論文アブストラクト： The widespread use of text-based search in user interfaces has led designers in visualization to occasionally add search functionality to their creations. Yet it remains unclear how search may impact a person's behavior. Given the unstructured context of the web, users may not have explicit information-seeking goals and designers cannot make assumptions about user attention. To bridge this gap, we observed the impact of integrating search with five visualizations across 830 online participants. In an unguided task, we find that (1) the presence of text-based search influences people's information-seeking goals, (2) search can alter the data that people explore and how they engage with it, and (3) the effects of search are amplified in visualizations where people are familiar with the underlying dataset. These results suggest that text-search in web visualizations drives users towards more diverse information seeking goals, and may be valuable in a range of existing visualization designs.
Web上のデータのインタラクティブなグラフィック表示に, テキスト検索機能を追加した場合, ユーザのデータ探索にどう影響するか調査. テキスト検索を設けた場合, より幅広いデータにアクセスをしていた.
論文アブストラクト： Understanding team communication and collaboration patterns is critical for improving work efficiency in organizations. This paper presents an interactive visualization system, T-Cal, that supports the analysis of conversation data from modern team messaging platforms (e.g., Slack). T-Cal employs a user-familiar visual interface, a calendar, to enable seamless multi-scale browsing of data from different perspectives. T-Cal also incorporates a number of analytical techniques for disentangling interleaving conversations, extracting keywords, and estimating sentiment. The design of T-Cal is based on an iterative user-centered design process including interview studies, requirements gathering, initial prototypes demonstration, and evaluation with domain users. The resulting two case studies indicate the effectiveness and usefulness of T-Cal in real-world applications, including daily conversations within an industry research lab and student group chats in a MOOC.
論文アブストラクト： The number of sensors in our surroundings that provide the same information steadily increases. Since sensing is prone to errors, sensors may disagree. For example, a GPS-based tracker on the phone and a sensor on the bike wheel may provide discrepant estimates on traveled distance. This poses a user dilemma, namely how to reconcile the conflicting information into one estimate. We investigated whether visualizing the uncertainty associated with sensor measurements improves the quality of users' inference. We tested four visualizations with increasingly detailed representation of uncertainty. Our study repeatedly presented two sensor measurements with varying degrees of inconsistency to participants who indicated their best guess of the "true" value. We found that uncertainty information improves users' estimates, especially if sensors differ largely in their associated variability. Improvements were larger for information-rich visualizations. Based on our findings, we provide an interactive tool to select the optimal visualization for displaying conflicting information.
不確実性があるデータにおける可視化手法の検討. 結果, 不確実の度合で最適手法が異なり, 不確実な場合ドットプロットが有効. https://github.com/hcilab-org/InformationAggregation
論文アブストラクト： Programmers must draw explicit connections between their code and runtime state to properly assess the correctness of their programs. However, debugging tools often decouple the program state from the source code and require explicitly invoked views to bridge the rift between program editing and program understanding. To unobtrusively reveal runtime behavior during both normal execution and debugging, we contribute techniques for visualizing program variables directly within the source code. We describe a design space and placement criteria for embedded visualizations. We evaluate our in situ visualizations in an editor for the Vega visualization grammar. Compared to a baseline development environment, novice Vega users improve their overall task grade by about 2 points when using the in situ visualizations and exhibit significant positive effects on their self-reported speed and accuracy.