In the e-business domain where data objects are quantitatively large, measuring similarity to find the same or similar objects is important. It basically requires comparing and computing the features of objects in pairs, and therefore takes longer time as the amount of data becomes bigger. Recent studies have shown various algorithms to efficiently perform it. Most of them show their performance superiority by empirical tests over some sets of data. In this paper, we introduce those data sets, present their characteristics and the meaningful threshold values that each of data sets contain in nature. The analysis on practical data sets with respect to their threshold values may serve as a referential baseline to the future experiments of newly developed algorithms.