WonderFlow: Narration-Centric Design of Animated Data Videos

Creating an animated data video enriched with audio narration takes a significant amount of time and effort and requires expertise. Users not only need to design complex animations, but also turn written text scripts into audio narrations and synchronize visual changes with the narrations. This paper presents WonderFlow, an interactive authoring tool, that facilitates narration-centric design of animated data videos. WonderFlow allows authors to easily specify a semantic link between text and the corresponding chart elements. Then it automatically generates audio narration by leveraging text-to-speech techniques and aligns the narration with an animation. WonderFlow provides a visualization structure-aware animation library designed to ease chart animation creation, enabling authors to apply pre-designed animation effects to common visualization components. It also allows authors to preview and iteratively refine their data videos in a unified system, without having to switch between different creation tools. To evaluate WonderFlow's effectiveness and usability, we created an example gallery and conducted a user study and expert interviews. The results demonstrated that WonderFlow is easy to use and simplifies the creation of data videos with narration-animation interplay

Exploring interactions with physically dynamic bar charts

Visualizations such as bar charts help users reason about data, but are mostly screen-based, rarely physical, and almost never physical and dynamic. This paper investigates the role of physically dynamic bar charts and evaluates new interactions for exploring and working with datasets rendered in dynamic physical form. To facilitate our exploration we constructed a 10x10 interactive bar chart and designed interactions that supported fundamental visualisation tasks, specifically; annotation, filtering, organization, and navigation. The interactions were evaluated in a user study with 17 participants. Our findings identify the preferred methods of working with the data for each task i.e. directly tapping rows to hide bars, highlight the strengths and limitations of working with physical data, and discuss the challenges of integrating the proposed interactions together into a larger data exploration system. In general, physical interactions were intuitive, informative, and enjoyable, paving the way for new explorations in physical data visualizations

Grammar-Based Interactive Genome Visualization

Visualization is an indispensable method in the exploration of genomic data. However, the current state of the art in genome browsers – a class of interactive visualization tools – limit the exploration by coupling the visual representations with specific file formats. Because the tools do not support the exploration of the visualization design space, they are difficult to adapt to atypical data. Moreover, although the tools provide interactivity, the implementations are often rudimentary, encumbering the exploration of the data. This thesis introduces GenomeSpy, an interactive genome visualization tool that improves upon the current state of the art by providing better support for exploration. The tool uses a visualization grammar that allows for implementing novel visualization designs, which can display the underlying data more effectively. Moreover, the tool implements GPU-accelerated interactions that better support navigation in the genomic space. For instance, smoothly animated transitions between loci or sample sets improve the perception of causality and help the users stay in the flow of exploration. The expressivity of the visualization grammar and the benefit of fluid interactions are validated with two case studies. The case studies demonstrate visualization of high-grade serous ovarian cancer data at different analysis phases. First, GenomeSpy is being used to create a tool for scrutinizing raw copy-number variation data along with segmentation results. Second, the segmentations along with point mutations are used in a GenomeSpy-based multi-sample visualization that allows for exploring and comparing both multiple data dimensions and samples at the same time. Although the focus has been on cancer research, the tool could be applied to other domains as well

VizRank: Data Visualization Guided by Machine Learning

Data visualization plays a crucial role in identifying interesting patterns in exploratory data analysis. Its use is, however, made difficult by the large number of possible data projections showing different attribute subsets that must be evaluated by the data analyst. In this paper, we introduce a method called VizRank, which is applied on classified data to automatically select the most useful data projections. VizRank can be used with any visualization method that maps attribute values to points in a two-dimensional visualization space. It assesses possible data projections and ranks them by their ability to visually discriminate between classes. The quality of class separation is estimated by computing the predictive accuracy of k-nearest neighbor classifier on the data set consisting of x and y positions of the projected data points and their class information. The paper introduces the method and presents experimental results which show that VizRank's ranking of projections highly agrees with subjective rankings by data analysts. The practical use of VizRank is also demonstrated by an application in the field of functional genomics