Skip to main content

Part of the book series: Studies in Big Data ((SBD,volume 54))

  • 699 Accesses

Abstract

‘Big Data’ is a relative term used to describe a tremendously large data. The large data is inclusive of audio, video, unstructured text, social media information, and so much more. Its concept has gained wide publicity or attention in many disciplines. Interestingly, ‘Big data’ means different things to various disciplines. For example, in atmospheric study, ‘big data’ means volume of data as large as one terabytes and above. Meanwhile in particle physics, ‘big data’ is in petabytes and above. For communication outfit, ‘big data’ may mean zettabytes. Hence, there is the need for disciplinary and multi-disciplinary outfit or research institutes to embrace ‘big data’ technologies such as in-memory technologies, sensory (Internet of Things) equipment, Cloud Data Storage, magnetic storage, Big Data databases (e.g. MongoDB) etc.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Borne, K. (2014). Top 10 big data challenges—A serious look at 10 big data V’s. https://mapr.com/blog/top-10-big-data-challenges-serious-look-10-big-data-vs/. Accessed January 31, 2018.

  • Cleverism. (2018). What is big data? https://www.cleverism.com/brief-history-big-data/. Accessed January 31, 2018.

  • Dataversity. (2018). Big Data Trends for 2018, https://www.dataversity.net/big-data-trends-2018/. Accessed November 12, 2018.

  • Emetere, M. E. (2016a). Statistical examination of the aerosols loading over Mubi-Nigeria: The satellite observation analysis. Geographica Panonica, 20(1), 42–50.

    Google Scholar 

  • Emetere, M. E. (2016b). Numerical modelling of West Africa regional scale aerosol dispersion. Thesis submitted to Covenant University.

    Google Scholar 

  • Emetere, M. E. (2017a). Investigations on aerosols transport over micro- and macro-scale settings of West Africa. Environmental Engineering Research, 22(1), 75–86.

    Google Scholar 

  • Emetere, M. E. (2017b). Lightning as a source of electricity: Atmospheric modeling of electromagnetic fields. International Journal of Technology, 8, 508–518.

    Google Scholar 

  • Emetere, M. E. (2017c). Impacts of recirculation event on aerosol dispersion and rainfall patterns in parts of Nigeria. Global Nest Journal, 19(2), 344–352.

    Google Scholar 

  • Emetere, M. E. (2017d). Monitoring the 3-year thermal signatures of the Calbuco pre-volcano eruption event. Arabian Journal of Geoscience, 10, 94. https://doi.org/10.1007/s12517-017-2861-z.

  • Emetere, M. E., & Akinyemi, M. L. (2017). Documentation of atmospheric constants over Niamey, Niger: A theoretical aid for measuring instruments. Meteorological Applications, 24(2), 260–267.

    Article  Google Scholar 

  • Emetere, M. E., Akinyemi, M. L., & Akinojo, O. (2015a). A novel technique for estimating aerosol optical thickness trends using meteorological parameters. 2015 PIAMSEE: AIP Conference Proceedings, 1705(1), 020037.

    Google Scholar 

  • Emetere, M. E., Akinyemi, M. L., & Uno, U. E. (2015b). Computational analysis of aerosol dispersion trends from cement factory. In IEEE Proceedings 2015 International Conference on Space Science & Communication (pp. 288–291).

    Google Scholar 

  • Emetere, M. E., Akinyemi, M. L., & Akinojo, O. (2015c). Parametric retrieval model for estimating aerosol size distribution via the AERONET, LAGOS station. Environmental Pollution, 207(C), 381–390.

    Google Scholar 

  • Emetere, M. E., Akinyemi, M. L., & Akin-Ojo, O. (2015d). Aerosol optical depth pollution in selected areas trends over different regions of Nigeria: Thirteen years analysis. Modern Applied Science, 9(9), 267–279.

    Google Scholar 

  • Emetere, M. E., Akinyemi, M. L., & Edeghe, E. B. (2016). A simple technique for sustaining solar energy production in active convective coastal regions. International Journal of Photoenergy, 2016(3567502), 1–11. https://doi.org/10.1155/2016/3567502.

    Article  Google Scholar 

  • Foote, K. D. (2017). A brief history of big data. http://www.dataversity.net/brief-history-big-data/. Accessed January 30, 2018.

  • Qubole, (2008). The Future of Big Data and Machine Learning Is Clear: It’s All on the Cloud, https://www.qubole.com/blog/the-future-of-big-data-and-machine-learning-is-clear-its-all-on-the-cloud/. Accessed November 12, 2018.

  • Stephenson, D. (2013). 7 big data techniques that create business value. https://www.firmex.com/thedealroom/7-big-data-techniques-that-create-business-value/. Accessed January 31, 2018.

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Moses Eterigho Emetere or Moses Eterigho Emetere .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Cite this chapter

Emetere, M.E. (2019). Big Data and Further Analysis. In: Environmental Modeling Using Satellite Imaging and Dataset Re-processing. Studies in Big Data, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-030-13405-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-13405-1_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13404-4

  • Online ISBN: 978-3-030-13405-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics