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In data-driven systems, data exploration is imperative for making real-timedecisions. However, big data is stored in massive databases that are difficultto retrieve. Approximate Query Processing (AQP) is a technique for providingapproximate answers to aggregate queries based on a summary of the data(synopsis) that closely replicates the behavior of the actual data, which canbe useful where an approximate answer to the queries would be acceptable in afraction of the real execution time. This study explores the novel utilizationof Generative Adversarial Networks (GANs) in the generation of tabular datathat can be employed in AQP for synopsis construction. We thoroughlyinvestigate the unique challenges posed by the synopsis construction process,including maintaining data distribution characteristics, handling boundedcontinuous and categorical data, and preserving semantic relationships and thenintroduce the advancement of tabular GAN architectures that overcome thesechallenges. Furthermore, we propose and validate a suite of statistical metricstailored for assessing the reliability of the GAN-generated synopses. Ourfindings demonstrate that advanced GAN variations exhibit a promising capacityto generate high-fidelity synopses, potentially transforming the efficiency andeffectiveness of AQP in data-driven systems.

GAN-based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and Solutions