VIStory: interactive storyboard for exploring visual information in scientific publications

Thirty-Two Years of IEEE VIS: Authors, Fields of Study and Citations

The IEEE VIS Conference (VIS) recently rebranded itself as a unified conference and officially positioned itself within the discipline of Data Science. Driven by this movement, we investigated (1) who contributed to VIS, and (2) where VIS stands in the scientific world. We examined the authors and fields of study of 3,240 VIS publications in the past 32 years based on data collected from OpenAlex and IEEE Xplore, among other sources. We also examined the citation flows from referenced papers (i.e., those referenced in VIS) to VIS, and from VIS to citing papers (i.e., those citing VIS). We found that VIS has been becoming increasingly popular and collaborative. The number of publications, of unique authors, and of participating countries have been steadily growing. Both cross-country collaborations, and collaborations between educational and non-educational affiliations, namely "cross-type collaborations", are increasing. The dominance of the US is decreasing, and authors from China are now an important part of VIS. In terms of author affiliation types, VIS is increasingly dominated by authors from universities. We found that the topics, inspirations, and influences of VIS research is limited such that (1) VIS, and their referenced and citing papers largely fall into the Computer Science domain, and (2) citations flow mostly between the same set of subfields within Computer Science. Our citation analyses showed that award-winning VIS papers had higher citations. Interactive visualizations, replication data, source code and supplementary material are available at https://32vis.hongtaoh.com and https://osf.io/zkvjm.

Deep Colormap Extraction From Visualizations

This article presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of  ∼ 64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous, and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases, i.e., color transfer and color remapping.

Interactive visual facets to support fluid exploratory search

Abstract

Exploratory search starts with ill-defined goals and involves browsing, learning, and formulating new targets for search. To fluidly support such dynamic search behaviours, we focus on devising interactive visual facets (IVF), visualising information facets to support user comprehension and control of the information space. We reviewed existing faceted search interfaces and derived two design requirements (DR) that have not been fully addressed to support fluid interactions in exploratory search. We then exemplified the requirements through devising an IVF tool, which coordinates a linear and a categorical facet representing the distribution and summarisation of items, respectively, and providing context for faceted exploration (DR1). To support rapid transitions between search criteria (DR2), the tool introduces a novel design concept of using facets to select items without filtering the item space. Particularly, we propose a filter-swipe technique that enables users to drag a categorical facet value sequentially over linear facet bars to view the items in the intersection of the two facets along with the categorical facet dynamically summarising the items in the intersection. Three applications with various datasets demonstrate how the features support information discovery with ease. A comparison with a baseline system provided evidence that the task performance of the IVF tool was comparable to the typical query search interface. Another study of 11 participants with realistic email search tasks shows that dynamic suggestions through the timeline navigation can help discover useful suggestions for search; the novel design concept was favoured over using facet values as filters. Based on these practices, we derive IVF design implications for fluid, exploratory searches.

Graphical abstract

VIS30K: A Collection of Figures and Tables From IEEE Visualization Conference Publications

We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years.

An Approach for Chart Description Generation in Cyber–Physical–Social System

There is an increasing use of charts generated by the social interaction environment in manufacturing enterprise applications. To transform these massive amounts of unstructured chart data into decision support knowledge for demand-capability matching in manufacturing enterprises, we propose a manufacturing enterprise chart description generation (MECDG) method, which is a two-phase automated solution: (1) extracting chart data based on optical character recognition and deep learning method; (2) generating chart description according to user input based on natural language generation method and matching the description with extracted chart data. We verified and compared the processing at each phase of the method, and at the same time applied the method to the interactive platform of the manufacturing enterprise. The ultimate goal of this paper is to promote the knowledge extraction and scientific analysis of chart data in the context of manufacturing enterprises, so as to improve the analysis and decision-making capabilities of enterprises.