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Engineering the compression of massive tables: An experimental approach

By Adam L. Buchsbaum, Donald F. Caldwell, Kenneth W. Church, Glenn S. Fowler and S. Muthukrishnan


We study the problem of compressing massive tables. We devise a novel compression paradigm—training for lossless compression— which assumes that the data exhibit dependencies that can be learned by examining a small amount of training material. We develop an experimental methodology to test the approach. Our result is a system, pzip, which outperforms gzip by factors of two in compression size and both compression and uncompression time for various tabular data. Pzip is now in production use in an AT&T network traffic data warehouse.

Year: 2000
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