Conventional compression techniques exploit general redundancy features in data to compress them. For example, Huffman or Lempel-Ziv techniques compresses data by statistical modeling or string matching while the Burrows-Wheeler Transform simply sorts data by context to improve compressibility. On the other hand, data can often be compressed better by exploiting their specific features. For example, columns or fields in a database table tend to be sparse, but not rows. Techniques have been developed to either group related table columns or compute dependency among them to transform data and enhance compressibility. For example, see the paper Using Column Dependency to Compress Tables by B.D. Vo and K.-P. Vo presented at DCC 2004 and references thereof.