  Exponential growth in data collection is creating significant challenges for
data storage and analytics latency.Approximate Query Processing (AQP) has long
been touted as a solution for accelerating analytics on large datasets,
however, there is still room for improvement across all key performance
criteria. In this paper, we propose a novel histogram-based data synopsis
called PairwiseHist that uses recursive hypothesis testing to ensure accurate
histograms and can be built on top of data compressed using Generalized
Deduplication (GD). We thus show that GD data compression can contribute to
AQP. Compared to state-of-the-art AQP approaches, PairwiseHist achieves better
performance across all key metrics, including 2.6$ \times $ higher accuracy,
3.5$ \times $ lower latency, 24$ \times $ smaller synopses and 1.5--4$ \times $
faster construction time.
