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We propose a versatile approach to lightweight, approximate query processingby creating compact but tunably precise representations of larger quantities oforiginal tuples, coined bubbles. Instead of working with tables of tuples, thequery processing then operates on bubbles but leaves the traditional queryprocessing paradigms conceptually applicable. We believe this is a natural andviable approach to render approximate query processing feasible for large datain disaggregated cloud settings or in resource-limited scenarios. Bubbles aretunable regarding the compactness of enclosed tuples as well as the granularityof statistics and the way they are instantiated. For improved accuracy, we putforward a first working solution that represents bubbles via Bayesian networks,per table, or along foreign-key joins. To underpin the viability of theapproach, we report on an experimental evaluation considering thestate-of-the-art competitors, where we show clear benefits when assessing theestimation accuracy, execution time, and required disk space.

Approximate Query Processing via Tuple Bubbles