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Multidimensional Business Benchmarking Analysis on Data Warehouses

Multidimensional Business Benchmarking Analysis on Data Warehouses

Akiko Campbell, Xiangbo Mao, Jian Pei, Abdullah Al-Barakati
Copyright: © 2017 |Volume: 13 |Issue: 1 |Pages: 25
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781522511311|DOI: 10.4018/IJDWM.2017010103
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MLA

Campbell, Akiko, et al. "Multidimensional Business Benchmarking Analysis on Data Warehouses." IJDWM vol.13, no.1 2017: pp.51-75. http://doi.org/10.4018/IJDWM.2017010103

APA

Campbell, A., Mao, X., Pei, J., & Al-Barakati, A. (2017). Multidimensional Business Benchmarking Analysis on Data Warehouses. International Journal of Data Warehousing and Mining (IJDWM), 13(1), 51-75. http://doi.org/10.4018/IJDWM.2017010103

Chicago

Campbell, Akiko, et al. "Multidimensional Business Benchmarking Analysis on Data Warehouses," International Journal of Data Warehousing and Mining (IJDWM) 13, no.1: 51-75. http://doi.org/10.4018/IJDWM.2017010103

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Abstract

Benchmarking analysis has been used extensively in industry for business analytics. Surprisingly, how to conduct benchmarking analysis efficiently over large data sets remains a technical problem untouched. In this paper, the authors formulate benchmark queries in the context of data warehousing and business intelligence, and develop a series of algorithms to answer benchmark queries efficiently. Their methods employ several interesting ideas and the state-of-the-art data cube computation techniques to reduce the number of aggregate cells that need to be computed and indexed. An empirical study using the TPC-H data sets and the Weather data set demonstrates the efficiency and scalability of their methods.

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