• DocumentCode
    2035377
  • Title

    The novel rule induction approach to dynamic big data in green energy

  • Author

    Chun-Che Huang ; Tseng, Tzu-Liang Bill ; Ming-Xuan Zhou

  • Author_Institution
    Dept. of Manage. Inf., Nat. Chi Nan Univ., Taiwan
  • fYear
    2015
  • fDate
    28-30 July 2015
  • Firstpage
    1427
  • Lastpage
    1432
  • Abstract
    With concerns about climate change growing it could be that green energy will begin to play a major role. Green Energy requires to resolve the optimization problem of electronic distribution, control, and storage with decision rule support. Due to the characteristics of green energy data nature - time dependency and variance, and big data, a novel approach to induct rules is required without re-computing rule sets from the very beginning, when new objects are updated to information system. The proposed approach updates rule sets by partly modifying original rule sets, hence a lot of time are saved, and it is especially useful when extracting rules from big data sets. The rules comparison helps decision maker to explore the marketing and qualified decision for renew energy distribution.
  • Keywords
    Big Data; climatology; data mining; decision making; green computing; optimisation; climate change; decision maker; decision rule support; dynamic big data; electronic distribution; energy distribution; green energy data nature; information system; optimization problem; rule induction approach; time dependency; time variance; Big data; Data mining; Distributed databases; Green products; Heuristic algorithms; Renewable energy sources; big data; data mining; decision rules; green/renew energy; rule induction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Science and Information Conference (SAI), 2015
  • Conference_Location
    London
  • Type

    conf

  • DOI
    10.1109/SAI.2015.7237334
  • Filename
    7237334