Abstract
Among all the popular V-words such as volume, velocity, and variety, etc. that describe the properties or dimensions of the big data, both volume and velocity can be measured, however so far variety has not found a way to become quantifiable. Although, it has been used to capture the most important aspect of the big data — the complexity of the big data caused by various relationships embedded in the data. This paper has applied Kolmogorov's Complexity Theory as the foundation to develop a practical measurement to quantify the impact of the variety of a big data set D towards a specific processing target r, i is an integer. Using the Inverse Compression Ratio defined by comparing the complexity of and the complexity of entire Relationships set R of the D, where R is the abstraction of the variety of the D, and r, belong to R. This measurement can help computing resource planning for conducting big data analytics.
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