Data visualization transforms data into images to aid the understanding of data; therefore, it is an invaluable tool for explaining the significance of data to visually inclined people. Given a (big) dataset, the essential task of visualization is to visualize the data to tell compelling stories by selecting, filtering, and transforming the data, and picking the right visualization type such as bar charts or line charts. Our ultimate goal is to automate this task that currently requires heavy user intervention in the existing visualization systems. An evolutionized system in the field faces the following three main challenges: (1) Visualization verification: to determine whether a visualization for a given dataset is interesting, from the viewpoint of human understanding; (2) Visualization search space: a "boring" dataset may become interesting after an arbitrary combination of operations such as selections, joins, and aggregations, among others; (3) On-time responses: do not deplete the user’s patience. In this paper, we present the DeepEye system to address these challenges. This system solves the first challenge by training a binary classifier to decide whether a particular visualization is good for a given dataset, and by using a supervised learning to rank model to rank the above good visualizations. It also considers popular visualization operations, such as grouping and binning, which can manipulate the data, and this will determine the search space. Our proposed system tackles the third challenge by incorporating database optimization techniques for sharing computations and pruning.
This work was supported by the National Key Basic Research and Development (973) Program of China (No. 2015CB358700) and the National Natural Science Foundation of China (Nos. 61373024, 61632016, 61422205, and 61472198).