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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]

Supporting the translation from natural language (NL) query to visualization (NL2VIS) can simplify the creation of data visualizations because if successful, anyone can generate visualizations by their natural language from the tabular data. The state-of-the-art NL2VIS approaches (e.g., NL4DV and FlowSense) are based on semantic parsers and heuristic algorithms, which are not end-to-end and are not designed for supporting (possibly) complex data transformations. Deep neural network powered neural machine translation models have made great strides in many machine translation tasks, which suggests that they might be viable for NL2VIS as well. In this paper, we present ncNet, a Transformer-based sequence-to-sequence model for supporting NL2VIS, with several novel visualization-aware optimizations, including using attention-forcing to optimize the learning process, and visualization-aware rendering to produce better visualization results. To enhance the capability of machine to comprehend natural language queries, ncNet is also designed to take an optional chart template (e.g., a pie chart or a scatter plot) as an additional input, where the chart template will be served as a constraint to limit what could be visualized. We conducted both quantitative evaluation and user study, showing that ncNet achieves good accuracy in the nvBench benchmark and is easy-to-use. [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]

Data visualization is crucial in today's data-driven business world, which has been widely used for helping decision making that is closely related to major revenues of many industrial companies. However, due to the high demand of data processing w.r.t. the volume, velocity, and veracity of data, there is an emerging need for database experts to help for efficient and effective data visualization. In response to this demand, this article surveys techniques that make data visualization more efficient and effective. (1) Visualization specifications define how the users can specify their requirements for generating visualizations. (2) Efficient approaches for data visualization process the data and a given visualization specification, which then produce visualizations with the primary target to be efficient and scalable at an interactive speed. (3) Data visualization recommendation is to auto-complete an incomplete specification, or to discover more interesting visualizations based on a reference visualization. [ABSTRACT FROM AUTHOR]