Advertisement

Back-Propagated Neural Network on MapReduce Frameworks: A Survey

  • Jenish DhananiEmail author
  • Rupa Mehta
  • Dipti Rana
  • Bharat Tidke
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 851)

Abstract

Back-propagated Neural Network (BPNN) is a popular supervised learning technique utilized in machine learning and deep learning to extract knowledge. However, BPNN has shown poor time and space complexity with large volume of data due to its in-memory processing. Huge amount of data sourced by ample of applications and services on the Internet needs to be distributed and parallel environment like MapReduce framework for processing and storage. The large volume of data is divided into smaller chunks and MapReduce framework exploits the cluster of commodity hardware to process these chunks. This research focuses on various MapReduce-based BPNN algorithms such as single-pass and multi-pass MapReduce-based BPNN. The research is intended to perform in-depth comparisons of these techniques considering various parameters such as computation complexity of Mapper and Reducer, granularity of Mapper’s output, number of MapReduce jobs required to build BPNN, and Mapper’s <Key, Value> pairs.

Keywords

MapReduce BPNN Distributed and parallel framework Big data Distributed file system 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Apache hadoop. https://hadoop.apache.org/. Accessed 08 Dec 2017
  2. 2.
    Apache spark. https://spark.apache.org/. Accessed 20 Dec 2017
  3. 3.
    Big data. https://www.gartner.com/it-glossary/big-data. Accessed 01 Jan 2018
  4. 4.
    Alekhya, G.S.S.L., Lydia, E.L., Challa, N.: Big data analytics: a survey. Int. J. Appl. Innov. Eng. Manag. (IJAIEM) 5(10), 090–106 (2016)Google Scholar
  5. 5.
    Binhan, Z., Wang, W., Zhang, X.: Training backpropagation neural network in MapReduce. In: Proceedings of International Conference on Computer, Communications and Information Technology (CCIT 2014), pp. 22–25. Atlantis-Press (2014)Google Scholar
  6. 6.
    Bu, Y., Howe, B., Balazinska, M., Ernst, M.D.: Haloop: efficient iterative data processing on large clusters. Proc. VLDB Endow. 3(1–2), 285–296 (2010)CrossRefGoogle Scholar
  7. 7.
    Chen, K., Kurgan, L.A.: Neural networks in bioinformatics. Handbook of Natural Computing, pp. 565–583. Springer, Berlin, Heidelberg (2012)Google Scholar
  8. 8.
    Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)CrossRefGoogle Scholar
  9. 9.
    Chu, C.T., Kim, S.K., Lin, Y.A., Yu, Y., Bradski, G., Ng, A.Y., Olukotun, K.: Map-reduce for machine learning on multicore. In: Proceedings of NIPS, vol. 6, pp. 281–288. Vancouver, BC (2006)Google Scholar
  10. 10.
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  11. 11.
    Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. CRC press (1994)Google Scholar
  12. 12.
    Ekanayake, J., Li, H., Zhang, B., Gunarathne, T., Bae, S.H., Qiu, J., Fox, G.: Twister: a runtime for iterative MapReduce. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, pp. 810–818. ACM (2010)Google Scholar
  13. 13.
  14. 14.
    Gantz, J., Reinsel, D.: Extracting value from chaos. IDC iview 1142(2011), 1–12 (2011)Google Scholar
  15. 15.
    Ghavami, P., Kapur, K.: Prognostics & artificial neural network applications in patient healthcare. In: 2011 IEEE Conference on Prognostics and Health Management (PHM), pp. 1–7. IEEE (2011)Google Scholar
  16. 16.
    Hecht-Nielsen, R.: Theory of the backpropagation neural network. Neural Networks for Perception. Elsevier, pp. 65–93 (1992)Google Scholar
  17. 17.
    Jiang, J., Zhang, J., Yang, G., Zhang, D., Zhang, L.: Application of back propagation neural network in the classification of high resolution remote sensing image: take remote sensing image of Beijing for instance. In: 2010 18th International Conference on Geoinformatics, pp. 1–6. IEEE (2010)Google Scholar
  18. 18.
    Li, R., Hu, H., Li, H., Wu, Y., Yang, J.: Mapreduce parallel programming model: a state-of-the-art survey. Int. J. Parallel Program. 44(4), 832–866 (2016)CrossRefGoogle Scholar
  19. 19.
    Liu, Y., Jing, W., Xu, L.: Parallelizing backpropagation neural network using MapReduce and cascading model. Comput. Intell. Neurosci. 2016 (2016)Google Scholar
  20. 20.
    Liu, Y., Xu, L., Li, M.: The parallelization of back propagation neural network in MapReduce and Spark. Int. J. Parallel Program. 45(4), 760–779 (2017)CrossRefGoogle Scholar
  21. 21.
    Liu, Y., Yang, J., Huang, Y., Xu, L., Li, S., Qi, M.: MapReduce based parallel neural networks in enabling large scale machine learning. Comput. Intell. Neurosci. 2015, 1–13 (2015)Google Scholar
  22. 22.
    Liu, Z., Li, H., Miao, G.: MapReduce-based backpropagation neural network over large scale mobile data. In: 2010 Sixth International Conference on Natural Computation (ICNC), vol. 4, pp. 1726–1730. IEEE (2010)Google Scholar
  23. 23.
    Nasrabadi, N.M.: Pattern recognition and machine learning. J. Electron. Imaging 16(4), 049901 (2007)Google Scholar
  24. 24.
    Patel, S.D., Quadros, D., Patil, V., Saxena, H.: Stock prediction using neural networks. Int. J. Eng. Manag. Res. (IJEMR) 7(2), 490–493 (2017)Google Scholar
  25. 25.
    Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G., Kozyrakis, C.: Evaluating MapReduce for multi-core and multiprocessor systems. In: IEEE 13th International Symposium on High Performance Computer Architecture, HPCA 2007, pp. 13–24. IEEE (2007)Google Scholar
  26. 26.
    Ren, G., Hua, Q., Deng, P., Yang, C.: FP-MRBP: fine-grained parallel MapReduce back propagation algorithm. In: International Conference on Artificial Neural Networks, pp. 680–687. Springer (2017)Google Scholar
  27. 27.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)CrossRefGoogle Scholar
  28. 28.
    Sharma, C.: Big data analytics using neural networks. Master’s thesis, San Jose State University, San Jose, CA 95192 (2014)Google Scholar
  29. 29.
    Talbot, J., Yoo, R.M., Kozyrakis, C.: Phoenix++: modular MapReduce for shared-memory systems. In: Proceedings of the Second International Workshop on MapReduce and its Applications, pp. 9–16. ACM (2011)Google Scholar
  30. 30.
    Tidke, B., Mehta, R., Dhanani, J.: A comprehensive survey and open challenges of mining bigdata. In: International Conference on Information and Communication Technology for Intelligent Systems, pp. 441–448. Springer (2017)Google Scholar
  31. 31.
    Zhao, K., Wang, C.: Sales forecast in e-commerce using convolutional neural network (2017). CoRR arXiv:1708.07946

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jenish Dhanani
    • 1
    Email author
  • Rupa Mehta
    • 1
  • Dipti Rana
    • 1
  • Bharat Tidke
    • 1
  1. 1.S. V. National Institute of TechnologySuratIndia

Personalised recommendations

Cite paper

Buy options