OCSLM: Optimized Clustering with Statistical Based Local Model to Leverage Distributed Mining in Grid Architecture

  • M. Shahina Parveen
  • G. Narsimha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 765)


Grid computing offers significant platform of technologies where complete computational potential of resources could be harnessed in order to solve a complex problem. However, applying mining approach over distributed grid is still an open-end problem. After reviewing the existing system, it is found that existing approaches doesn’t emphasized on data diversity, data ambiguity, data dynamicity, etc. which leads to inapplicability of mining techniques on distributed data in grid. Hence, the proposed system introduces Optimized Clustering with Statistical Based local Model (OCSLM) in order to address this problem. A simple and yet cost effective machine-learning based optimization principle is presented which offers the capability to minimize the errors in mined data and finally leads to accumulation of superior quality of mined data. The study outcome was found to offer better sustainability with optimal computational performance when compared to existing clustering algorithms on distributed networking system.


Grid computing Grid network Clustering Data mining Machine learning 


  1. 1.
    Mahmood, Z.: Software Project Management for Distributed Computing: Life-Cycle Methods for Developing Scalable and Reliable Tools. Springer, Cham (2017)CrossRefGoogle Scholar
  2. 2.
    Kacsuk, P., Kranzlmüller, D., Németh, Z., Volkert, J.: Distributed and Parallel Systems: Cluster and Grid Computing. Springer, New York (2017)Google Scholar
  3. 3.
    Magoules, F., Pan, J., Tan, K.A., Kumar, A.: Introduction to Grid Computing. CRC Press, New York (2009)zbMATHGoogle Scholar
  4. 4.
    Kosar, T.: Data Intensive Distributed Computing: Challenges and Solutions for Large-scale Information Management: Challenges and Solutions for Large-scale Information Management. IGI Global, Hershey, PA (2012)Google Scholar
  5. 5.
    Wang, L., Jie, W., Chen, J.: Grid Computing: Infrastructure, Service, and Applications. CRC Press, New York (2009)CrossRefGoogle Scholar
  6. 6.
    Suwan, A., Siewe, F., Abwnawar, N.: Towards monitoring security policies in grid computing: a survey. In: SAI Computing Conference (SAI), London, pp. 573–578 (2016)Google Scholar
  7. 7.
    Setia, H., Jain, A.: Literature survey on various scheduling approaches in grid computing environment. In: IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, pp. 1–4 (2016)Google Scholar
  8. 8.
    Liu, Y., Rong, Z., Jun, C., Ping, C.Y.: Survey of grid and grid computing. In: International Conference on Internet Technology and Applications, Wuhan, pp. 1–4 (2011)Google Scholar
  9. 9.
    Aggarwal, S., Agarwal, N., Jain, M.: Uncertain data mining: a review of optimization methods for UK-means. In: 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp. 3672–3677 (2016)Google Scholar
  10. 10.
    Shahina Parveen, M., Narsimha, G.: Scaling effectivity of research contributions in distributed data mining over grid infrastructures. Commun. Appl. Electron. 3(8), 17–27 (2015). Published by Foundation of Computer Science (FCS), NY, USACrossRefGoogle Scholar
  11. 11.
    Zhang, Y., Yang, R., Zhang, K., Jiang, H., Zhang, J.J.: Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch. IEEE Intell. Syst. 32(4), 59–63 (2017)CrossRefGoogle Scholar
  12. 12.
    Asad, Z., Chaudhry, M.A.R.: A two-way street: green big data processing for a greener smart grid. IEEE Syst. J. 11(2), 784–795 (2017)CrossRefGoogle Scholar
  13. 13.
    Ergin, M.O., Handziski, V., Wolisz, A.: Grid-based position discovery. In: International Conference on Localization and GNSS (ICL-GNSS), Barcelona, pp. 1–8 (2016)Google Scholar
  14. 14.
    Goyal, A., et al.: Asset health management using predictive and prescriptive analytics for the electric power grid. IBM J. Res. Dev. 60(1), 4:1–4:14 (2016)CrossRefGoogle Scholar
  15. 15.
    Hochbaum, D.S., Baumann, P.: Sparse computation for large-scale data mining. IEEE Trans. Big Data 2(2), 151–174 (2016)CrossRefGoogle Scholar
  16. 16.
