Improving Efficiency of Quality Correlation in Query Using Spark

Authors

  • P. Suma Priya  M.Tech Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, India
  • Dr. P. Radhika Raju  Ad-hoc Assistant Professor, Department of Computer Science and Engineering, JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, India
  • Prof. P. Ananda Rao  Professor, Department of Computer Science and Engineering, JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT195432

Keywords:

Apache Spark, Service Composition, Quality of Service, Query of Quality Correlation (Q2C), Spark Distributed Technique, Quality Correlation Algorithms

Abstract

Service oriented models to easily development of the service-based systems, many services has been developed. Based on the service composition it creates the enterprise services. The main aim of the service-based system is to fulfil the specific quality constraints. Quality aware service composition cannot consider the quality correlations among services, to solve this problem develop an approach efficient query of quality correlations(Q2C). To consider these type of quality correlations further increasing search space, to reducing search space and provide efficient correlations develop quality correlation index graph(QCIG). In the QCIG quality constraints in the quality dimensions increased of pre-processing time and search time by the quality correlation. When raising this issue which affect the efficiency of Q2C. To improve efficiency of Q2C and get fast, better results use spark distributed technique.

References

  1. Qiang He, “Quality-Aware Service Selection for Service-Based Systems Based on Iterative Multi-Attribute Combinatorial Auction” IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 40, NO. 2, FEBRUARY 2014.
  2. Qiang He, Jun Han, Feifei Chen, Yanchun Wang “QoS-Aware Service Selection for Customizable Multi-Tenant Service-Based Systems: Maturity and Approaches “, 2015 IEEE 8th International Conference on Cloud Computing.
  3. Jong Myoung Kim, Hancheol Park, Gahgene Gweon, and Jeong Hur “The Correlation between Search Quality and Query Popularity” 2016 International Conference on Big Data and Smart Computing (Big Comp), 18-20 jan 2016.
  4.  M. Alrifai and T. Risse, "Combining Global Optimization with Local Selection for Efficient QoS-Aware Service Composition," Proc. the 18th International Conference on World Wide Web (WWW2009), Madrid, Spain, pp. 881-890, 2009 .
  5. Deng, H. Wu, D. Hu, and J. L. Zhao, "Service Selection for Composition with QoS Correlations," IEEE Transactions on Services Computing, vol. 9, no. 2, pp. 291-303, 2016.
  6. T. Yu, Y. Zhang, and K.-J. Lin, "Efficient Algorithms for Web Services Selection with End-to-End QoS Constraints," ACM Transactions on the Web, vol. 1, no. 1, 2007.
  7. M. Alrifai, D. Skoutas, and T. Risse, "Selecting Skyline Services for QoS-based Web Service Composition," Proc. the 19th International Conference on World Wide Web, Raleigh, North Carolina, USA, pp. 11- 20, 2010.
  8. Yiwenzhang, guangming cui,” Efficient query of quality correlation for service composition” IEEE transactions on service computing.
  9. Lina Barakat, Simon Miles, Michael Luck “Efficient Correlation-aware Service Selection” 2012 IEEE 19th International Conference on Web Services.
  10. Zhenqiu Huang, Wei Jiang, Songlin Hu, Zhiyong Liu “Effective Pruning Algorithm for QoS-Aware Service Composition” 2009 IEEE Conference on Commerce and Enterprise Computing.
  11. Jong Myoung Kim, Hancheol Park, “The Correlation between Search Quality and Query Popularity”2016 IEEE transactions on big data computing.
  12. John F. Kolen and Tim Hutcheson “Reducing the Time Complexity of the Fuzzy C-Means Algorithm” IEEE transactions on fuzzy systems, vol 10, n0 2, APRIL 2002.
  13. Zhen Ye, Xiaofang, Zhou “Genetic Algorithm Based QoS-Aware Service Compositions in Cloud Computing” 16th international conference, pp. 321-334, 2011.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-08-30

Issue

Volume 5, Issue 4, July-August-2019

Section

Research Articles

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Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
P. Suma Priya, Dr. P. Radhika Raju, Prof. P. Ananda Rao, " Improving Efficiency of Quality Correlation in Query Using Spark, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 4, pp.182-188, July-August-2019. Available at doi : https://doi.org/10.32628/CSEIT195432