Prefetched wald adaptive boost classification based Czekanowski similarity MapReduce for user query processing with bigdata


With large volumes of data being generated in recent years and the inception of big data analytics on social media necessitates accurate user query processing with minimum time complexity. Several research works have been conducted in this area, to address accuracy and time complexity involved in query processing, in this work, Wald Adaptive Prefetched Boosting Classification based Czekanowski Similarity MapReduce (WAPBC–CSMR) technique is introduced. The WAPBC–CSMR technique uses the big dataset for processing large number of user queries. First, a technique called, Wald Adaptive Prefetched Boosting is employed with the objective of classifying the big dataset into different classes. To reduce the time involved in classification, in this paper a classifier called Gaussian distributive Rocchio is used that achieves significant classification in minimum time. With the classified results, a Likelihood Radio Test is applied to integrate the weak learner results into strong classification results. Then the classified and refined data are stored on the prefetcher cache. Upon reception of multi-dimensional user queries by the prefetch manager, the queries are now split into multiple keywords and are fed into the map phase, where mapping function is performed using Czekanowski Similarity Index with the objective of identifying the repeated jobs with maximum query processing accuracy. Followed by which the relevant data are retrieved from the prefetcher cache and repeated user query task is removed in the reduce phase via statistical function, therefore contributing to minimum time. Result analysis of WAPBC–CSMR is performed with big dataset using different metrics such as query processing accuracy, error rate and processing time for varied number of user queries. The result shows that WAPBC–CSMR technique enhances query processing accuracy and lessens the time as well as the error rate than the conventional methods.

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  1. 1.

    Fathimabi, S., Subramanyam, R.B.V., Somayajulu, D.V.L.N.: MSP: multiple sub-graph query processing using structure-based graph partitioning strategy and map-reduce. J. King Saud Univ.-Comput. Inf. Sci. 31, 22–34 (2019)

    Google Scholar 

  2. 2.

    Shi, M., Shen, D., Nie, T., Kou, Y., Yu, G.: HPPQ: a parallel package queries processing approach for large-scale data. Big Data Min. Anal. 1(2), 146–159 (2018)

    Article  Google Scholar 

  3. 3.

    Smys, S., Joe, C.V.: Big data business analytics as a strategic asset for health care industry. J. ISMAC 1(2), 92–100 (2019)

    Google Scholar 

  4. 4.

    Lee, K., Liu, L., Ganti, R.K., Srivatsa, M., Zhang, Q., Zho, Y.: Lightweight indexing and querying services for big spatial data. IEEE Trans. Serv. Comput. 12(3), 343–355 (2019)

    Article  Google Scholar 

  5. 5.

    Wang, H., Qin, X., Zhou, X., Li, F., Qin, Z., Zhu, Q., Wang, S.: Efficient query processing framework for a big data warehouse: an almost join-free approach. Front. Comput. Sci. 9(2), 224–236 (2015)

    MathSciNet  Article  Google Scholar 

  6. 6.

    Karthiban, M.K., Raj, J.S.: Big data analytics for developing secure internet of everything. J. ISMAC 1(02), 129–136 (2019)

    Google Scholar 

  7. 7.

    Tang, Y., Wang, H.S.Q., Liu, X.: Handling multi-dimensional complex queries in key-value data stores. Inf. Syst. 66, 82–96 (2017)

    Article  Google Scholar 

  8. 8.

    Birjali, M., Beni-Hssane, A., Erritali, M.: Evaluation of high-level query languages based on MapReduce in Big Data. J. Big Data 5, 1–21 (2018)

    Article  Google Scholar 

  9. 9.

    Xiao, G., Li, K., Zhou, X., Li, K.: Efficient monochromatic and bichromatic probabilistic reverse top-k query processing for uncertain big data. J. Comput. Syst. Sci. 89, 92–113 (2017)

    MathSciNet  Article  Google Scholar 

  10. 10.

    Smys, S.: Energy-aware security routing protocol for WSN in big-data applications. J. ISMAC 1(01), 38–55 (2019)

    Google Scholar 

  11. 11.

    Kim, M., Liu, L., Choi, W.: A GPU-aware parallel index for processing high-dimensional big data. IEEE Trans. Comput. 67(10), 1388–1402 (2018)

    MathSciNet  Article  Google Scholar 

  12. 12.

    Fan, H., Ma, Z., Wang, D., Liu, J.: Handling distributed XML queries over large XML data based on MapReduce framework. Inf. Sci. 453, 1–20 (2018)

    MathSciNet  Article  Google Scholar 

  13. 13.

    Franciscus, N., Ren, X., Stantic, B.: Precomputing architecture for flexible and efficient big data analytics. Vietnam J. Comput. Sci. 5(2), 133–142 (2018)

    Article  Google Scholar 

  14. 14.

    García-García, F., Corral, A., Iribarne, L., Vassilakopoulos, M.: Improving distance-join query processing with Voronoi-Diagram based partitioning in SpatialHadoop. Future Gener. Comput. Syst. 111, 723–740 (2020)

    Article  Google Scholar 

  15. 15.

    Pandian, A.P.: Enhanced edge model for big data in the internet of things based applications. J. Trends Comput. Sci. Smart Technol. (TCSST) 1(1), 63–73 (2019)

    Article  Google Scholar 

  16. 16.

    Al-Naami, K.M., Seker, S.E., Khan, L.: GISQAF: MapReduce guided spatial query processing and analytics system. Software 46(10), 1329–1349 (2016)

    Google Scholar 

  17. 17.

    Li, H., Yoo, J.: Efficient continuous skyline query processing scheme over large dynamic data sets. ETRI J. 38(6), 1197–1206 (2016)

    Article  Google Scholar 

  18. 18.

    Sahal, R., Khafagy, M.H., Omara, F.A.: Exploiting coarse-grained reused-based opportunities in big data multi-query optimization. J. Comput. Sci. 26, 432–452 (2018)

    Article  Google Scholar 

  19. 19.

    Joseph, S.I.T., Thanakumar, I.: Survey of data mining algorithm’s for intelligent computing system. J. Trends Comput. Sci. Smart Technol. (TCSST) 1(1), 14–24 (2019)

    Article  Google Scholar 

  20. 20.

    Wang, Y., Xia, Y., Fang, Q., Xu, X.: AQP++: a hybrid approximate query processing framework for generalized aggregation queries. J. Comput. Sci. 26, 419–431 (2018)

    MathSciNet  Article  Google Scholar 

  21. 21.

    Kim, T., Li, W., Behma, A., Cetindila, I., Vernica, R., Borkar, V., Carey, M.J., Li, C.: Similarity query support in big data management systems. Inf. Syst. 88, 10455 (2020)

    Article  Google Scholar 

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Correspondence to S. Tamil Selvan.

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Tamil Selvan, S., Balamurugan, P. & Vijayakumar, M. Prefetched wald adaptive boost classification based Czekanowski similarity MapReduce for user query processing with bigdata. Distrib Parallel Databases (2021).

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  • Big data query processing
  • Wald adaptive boosting classification
  • Gaussian distributive Rocchio classifier
  • MapReduce
  • Czekanowski similarity