Session:「Understanding Data Visualization」

Understanding Concept Maps: A Closer Look at How People Organise Ideas

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025977

論文アブストラクト: Research into creating visualisations that organise ideas into concise concept maps often focuses on implicit mathematical and statistical theories which are built around algorithmic efficacy or visual complexity. Although there are multiple techniques which attempt to mathematically optimise this multi-dimensional problem, it is still unknown how to create concept maps that are immediately understandable to people. In this paper, we present an in-depth qualitative study observing the behaviour and discussing the strategy used by non-expert participants to create, interact, update and communicate a concept map that represents a collection of research ideas. Our results show non-expert individuals create concept maps differently to visualisation algorithms. We found that our participants prioritised narrative, landmarks, abstraction, clarity, and simplicity. Finally, we derive design recommendations from our results which we hope will inspire future algorithms that automatically create more usable and compelling concept maps better suited to the natural behaviours and needs of users.

日本語のまとめ:

79のアイディアから1つのコンセプトマップを作らせる実験を14人に行った.作成プロセスを4つの主テーマと28の副テーマに分解し,9つの推奨されるデザインパターンを定義した.コンセプトマップの自動生成アルゴリズムへの応用が期待される.

Increasing Users' Confidence in Uncertain Data by Aggregating Data from Multiple Sources

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025998

論文アブストラクト: We often base our decisions on uncertain data - for instance, when consulting the weather forecast before deciding what to wear. Due to their uncertainty, such forecasts can differ by provider. To make an informed decision, many people compare several forecasts, which is a time-consuming and cumbersome task. To facilitate comparison, we identified three aggregation mechanisms for forecasts: manual comparison and two mechanisms of computational aggregation. In a survey, we compared the mechanisms using different representations. We then developed a weather application to evaluate the most promising candidates in a real-world study. Our results show that aggregation increases users' confidence in uncertain data, independent of the type of representation. Further, we find that for daily events, users prefer to use computationally aggregated forecasts. However, for high-stakes events, they prefer manual comparison. We discuss how our findings inform the design of improved interfaces for comparison of uncertain data, including non-weather purposes.

日本語のまとめ:

不確かな情報に関する意思決定のための効率的な複数の情報源の比較方法の検討を天気予報を用いて行った. 3つの情報源を用いた実験から得られた値の範囲を表示する方法とそれぞれのデータを並べて表示する方法が効果的だと分かった。 

Bottom-up vs. Top-down: Trade-offs in Efficiency, Understanding, Freedom and Creativity with InfoVis Tools

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025942

論文アブストラクト: The emergence of tools that support fast-and-easy visualization creation by non-experts has made the benefits of InfoVis widely accessible. Key features of these tools include attribute-level operations, automated mappings, and visualization templates. However, these features shield people from lower-level visualization design steps, such as the specific mapping of data points to visuals. In contrast, recent research promotes constructive visualization where individual data units and visuals are directly manipulated. We present a qualitative study comparing people's visualization processes using two visualization tools: one promoting a top-down approach to visualization construction (Tableau Desktop) and one implementing a bottom-up constructive visualization approach (iVoLVER). Our results show how the two approaches influence: 1) the visualization process, 2) decisions on the visualization design, 3) the feeling of control and authorship, and 4) the willingness to explore alternative designs. We discuss the complex trade-offs between the two approaches and outline considerations for designing better visualization tools.

日本語のまとめ:

ボトムアップとトップダウンの2種類の視覚化ツールを用いてデータセットの視覚化を行わせた。前者は視覚化プロセスの理解を助け成果物へのオーサーシップの感情を促進すること後者は負担が少ないがシステムの影響を受けやすいことが分かった.

What Happened in my Home?: An End-User Development Approach for Smart Home Data Visualization

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025485

論文アブストラクト: Smart home systems change the way we experience the home. While there are established research fields within HCI for visualizing specific use cases of a smart home, studies targeting user demands on visualizations spanning across multiple use cases are rare. Especially, individual data-related demands pose a challenge for usable visualizations. To investigate potentials of an end-user development (EUD) approach for flexibly supporting such demands, we developed a smart home system featuring both pre-defined visualizations and a visualization creation tool. To evaluate our concept, we installed our prototype in 12 households as part of a Living Lab study. Results are based on three interview studies, a design workshop and system log data. We identified eight overarching interests in home data and show how participants used pre-defined visualizations to get an overview and the creation tool to not only address specific use cases but also to answer questions by creating temporary visualizations.

日本語のまとめ:

スマートハウスで収集したデータの視覚化システムDASHを開発した。電力の使用状況や窓の開閉状態など知ることができ、カスタマイズも可能。自分の関心のあるトピックに関する概要を知るためにツールを使うことができることが分かった。