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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 |Volume: 17 |Issue: 3 |Pages: 13
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799859505|DOI: 10.4018/IJDWM.2021070104
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MLA

Sukhwani, Nitesh, et al. "Efficient Computation of Top-K Skyline Objects in Data Set With Uncertain Preferences." IJDWM vol.17, no.3 2021: pp.68-80. http://doi.org/10.4018/IJDWM.2021070104

APA

Sukhwani, N., Kagita, V. R., Kumar, V., & Panda, S. K. (2021). Efficient Computation of Top-K Skyline Objects in Data Set With Uncertain Preferences. International Journal of Data Warehousing and Mining (IJDWM), 17(3), 68-80. http://doi.org/10.4018/IJDWM.2021070104

Chicago

Sukhwani, Nitesh, et al. "Efficient Computation of Top-K Skyline Objects in Data Set With Uncertain Preferences," International Journal of Data Warehousing and Mining (IJDWM) 17, no.3: 68-80. http://doi.org/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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