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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