  Data visualization is by far the most commonly used mechanism to explore
data, especially by novice data analysts and data scientists. And yet, current
visual analytics tools are rather limited in their ability to guide data
scientists to interesting or desired visualizations: the process of visual data
exploration remains cumbersome and time-consuming. We propose zenvisage, a
platform for effortlessly visualizing interesting patterns, trends, or insights
from large datasets. We describe zenvisage's general purpose visual query
language, ZQL ("zee-quel") for specifying the desired visual trend, pattern, or
insight - ZQL draws from use-cases in a variety of domains, including biology,
mechanical engineering, climate science, and commerce. We formalize the
expressiveness of ZQL via a visual exploration algebra, and demonstrate that
ZQL is at least as expressive as that algebra. While analysts are free to use
ZQL directly, we also expose ZQL via a visual specification interface that we
describe in this paper. We then describe our architecture and optimizations,
preliminary experiments in supporting and optimizing for ZQL queries in our
initial zenvisage prototype, and a user study to evaluate whether data
scientists are able to effectively use zenvisage for real applications.
