High Performance Analytical Data Reconstructing Strategy in Data Warehouse

Xiufeng Xia (), Qing Li, Lingyu Xu, Weidong Sun, Suixiang Shi and Ge Yu
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Xiufeng Xia: Northeastern University, School of Information 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

A chapter in Current Trends in High Performance Computing and Its Applications, 2005, pp 527-531 from Springer

Abstract: 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.

Keywords: data warehouse; on-line analytical processing data mining; code equivalence; code coincidence; code reverting; code abstracting (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-27912-9_72

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DOI: 10.1007/3-540-27912-1_72

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