  Analysts commonly investigate the data distributions derived from statistical
aggregations of data that are represented by charts, such as histograms and
binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate
queries are implicitly executed through such a process. Datasets are constantly
extremely large; thus, the response time should be accelerated by calculating
predefined data cubes. However, the queries are limited to the predefined
binning schema of preprocessed data cubes. Such limitation hinders analysts'
flexible adjustment of visual specifications to investigate the implicit
patterns in the data effectively. Particularly, RSATree enables arbitrary
queries and flexible binning strategies by leveraging three schemes, namely, an
R-tree-based space partitioning scheme to catch the data distribution, a
locality-sensitive hashing technique to achieve locality-preserving random
access to data items, and a summed area table scheme to support interactive
query of aggregated values with a linear computational complexity. This study
presents and implements a web-based visual query system that supports visual
specification, query, and exploration of large-scale tabular data with
user-adjustable granularities. We demonstrate the efficiency and utility of our
approach by performing various experiments on real-world datasets and analyzing
time and space complexity.
