2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)最新文献

Axes are the main components of coordinate systems representations. They play a critical role for the visual analysis of multi-dimensional data. However their representation seems to have always be considered self evident, with oriented lines crossing at an origin, completed with labels such as ticks and names. Such classical representation show limits when it comes 3D visualization and immersive analytic (IA), mainly because orthogonal projection of points on linear axes is hard in a 3d environment, and because the user can move therefore the axes can get out of his field of view. In this paper we propose a task-based definition of axes and coordinate systems representation, as well as a tentative design space for coordinates systems representation in immersion. We also present an exploratory user study we carried out to compare three grid-based representations of coordinate systems for multidimensional data analysis with 3D scatterplots.

坐标轴是坐标系表示的主要组成部分。它们对于多维数据的可视化分析起着至关重要的作用。然而，它们的表现似乎总是被认为是不言自明的，有方向的线在原点相交，用刻度和名字等标签完成。当涉及到3D可视化和沉浸式分析(IA)时，这种经典的表示方式显示出局限性，主要是因为在3D环境中，线性轴上的点的正交投影很难实现，而且因为用户可以移动，因此轴可以脱离他的视野。在本文中，我们提出了一个基于任务的轴和坐标系表示的定义，以及一个浸入式坐标系表示的暂定设计空间。我们还提出了一项探索性的用户研究，我们进行了比较三维散点图和多维数据分析坐标系的三种基于网格的表示。

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引用次数: 10

LTMA: Layered Topic Matching for the Comparative Exploration, Evaluation, and Refinement of Topic Modeling Results LTMA:分层主题匹配，用于主题建模结果的比较探索、评估和改进

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)

Pub Date : 2018-10-01 DOI: 10.1109/BDVA.2018.8534018

Mennatallah El-Assady, F. Sperrle, R. Sevastjanova, M. Sedlmair, D. Keim

We present LTMA, a Layered Topic Matching approach for the unsupervised comparative analysis of topic modeling results. Due to the vast number of available modeling algorithms, an efficient and effective comparison of their results is detrimental to a data- and task-driven selection of a model. LTMA automates this comparative analysis by providing topic matching based on two layers (document-overlap and keyword-similarity), creating a novel topic-match data structure. This data structure builds a basis for model exploration and optimization, thus, allowing for an efficient evaluation of their performance in the context of a given type of text data and task. This is especially important for text types where an annotated gold standard dataset is not readily available and, therefore, quantitative evaluation methods are not applicable. We confirm the usefulness of our technique based on three use cases, namely: (1) the automatic comparative evaluation of topic models, (2) the visual exploration of topic modeling differences, and (3) the optimization of topic modeling results through combining matches.

我们提出了LTMA，一种分层主题匹配方法，用于主题建模结果的无监督比较分析。由于有大量可用的建模算法，对其结果进行高效和有效的比较对于数据和任务驱动的模型选择是有害的。LTMA通过提供基于两层(文档重叠和关键字相似)的主题匹配来自动化这种比较分析，从而创建了一种新颖的主题匹配数据结构。此数据结构为模型探索和优化构建了基础，因此，允许在给定类型的文本数据和任务上下文中有效地评估它们的性能。这对于不容易获得带注释的金标准数据集的文本类型尤其重要，因此不适用定量评估方法。我们通过三个用例验证了我们的技术的有用性，即:(1)主题模型的自动比较评估，(2)主题建模差异的视觉探索，(3)通过组合匹配优化主题建模结果。

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引用次数: 8

Evaluating Navigation Techniques for 3D Graph Visualizations in Virtual Reality 评估虚拟现实中三维图形可视化的导航技术

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)

Pub Date : 2018-10-01 DOI: 10.1109/BDVA.2018.8533895

Adam Drogemuller, Andrew Cunningham, James A. Walsh, Maxime Cordeil, W. Ross, B. Thomas

Research into how virtual reality (VR) can be a beneficial technology for new and emerging large, complex data visualizations for data scientists is ongoing. In this paper, we evaluate three-dimensional VR navigation technique for data visualizations and test their effectiveness with a large graph visualization. We evaluate two prominent navigation techniques employed in VR (Teleportation and One-Handed Flying) against two less common methods (Two-Handed Flying and Worlds In Miniature) and evaluate their performance and effectiveness through a series of tasks. We found Steering Patterns (One-Handed Flying and Two-Handed Flying) to be faster and preferred by participants for completing searching tasks in comparision to Teleportation. Worlds-In-Miniature was the least physically demanding of the navigations, and was preferred by participants for tasks that required an overview of the graph such as triangle counting.

对于数据科学家来说，虚拟现实(VR)如何成为新兴的大型复杂数据可视化的有益技术的研究正在进行中。在本文中，我们评估了三维VR导航技术的数据可视化，并通过大型图形可视化测试了它们的有效性。我们将VR中使用的两种突出的导航技术(隐形传送和单手飞行)与两种不太常见的方法(双手飞行和微型世界)进行比较，并通过一系列任务评估它们的性能和有效性。我们发现操纵模式(单手飞行和双手飞行)在完成搜索任务时比隐形传送更快，更受参与者的青睐。“微缩世界”对导航的体力要求最低，在需要对图形进行概述(如三角形计数)的任务中，参与者更喜欢使用微缩世界。

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引用次数: 36

SocialOcean: Visual Analysis and Characterization of Social Media Bubbles SocialOcean:社交媒体泡沫的视觉分析和特征

2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) : Konstanz, Germany, October 17 -19, 2018. IEEE International Symposium on Big Data Visual and Immersive Analytics (4th : 2018 : Konstanz, Germany)

Pub Date : 2018-10-01 DOI: 10.1109/BDVA.2018.8534023

A. Diehl, Michael Hundt, Johannes Häussler, Daniel Seebacher, Siming Chen, Nida Cilasun, D. Keim, T. Schreck

Social media allows citizens, corporations, and authorities to create, post, and exchange information. The study of its dynamics will enable analysts to understand user activities and social group characteristics such as connectedness, geospatial distribution, and temporal behavior. In this context, social media bubbles can be defined as social groups that exhibit certain biases in social media. These biases strongly depend on the dimensions selected in the analysis, for example, topic affinity, credibility, sentiment, and geographic distribution. In this paper, we present SocialOcean, a visual analytics system that allows for the investigation of social media bubbles. There exists a large body of research in social sciences which identifies important dimensions of social media bubbles (SMBs). While such dimensions have been studied separately, and also some of them in combination, it is still an open question which dimensions play the most important role in defining SMBs. Since the concept of SMBs is fairly recent, there are many unknowns regarding their characterization. We investigate the thematic and spatiotemporal characteristics of SMBs and present a visual analytics system to address questions such as: What are the most important dimensions that characterize SMBs? and How SMBs embody in the presence of specific events that resonate with them? We illustrate our approach using three different real scenarios related to the single event of Boston Marathon Bombing, and political news about Global Warming. We perform an expert evaluation, analyze the experts' feedback, and present the lessons learned.

社交媒体允许公民、企业和当局创建、发布和交换信息。对其动态的研究将使分析人员能够了解用户活动和社会群体特征，如连通性、地理空间分布和时间行为。在这种情况下，社交媒体泡沫可以被定义为在社交媒体上表现出某种偏见的社会群体。这些偏差很大程度上取决于分析中选择的维度，例如，主题亲和力、可信度、情感和地理分布。在本文中，我们介绍了SocialOcean，这是一个可视化分析系统，可以对社交媒体泡沫进行调查。社会科学领域有大量的研究确定了社交媒体泡沫的重要维度。虽然这些方面已经分别研究过，有些方面也结合研究过，但哪些方面在界定中小企业方面发挥了最重要的作用仍然是一个悬而未决的问题。由于smb的概念是最近才出现的，因此关于它们的特征有许多未知的东西。我们研究了中小企业的主题和时空特征，并提出了一个可视化分析系统来解决以下问题:什么是中小企业最重要的特征维度?以及中小企业如何在与他们产生共鸣的特定事件中体现出来?我们用与波士顿马拉松爆炸事件和全球变暖的政治新闻有关的三个不同的真实场景来说明我们的方法。我们进行专家评估，分析专家的反馈，并提出经验教训。

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引用次数: 1