Cloud based computational intelligence approaches to machine learning and big data analytics: literature survey

  • Authors

    • D Venkata Siva Reddy
    • R Vasanth Kumar Mehta
    2018-03-01
    https://doi.org/10.14419/ijet.v7i1.9.9817
  • Machine Learning, Cloud Computing, Big Data Analytics, Hadoop and Spark.

  • Abstract

    Today there are many sources through which we can access information from internet and based on the dependency now there is an over flow of data either in refined form or unrefined form. Handling large information is a complicated task. It has to overcome many challenges. There are some challenges like drawing useful information from undefined patterns which we can overcome by using data mining techniques but certain challenges like scalability, easy accessing of large data, time, or cost areto be handled in better sense.

    Machine learning helps in learning patterns from data automatically and can be leverage this data in further predictions. Cloud computing has now turned out to be a big alternative while handling big data because cloud itself carry certain features which help in analyzing and accessing big data in proper manner.Before switching to Cloud based approaches it provides an ease of set up or testing and is economical.Thus there is a demand for cloud computing and machine learning techniques with Hadoop or Spark.

    Mainly we are focusing on various works that have been done in handling big data. Here the analysis of various algorithms that are used by various researches in handling big data as well as outcome that they obtained in overcoming the challenges in handling big data.

  • References

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      [2] D. Agrawal, S. Das, A. E. Abbadi, "Big data and cloud computing: New wine or just new bottles?", PVLDB, vol. 3, no. 2, pp. 1647-1648.

      [3] D. Agrawal, A. El Abbadi, S. Antony, S. Das, "Data Management Challenges in Cloud Computing Infrastructures", DNIS, pp. 1-10, 2010.

      [4] Rajesh, M., and J. M. Gnanasekar. "Path Observation Based Physical Routing Protocol for Wireless Ad Hoc Networks." Wireless Personal Communications 97.1 (2017): 1267-1289.

      [5] Chen Hsinchun, H. Roger, L. Chiang et al., "Business intelligence and analytics: from big data to big impact", MIS Quarterly, vol. 36, no. 4, pp. 1165-1188, December 2012.

      [6] J. B. Rothnie, P. A. Bernstein, S. Fox, N. Goodman, M. Hammer, T. A. Landers, C. L. Reeve, D. W. Shipman, E. Wong, "Introduction to a System for Distributed Databases (SDD-1) ACM Trans", Database Syst., vol. 5, no. 1, pp. 1-17, 1980.

      [7] Venkata Narasimha Inukollu et al., "Security Issues Associated With Big Data Incloud Computing", International Journal of Network Security & Its Applications (IJNSA), vol. 6, no. 3, pp. 45-56, May 2014.

      [8] AiLingDuan et al., "Research and Practice of Distributed Parallel Search Algorithm on Hadoop_MapReduce", International Conference on Control Engineering and Communication Technology 2012 IEEE, 2012.

      [9] XiaofeiHou, Kumar T K Ashwin et al., "Dynamic Workload Balancing for Hadoop MapReduce", IEEE Fourth International Conference on Big Data and Cloud Computing 2014 IEEE, pp. 56-62, 2014.

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  • How to Cite

    Venkata Siva Reddy, D., & Vasanth Kumar Mehta, R. (2018). Cloud based computational intelligence approaches to machine learning and big data analytics: literature survey. International Journal of Engineering & Technology, 7(1.9), 186-188. https://doi.org/10.14419/ijet.v7i1.9.9817

    Received date: 2018-03-03

    Accepted date: 2018-03-03

    Published date: 2018-03-01