Skip to main content

A New Conceptual Model for Big Data Analysis

  • Conference paper
  • First Online:
Genetic and Evolutionary Computing (ICGEC 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 579))

Included in the following conference series:

  • 646 Accesses

Abstract

In today modern societies, everywhere has to deal in one way or another with Big Data. Academicians, researchers, industrialists and many others have developed and still developing variety of methods, approaches and solutions for such big in volume, fast in velocity, versatile in variety and value in vicinity known as Big Data problems. However much has to be done concerning with Big Data analysis. Therefore, in this paper we propose a new concept named as Big Data Reservoir which can be interpreted as Ocean in which all most all information is stored, transmitted, communicated and extracted to utilize in our daily life. As a starting point of our proposed new concept, in this paper we shall consider a stochastic model for input/output analysis of Big Data by using Water Storage Reservoir Model in the real world. Specifically, we shall investigate the Big Data information processing in terms of stochastic model in the theory of water storage or dam theory. Finally, we shall present some illustrations with simulation.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight 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

  1. Hilbert, M.: Big Data for development: a review of promises and challenges. Dev. Policy Rev. 34(1), 135–174 (2015)

    Article  Google Scholar 

  2. Wang, L., et al.: Bigdatabench: a big data benchmark suite from internet services. In: Proceedings of 20th IEEE International Symposium on High Performance Computer Architecture, pp. 488–499 (2014)

    Google Scholar 

  3. Li, D.R., Yao, Y., Shao, Z.F.: Big Data in the smart city. Geomatics Inf. Sci. Wuhan Univ. 39(6), 630–640 (2014)

    Google Scholar 

  4. Al-Jarrah, O.Y., Yoo, P.D., Muhaidat, S., Karagiannidis, G.K., Taha, K.: Efficient machine learning for big data: a review. Big Data Res. 2(3), 87–93 (2015)

    Article  Google Scholar 

  5. Phatarfod, R.M.: Some aspects of stochastic reservoir theory. J. Hydrol. 30(3), 199–217 (1976)

    Article  Google Scholar 

  6. Bohling, G.: Stochastic simulation and reservoir modeling workflow. Aust. J. Basic Appl. Sci. 3, 330–341 (2005)

    Google Scholar 

  7. Karacan, C.Ö., Olea, R.A.: Stochastic reservoir simulation for the modeling of uncertainty in coal seam degasification. Fuel 148, 87–97 (2015)

    Article  Google Scholar 

  8. Browning, C., Kumin, H.: Stochastic reservoir systems with different assumptions for storage losses. Am. J. Oper. Res. 6(5), 414 (2016)

    Article  Google Scholar 

  9. Archibald, T.W., McKinnon, K.I.M., Thomas, L.C.: An aggregate stochastic dynamic programming model of multi-reservoir systems. Water Resour. Res. 33(2), 333–340 (1997)

    Article  Google Scholar 

  10. Thomas, A., McMahon, T.A., Pegram, G.S., Vogel, R.M., Peel, M.C.: Revisiting reservoir storage-yield relationships using a global stream flow database. Adv. Water Resour. 30, 1858–1872 (2007)

    Article  Google Scholar 

  11. Prabhu, N.U.: Some exact results for the finite dam. Ann. Math. Stat. 29(4), 1234–1243 (1958)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgment

This work is partially supported by the Grant of Telecommunication Advanced Foundation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thi Thi Zin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Zin, T.T., Tin, P., Hama, H. (2018). A New Conceptual Model for Big Data Analysis. In: Lin, JW., Pan, JS., Chu, SC., Chen, CM. (eds) Genetic and Evolutionary Computing. ICGEC 2017. Advances in Intelligent Systems and Computing, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-10-6487-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6487-6_7

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6486-9

  • Online ISBN: 978-981-10-6487-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics