Session:「Exploration through modelling and visualisation」

Beagle: Automated Extraction and Interpretation of Visualizations from the Web

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3174168

論文アブストラクト: "How common is interactive visualization on the web?" "What is the most popular visualization design?" "How prevalent are pie charts really?" These questions intimate the role of interactive visualization in the real (online) world. In this paper, we present our approach (and findings) to answering these questions. First, we introduce Beagle, which mines the web for SVG-based visualizations and automatically classifies them by type (i.e., bar, pie, etc.). With Beagle, we extract over 41,000 visualizations across five different tools and repositories, and classify them with 85% accuracy, across 24 visualization types. Given this visualization collection, we study usage across tools. We find that most visualizations fall under four types: bar charts, line charts, scatter charts, and geographic maps. Though controversial, pie charts are relatively rare for the visualization tools that were studied. Our findings also suggest that the total visualization types supported by a given tool could factor into its ease of use. However this effect appears to be mitigated by providing a variety of diverse expert visualization examples to users.

日本語のまとめ:

『最も一般的なグラフデザインは何か』などを評価するため,Webからグラフ画像を収集し,グラフタイプ(棒,円など)を自動的に分類する(86%の精度)システムを開発.主に棒・折れ線・散布図・地理図の4種が使われていることがわかった.

A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3174209

論文アブストラクト: Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data. We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.

日本語のまとめ:

高次元データを2次元に変換する次元削減は,『削減後の横・縦軸が何を意味しているかわからない』などの問題があるので,ユーザが次元削減の入力(出力)の値を変更し,出力(入力)がどのように変化するか観察できるフレームワークを提案.