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A Distributed, Scalable Computing Facility for Big Data Analytics in Atmospheric Physics

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 721)

Abstract

Technological advancements in computing and communication have led to a flood of data from different domains like healthcare, social networks, Internet commerce and finance. Over the past few years a larger chunk of data comes from the domain of scientific applications, using simulated experiments or collected using sensors. This development calls for new architectural models for data acquisition, storage, and large-scale data analytics.

In this paper, we present a distributed and scalable computing facility, using low cost machines, which support analytics of large scientific data sets, constituting three sequential modules, namely data pre-processing, data analytics and data post-processing. These three modules together form a big data value chain which is illustrated through a case study related to Atmospheric physics.

Keywords

Data analytics Big data MapReduce Clustering Hadoop Atmospheric physics 

Notes

Acknowledgments

The experimental data sets for this work are obtained from the Atmospheric Physics research lab, Nowrosjee Wadia College, Pune. The authors would like to thank Dr. Gajanan Aher and his team for their enthusiastic support and guidance.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Reena Bharathi
    • 1
  • S. C. Shirwaikar
    • 1
  • Vilas Kharat
    • 1
  1. 1.Department of Computer ScienceSavitribai Phule Pune UniversityPuneIndia

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