    Jindal, A., Dua, A., Kaur, K., Singh, M., Kumar, N., Mishra, S.: Decision tree and SVM-based data analytics for theft detection in smart grid. IEEE Trans. Industr. Inf. 12(3), 1005–1016 (2016)CrossRefGoogle Scholar
  17. 17.
    Lee, S., et al.: CloudSocket: Smart grid platform for datacenters. In: IEEE 34th International Conference on Computer Design (ICCD), Scottsdale, AZ, pp. 436–439 (2016)Google Scholar
  18. 18.
    Aouad, L.M., An-LeKhac, N., Kechadi, T.: Grid-based approaches for distributed data mining applications. J. Algorithms Comput. Technol. 3(4), 517–534 (2009)CrossRefGoogle Scholar
  19. 19.
    Giri, J.: Proactive Management of the Future Grid. IEEE Power Energy Technol. Syst. J. 2(2), 43–52 (2015)CrossRefGoogle Scholar
  20. 20.
    Kar, S., Samantaray, S.R., Zadeh, M.D.: Data-mining model based intelligent differential microgrid protection scheme. IEEE Syst. J. 11(2), 1161–1169 (2017)CrossRefGoogle Scholar
  21. 21.
    Kumar, S.M.D., Jumnal, A.: A real-time grid enabled test bed for sharing and searching documents among universities. In: Second International Conference on Advances in Computing and Communication Engineering, Dehradun, pp. 604–609 (2015)Google Scholar
  22. 22.
    Loia, V., Terzija, V., Vaccaro, A., Wall, P.: An affine-arithmetic-based consensus protocol for smart-grid computing in the presence of data uncertainties. IEEE Trans. Industr. Electron. 62(5), 2973–2982 (2015)CrossRefGoogle Scholar
  23. 23.
    Luan, W., Peng, J., Maras, M., Lo, J., Harapnuk, B.: Smart meter data analytics for distribution network connectivity verification. IEEE Trans. Smart Grid 6(4), 1964–1971 (2015)CrossRefGoogle Scholar
  24. 24.
    Xu, Q., et al.: A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining. IEEE Trans. Sustain. Energy 6(4), 1283–1291 (2015)CrossRefGoogle Scholar
  25. 25.
    García-Galán, S., Prado, R.P., Expósito, J.E.M.: Swarm fuzzy systems: knowledge acquisition in fuzzy systems and its applications in grid computing. IEEE Trans. Knowl. Data Eng. 26(7), 1791–1804 (2014)CrossRefGoogle Scholar
  26. 26.
    Chung, W.C., Hsu, C.J., Lai, K.C., Li, K.C., Chung, Y.C.: Direction-aware resource discovery service in large-scale grid and cloud computing. In: IEEE International Conference on Service-Oriented Computing and Applications (SOCA), Irvine, CA, pp. 1–8 (2011)Google Scholar
  27. 27.
    Dan, Z.: Study on the construction of digital library model under semantic Grid environment. In: International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, pp. V1-494–V1-498 (2010)Google Scholar
  28. 28.
    Pan, S., Morris, T., Adhikari, U.: Developing a hybrid intrusion detection system using data mining for power systems. IEEE Trans. Smart Grid 6(6), 3104–3113 (2015)CrossRefGoogle Scholar
  29. 29.
    Tlili, R., Slimani, Y.: Executing association rule mining algorithms under a grid computing environment. ACM J., 55–61 (2011)Google Scholar
  30. 30.
    Guan, T. et al.: Enhancing Grid service discovery with a semantic wiki and the concept matching approach. In: Fifth International Conference on Semantics, Knowledge and Grid, Zhuhai, pp. 208–215 (2009)Google Scholar
  31. 31.
    Cesario, E., Talia, D.: A failure handling framework for distributed data mining services on the grid. In: 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing, Ayia Napa, pp. 70–79 (2011)Google Scholar
  32. 32.
    Shahina Parveen, M., Narsimha, G.: Distributed data mining approaches as services on the grid infrastructure. In: National Conference on Soft Computing and Knowledge Discovery (2012)Google Scholar
  33. 33.
    Shahina Parveen, M., Narsimha, G.: Optimized clustering with statistical-based local model for replica management in DDM over grid. Software Engineering Perspectives and Application in Intelligent Systems, vol. 2. Springer, Cham (2016)Google Scholar
  34. 34.
    Shahina Praveen, M., Narsimha, G.: SADM: Sophisticated architecture of distributed mining over grid infrastructure. International Journal of Computer Science and Electronics Engineering (IJCSEE) 4(3), 129–134 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJNTUHyderabadIndia
  2. 2.Professor of CSE and HODJNTUH-CESSulthanpur, Sanga Reddy (D)India

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