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 everincreasing 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������������ � , a system that takes advantage of attribute semantics and data-mining models to perform lossy compression of massive data tables. ������������ � 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,������������ � selects a certain subset of 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 and construction cost of possible CaRT predictors,������������ � employs sophisticated learning techniques and novel combinatorial optimization algorithms. Our experimentation with several real-life data sets offers convincing evidence of the effectiveness of������������ � ’s model-based approach –������������ � is able to consistently yield substantially better compression ratios than existing semantic or syntactic compression tools (e.g., gzip) while utilizing only small data samples for model inference. 1
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