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An Iterative Processing Scheme for Enhancing the Map Reduce Using Map Information Storage in Wireless Environment

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Abstract

The intention is to design the schemes for the enhancements for the enhancement of behaviour of map reduction scheme when it is employed for the iterative processing. The iterative processing has usually employed the place the information is restored episodically to imitate minimal alterations to the input information set. For minimizing the delays in reanalysis of unaltered information announces the schemes which precisely estimate only the information only when the information that has been modified. It integrates the concept of blooms screening. The blooms screening is a space effective information framework which could have a precise possibility verification in case the information is altered or not. The conventional systems process the comprehensive information in a minimal proportion or none of the information is altered. This the time arduous and it guzzles the immense number of CPU clock cycles moreover to process the information has not been altered. For minimizing the consumption of CPU clock cycles the system is designed so that the scheme of implementation employs blooms screening aids enhancing the behaviour of the system nearly to 17% as evaluated to the conventional system.

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References

  1. Ene, S., Nicolae, B., Costan, A., & Antoniu, G. (2014). To overlap or not to overlap: Optimizing incremental MapReduce computations for on-demand data upload, in data-intensive computing in the clouds (DataCloud) In 2014 5th international workshop on (pp. 9–16).

  2. Deke, G., Yunhao, L., XiangYang, L., & Panlong, Y. (2010). False negative problem of counting bloom filter. IEEE Transactions on Knowledge and Data Engineering,22(5), 651–664.

    Article  Google Scholar 

  3. Sivaparthipan, C. B., Karthikeyan, N., & Karthik, S (2018). Designing statistical assessment healthcare information system for diabetics analysis using big data. Multimedia Tools and Applications.

  4. BalaAnand, M., Karthikeyan, N., Karthik, S., & Sivaparthipan, C. B. (2017). A survey on BigData with various V’s on comparison of apache hadoop and apache spark. Advances in Natural and Applied Sciences,11, 362–370.

    Google Scholar 

  5. Yanfeng, Z., Shimin, C., Qiang, W., & Ge, Y. (2015). MapReduce: Incremental MapReduce for mining evolving big data. IEEE Transactions on Knowledge and Data Engineering,27(7), 1906–1919.

    Article  Google Scholar 

  6. BalaAnand, M., Karthikeyan, N., & Karthik, S. (2019). Envisioning social media information for big data using big vision schemes in wireless environment. Wireless Personal Communications. https://doi.org/10.1007/s11277-019-06590-w.

    Article  Google Scholar 

  7. BalaAnand, M., Karthikeyan, N., & Karthik, S. (2018). Designing a framework for communal software: Based on the assessment using relation modelling. International Journal of Parallel Programming. https://doi.org/10.1007/s10766-018-0598-2.

    Article  Google Scholar 

  8. Agarwal, P., Shroff, G., & Malhotra, P. (2013). Approximate incremental big-data harmonization. In 2013 IEEE International Congress on Big Data (BigData Congress) (pp. 118–125).

  9. Bhatotia, P., Wieder, A., Rodrigues, R., Acar, U.A., & Pasquin, R. (2011). Incoop: MapReduce for incremental computations. In Proceedings of the 2nd ACM symposium on cloud computing cascais (pp. 1–14). Portugal: ACM.

  10. Bhushan, M., Singh, M., & Yadav, S. K. (2015). Big data query optimization by using locality sensitive bloom filter. In 2015 2nd International conference on computing for sustainable global development (INDIACom) (pp. 1424–1428).

  11. Cairong, Y., Xin, Y., Ze, Y., Min, L., & Xiaolin, L. (2012). IncMR: Incremental data processing based on MapReduce. In 2012 IEEE 5th international conference on cloud computing (CLOUD) (pp. 534–541).

  12. Fang, H., Kodialam, M., & Lakshman, T. V. (2008). Incremental bloom filters. In INFOCOM 2008.The 27th conference on computer communications (p. 1). IEEE.

  13. Jun, Z., Zhu, L., & Yong, Y. (2012). Parallelized incremental support vector machines based on MapReduce and Bagging technique. In 2012 International conference on information science and technology (ICIST) (pp. 297–301).

  14. Khopkar, S. S., Nagi, R., & Nikolaev, A. G. (2012). An efficient map-reduce algorithm for the incremental computation of all-pairs shortest paths in social networks. In 2012 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) (pp. 1144–1148).

  15. Vulimiri, A., et al. (2015). WANalytics: analytics for a geo-distributeddata-intensive world. In CIDR.

  16. Reed, A., & Dongarra, J. (2015). Exascale computing and big data. Communications of the ACM,58(7), 56–68.

    Article  Google Scholar 

  17. Hawick, K. A., et al. (2003). Distributed frameworks and parallelalgorithms for processing large-scale geographic data. ParallelComputing,29(10), 1297–1333.

    Google Scholar 

  18. Kloudas, K., et al. (2015). PIXIDA: Optimizing data parallel jobs in wide-area data analytics. PVLDB,9(2), 72–83.

    Google Scholar 

  19. Tudoran, R., et al. (2014). Bridging data in the clouds: An environment-aware system for geographically distributed data transfers. In CCGrid (pp. 92–101).

  20. Tudoran, R., Antoniu, G., & Boug´e, L. (2013). SAGE: Geo-distributed streaming data analysis in clouds. In IPDPS workshops (pp. 2278–2281).

  21. Pu, Q., et al. (2015). Low latency geo-distributed data analytics. In SIGCOMM (pp. 421–434).

  22. Zhang, Q., et al. (2014) Improving Hadoop service provisioning in a geographically distributed cloud. In IEEE Cloud (pp. 432–439).

  23. Rabkin, A., Arye, M., Sen, S., Pai, V. S., & Freedman, M. J. (2013). Makingevery bit count in wide-area analytics. In HotOS.

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Authors and Affiliations

  1. RVS College of Engineering and Technology, Sulur, Coimbatore, India

    K. Arunkumar

  2. SNS College of Engineering, Coimbatore, India

    N. Karthikeyan

  3. SNS College of Technology, Coimbatore, India

    S. Karthik

Authors
  1. K. Arunkumar
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  2. N. Karthikeyan
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  3. S. Karthik
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Correspondence to K. Arunkumar.

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Arunkumar, K., Karthikeyan, N. & Karthik, S. An Iterative Processing Scheme for Enhancing the Map Reduce Using Map Information Storage in Wireless Environment. Wireless Pers Commun 111, 1575–1587 (2020). https://doi.org/10.1007/s11277-019-06941-7

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