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A Study of the Correlation Between Internet Addiction and Aggressive Behaviour Among the Namibian University Students Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Skyline Computation for Big Data

Authors : R. D. Kulkarni, B. F. Momin

Published in: Data Science and Big Data Analytics

Publisher: Springer Singapore

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Abstract

From a multidimensional dataset, a skyline query extracts the data which satisfy the multiple preferences given by the user. The real challenge in skyline computation is to retrieve such data, in the optimum time. When the datasets are huge, the challenge becomes critical. In this paper, we address exactly this issue focusing on the big data. For this, we aim at utilizing the correlations observed in the user queries. These correlations and the results of historical skyline queries, executed on the same dataset, are very much helpful in optimizing the response time of further skyline computation. For the same purpose, we have earlier proposed a novel structure namely Query Profiler (QP). In this paper, we present a technique namely SkyQP to assert the effectiveness of this concept against the big data. We have also presented the time and space analysis of the proposed technique. The experimental results obtained assert the efficacy of the SkyQP technique.

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Metadata
Title
Skyline Computation for Big Data
Authors
R. D. Kulkarni
B. F. Momin
Copyright Year
2019
Publisher
Springer Singapore
Book
Data Science and Big Data Analytics

Print ISBN: 978-981-10-7640-4

Electronic ISBN: 978-981-10-7641-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-10-7641-1_23

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