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Skyline Computation for Big Data

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Data Science and Big Data Analytics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 16))

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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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Walchand College of Engineering, Sangli, Maharashtra, India

    R. D. Kulkarni & B. F. Momin

Authors
  1. R. D. Kulkarni
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  2. B. F. Momin
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Corresponding author

Correspondence to R. D. Kulkarni .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  2. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

  3. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Aynur Unal

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Kulkarni, R.D., Momin, B.F. (2019). Skyline Computation for Big Data. In: Mishra, D., Yang, XS., Unal, A. (eds) Data Science and Big Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-10-7641-1_23

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