Estimating the Compression Fraction of an Index using Sampling
Data compression techniques such as null suppression and dictionary compression are commonly used in today's database systems. In order to effectively leverage compression, it is necessary to have the ability to efficiently and accurately estimate the size of an index if it were to be compressed. Such an analysis is critical if automated physical design tools are to be extended to handle compression. Several database systems today provide estimators for this problem based on random sampling. While this approach is efficient, there is no previous work that analyses its accuracy.