SPARTAN: A Model-Based Semantic Compression System for Massive Data Tables

While a variety of lossy compression schemes have been developed for certain forms of digital data (e.g., images, audio, video), the area of lossy compression techniques for arbitrary data tables has been left relatively unexplored. Nevertheless, such techniques are clearly motivated by the ever-increasing data collection rates of modern enterprises and the need for effective, guaranteed-quality approximate answers to queries over massive relational data sets. In this paper, we propose SPARTAN, a system that takes advantage of attribute semantics and data-mining models to perform lossy compression of massive data tables. SPARTAN is based on the novel idea of exploiting predictive data correlations and prescribed error tolerances for individual attributes to construct concise and accurate Classification and Regression Tree (CaRT) models for entire columns of a table. More precisely, SPARTAN selects a certain subset of attributes (referred to as predicted attributes) for which no values are explicitly stored in the compressed table; instead, concise CaRTs that predict these values (within the prescribed error bounds) are maintained. To restrict the huge search space of possible CaRT predictors, SPARTAN first builds a Bayesian network to uncover strong dependencies and predictive correlations in the data.