Real datasets are often large enough to necessitate data compression.
Traditional `syntactic' data compression methods treat the table as a
large byte string and operate at the byte level.  The tradeoff in such
cases is usually between the ease of retrieval (the ease with which one
can retrieve a single tuple or attribute value without decompressing a
much larger unit) and the effectiveness of the compression. In this
regard, the use of semantic compression has generated considerable
interest and motivated certain recent works.

In this paper, we propose a semantic compression algorithm called
ItCompress (ITerative Compression), which achieves good compression
while permitting access even at attribute level without requiring the
decompression of a larger unit.  ItCompress iteratively improves the
compression ratio of the compressed output during each scan of the
table.  The amount of compression can be tuned based on the number of
iterations. Moreover, the initial iterations provide significant
compression, thereby making it a cost-effective compression technique.
Extensive experiments were conducted and the results indicate the
superiority of ItCompress with respect to previously known tehniques,
such as `SPARTAN' and `fascicles'.