  Datamation is designed to animate an analysis pipeline step by step, which is
an intuitive and effective way to interpret the results from data analysis.
However, creating a datamation is not easy. A qualified datamation needs to not
only provide a correct analysis result but also ensure that the data flow and
animation are coherent. Existing animation authoring tools focus on either
leveraging algorithms to automatically generate an animation based on
user-provided charts or building graphical user interfaces to provide a
programming-free authoring environment for users. None of them are able to help
users translate an analysis task into a series of data operations to form an
analysis pipeline and visualize them as a datamation. To fill this gap, we
introduce Datamator, an intelligent authoring tool developed to support
datamation design and generation. It leverages a novel data query decomposition
model to allow users to generate an initial datamation by simply inputting a
data query in natural language. The initial datamation can be refined via rich
interactions and a feedback mechanism is utilized to update the decomposition
model based on user knowledge and preferences. Our system produces an animated
sequence of visualizations driven by a set of low-level data actions. It
supports unit visualizations, which provide a mapping from each data item to a
unique visual mark. We demonstrate the effectiveness of Datamator via a series
of evaluations including case studies, performance validation, and a controlled
user study.
