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Datamation is designed to animate an analysis pipeline step by step, which isan intuitive and effective way to interpret the results from data analysis.However, creating a datamation is not easy. A qualified datamation needs to notonly provide a correct analysis result but also ensure that the data flow andanimation are coherent. Existing animation authoring tools focus on eitherleveraging algorithms to automatically generate an animation based onuser-provided charts or building graphical user interfaces to provide aprogramming-free authoring environment for users. None of them are able to helpusers translate an analysis task into a series of data operations to form ananalysis pipeline and visualize them as a datamation. To fill this gap, weintroduce Datamator, an intelligent authoring tool developed to supportdatamation design and generation. It leverages a novel data query decompositionmodel to allow users to generate an initial datamation by simply inputting adata query in natural language. The initial datamation can be refined via richinteractions and a feedback mechanism is utilized to update the decompositionmodel based on user knowledge and preferences. Our system produces an animatedsequence of visualizations driven by a set of low-level data actions. Itsupports unit visualizations, which provide a mapping from each data item to aunique visual mark. We demonstrate the effectiveness of Datamator via a seriesof evaluations including case studies, performance validation, and a controlleduser study.

Datamator: An Intelligent Authoring Tool for Creating Datamations via Data Query Decomposition