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High Performance Analytical Data Reconstructing Strategy in Data Warehouse

In: Current Trends in High Performance Computing and Its Applications

Author

Listed:
  • Xiufeng Xia

    (Northeastern University, School of Information Science & Engineering
    Shenyang Institute of Aeronautical Engineering, School of Computer Science & Engineering)

  • Qing Li

    (Shanghai University, School of Computer Science & Engineering)

  • Lingyu Xu

    (Shanghai University, School of Computer Science & Engineering)

  • Weidong Sun

    (Shenyang Institute of Aeronautical Engineering, School of Computer Science & Engineering)

  • Suixiang Shi

    (Northeastern University, School of Information Science & Engineering)

  • Ge Yu

    (Northeastern University, School of Information Science & Engineering)

Abstract

Many special requirements in on-line analytical processing and data mining need to access the historical available data of data warehouse. The computing performance is related to the analytical data reconstruction. We first analyze the differences between historical and current available data in data warehouse due to the time causation, and then advance the code equivalence and code coincidence. The high performance arithmetic by reconstructing the historical data to current analytical dada by using code reverting and abstracting methods are discussed.

Suggested Citation

  • Xiufeng Xia & Qing Li & Lingyu Xu & Weidong Sun & Suixiang Shi & Ge Yu, 2005. "High Performance Analytical Data Reconstructing Strategy in Data Warehouse," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 527-531, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_72
    DOI: 10.1007/3-540-27912-1_72
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