitable for the situation when the data is categorical and uncertain.

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