skip to main content
10.1145/3291078.3291090acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiceelConference Proceedingsconference-collections
research-article

Construction of Distributed Database Model Of Mass Sports Data of College Based on Cloud Computing

Published:05 November 2018Publication History

ABSTRACT

This paper studies the construction and data mining of college sports database model. Aiming at the problems of low parallelism and unsystematic of current sports data processing, this paper proposes a mass of college sports data modeling and analysis method based on cloud computing to improve the management and analysis ability of college sports information, and constructs a distributed database model of college sports data. In the cloud computing environment, the database access model is designed, and the K-means data clustering method is used to mine the reliability of sports data, so as to realize the optimal information scheduling and retrieval analysis of college sports data. The simulation results show that the model has good real-time performance, high accuracy and reliability, and has a certain application prospect.

References

  1. Yan, H, F. Jiang, H. Zhang, W, Q. 2014. Estimate the Optimal Length of Branch Sequence for No Root Tree by the MCEM Algorithm. Natural Science Journal of Xiangtan college. 36, 2 (August. 2014), 13--16.Google ScholarGoogle Scholar
  2. Liu, Y. Su, J, F. and Zhu, M, Q. 2013. A Monte-Carlo Localization Algorithm Based on Iterated Cubature Particle Filter. Information and Control. 42, 5 (November. 2013), 632--638.Google ScholarGoogle Scholar
  3. Xu, J, L. Zhao, R, C. and Han, L. 2015. Vector exploring path optimization algorithm of superword level parallelism with subsection constraints. Journal of Computer Applications. 35, 4 (June. 2015), 950--955.Google ScholarGoogle Scholar
  4. Zhou, Y, L. 2015. Research on multi-channel data decoding technology based on FPGA. Internet of things technologies. 39, 3 (September. 2015), 32--34.Google ScholarGoogle Scholar
  5. Du, L, P. Li, X, G. and Zhou, Y, Z., et al. 2015. Application of improved point-wise mutual information in term extraction. Journal of Computer Applications. 20, 6 (June. 2015), 996--1000.Google ScholarGoogle Scholar
  6. Yamamoto, K. Carusone A. 2011.A 1-1-1-1 MASH Delta-Sigma Modulator With Dynamic Comparator-Based OTAs.IEEE Journal of Solid-State Circuits. 47, 8 (August. 2012), 1866--1883.Google ScholarGoogle Scholar
  7. CZIBULA, G. MARIAN, Z. and CZIBULA, I, G. 2014. Detecting software design defects using relational association rule mining.Knowledge and information systems. 42, 3 (January. 2014), 545--577. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. HILLS, J. BAGNALL, A. IGLESIA, B., et al. 2013. BruteSuppression: a size reduction method for Apriori rule sets.Journal of intelligent information systems. 40, 3 (June. 2013), 431--454. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Yang, T. Shia, B. C. Wei, J., et al. 2012. Mass Data Analysis and Forecasting Based on Cloud Computing. Journal of Software, 7, 10 (October. 2012), 2189--2195.Google ScholarGoogle ScholarCross RefCross Ref
  10. Hu, Z, H. Zhou, J, X. and Zhang, M, J. 2015. Methods for ranking college sports coaches based on data envelopment analysis and PageRank. Expert Systems, 32, 6 (December. 2015), 652--673. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Construction of Distributed Database Model Of Mass Sports Data of College Based on Cloud Computing

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        ICEEL '18: Proceedings of the 2018 2nd International Conference on Education and E-Learning
        November 2018
        224 pages
        ISBN:9781450365772
        DOI:10.1145/3291078

        Copyright © 2018 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 5 November 2018

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited
      • Article Metrics

        • Downloads (Last 12 months)5
        • Downloads (Last 6 weeks)2

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader