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In this work, we present a self-driving data visualization system, called DeepEye, that automatically generates and recommends visualizations based on the idea of visualization by examples. We propose effective visualization recognition techniques to decide which visualizations are meaningful and visualization ranking techniques to rank the good visualizations. Furthermore, a main challenge of automatic visualization system is that the users may be misled by blindly suggesting visualizations without knowing the user's intent. To this end, we extend DeepEye to be easily steerable by allowing the user to use keyword search and providing click-based faceted navigation. Empirical results, using real-life data and use cases, verify the power of our proposed system. [ABSTRACT FROM AUTHOR]

Visualization charts are widely utilized for presenting structured data. Under many circumstances, people want to digitalize the data in the charts collected from various sources (e.g., papers and websites), in oder to further analyze the data or create new charts. However, existing automatic and semi-automatic approaches are not always effective due to the variety of charts. In this paper, we introduce a crowdsourcing approach that leverages human ability to extract data from visualization charts. There are several challenges. The first is how to avoid tedious human interaction with charts and design effective crowdsourcing tasks. Second, it is challenging to evaluate worker’s quality for truth inference, because workers may not only provide inaccurate values but also misalign values to wrong data series. Third, to guarantee quality, one may assign a task to many workers, leading to a high crowdsourcing cost. To address these challenges, we design an effective crowdsourcing task scheme that splits a chart into simple micro-tasks. We introduce a novel worker quality model by considering worker’s accuracy and task difficulty. We also devise effective task assignment and early-termination mechanisms to save the cost. We evaluate our approach on real-world datasets on real crowdsourced platforms, and the results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]