Multidimensional benchmarking in data warehouses

Article type: Research Article

Authors: Campbell, Akiko^{a; b} | Mao, Xiangbo^{b; c; *} | Pei, Jian^{b; d} | Al-Barakati, Abdullah^d

Affiliations: [a] LifeLabs Medical Laboratories, Burnaby, BC, Canada | [b] Simon Fraser University, Burnaby, BC, Canada | [c] Zhejiang University, Hangzhou, Zhejiang, China | [d] King Abdulaziz University, Jeddah, Saudi Arabia

Correspondence: [*] Corresponding author: Xiangbo Mao, 8888 University Drive, Burnaby, BC, Canada V5A1S6. Tel.: +1 604 442 6805; E-mail: [email protected].

Abstract: Benchmarking is among the most widely adopted practices in business today. However, to the best of our knowledge, conducting multidimensional benchmarking in data warehouses has not been explored from a technical efficiency perspective. In this paper, we formulate benchmark queries in the context of data warehousing and business intelligence, and develop algorithms to answer benchmark queries efficiently. Our methods employ a few 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 and the Weather data sets demonstrates the efficiency and the scalability of our methods.

Keywords: Benchmarking, data cubes, data warehouse, business intelligence

DOI: 10.3233/IDA-160035

Journal: Intelligent Data Analysis, vol. 21, no. 4, pp. 781-801, 2017