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Date(s) - 04/03/2017
10:30 am - 11:30 am



Data analysts often build visualizations as the first step in their analytical
workflow. However, when working with high-dimensional
datasets, identifying visualizations that show relevant or desired
trends in data can be laborious. We propose SEEDB, a visualization
recommendation engine to facilitate fast visual analysis:
given a subset of data to be studied, SEEDB intelligently explores
the space of visualizations, evaluates promising visualizations for
trends, and recommends those it deems most “useful” or “interesting”.
The two major obstacles in recommending interesting visualizations
are (a) scale: evaluating a large number of candidate
visualizations while responding within interactive time scales, and
(b) utility: identifying an appropriate metric for assessing interestingness
of visualizations. For the former, SEEDB introduces pruning
optimizations to quickly identify high-utility visualizations and
sharing optimizations to maximize sharing of computation across
visualizations. For the latter, as a first step, we adopt a deviationbased
metric for visualization utility, while indicating how we may
be able to generalize it to other factors influencing utility. We implement
SEEDB as a middleware layer that can run on top of any
DBMS. Our experiments show that our framework can identify interesting
visualizations with high accuracy. Our optimizations lead
to multiple orders of magnitude speedup on relational row and column
stores and provide recommendations at interactive time scales.
Finally, we demonstrate via a user study the effectiveness of our
deviation-based utility metric and the value of recommendations in
supporting visual analytics.


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