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Integrating annotations into multidimensional visual dashboards 将注释集成到多维可视化仪表板中
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-03-07 DOI: 10.1177/14738716221079591
Sriram Karthik Badam, Senthil K. Chandrasegaran, N. Elmqvist
Multidimensional data is often visualized using coordinated multiple views in an interactive dashboard. However, unlike in infographics where text is often a central part of the presentation, there is currently little knowledge of how to best integrate text and annotations in a visualization dashboard. In this paper, we explore a technique called FacetNotes for presenting these textual annotations on top of any visualization within a dashboard irrespective of the scale of data shown or the design of visual representation itself. FacetNotes does so by grouping and ordering the textual annotations based on properties of (1) the individual data points associated with the annotations, and (2) the target visual representation on which they should be shown. We present this technique along with a set of user interface features and guidelines to apply it to visualization interfaces. We also demonstrate FacetNotes in a custom visual dashboard interface. Finally, results from a user study of FacetNotes show that the technique improves the scope and complexity of insights developed during visual exploration.
多维数据通常使用交互式仪表板中协调的多个视图进行可视化。然而,与信息图形中文本通常是演示的中心部分不同,目前对如何在可视化面板中最好地集成文本和注释知之甚少。在本文中,我们探索了一种名为FacetNotes的技术,用于在仪表板内的任何可视化之上显示这些文本注释,而不管显示的数据规模或视觉表示本身的设计如何。FacetNotes通过基于以下属性对文本注释进行分组和排序来做到这一点:(1)与注释相关联的单个数据点,以及(2)它们应该在其上显示的目标视觉表示。我们介绍了这项技术以及一组将其应用于可视化界面的用户界面特性和指南。我们还在自定义的可视化面板界面中演示FacetNotes。最后,FacetNotes的用户研究结果表明,该技术提高了视觉探索过程中开发的见解的范围和复杂性。
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引用次数: 2
A comparative analysis of matrix reordering algorithms regarding canonical data patterns 基于规范数据模式的矩阵重排序算法的比较分析
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-01-01 DOI: 10.1177/14738716221091487
M. Baroni, C. G. Silva
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引用次数: 0
HFTViz: Visualization for the exploration of high frequency trading data HFTViz:用于探索高频交易数据的可视化
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-12-29 DOI: 10.1177/14738716211064921
Javad Yaali, Vincent Grégoire, Thomas Hurtut
High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.
高频交易(HFT)主要基于高速基础设施,是交易行业的重要组成部分。然而,交易机器会产生大量的交易信息,金融研究人员和交易员很难探索这些信息。金融数据的可视化工具通常侧重于投资组合管理和风险与回报之间关系的分析。除了风险收益关系,还有其他方面吸引着金融研究人员,比如流动性和市场瞬间崩盘。HFT研究人员可以从HFT数据中提取这些方面,因为它显示了市场运动的每一个细节。在本文中,我们介绍了HFTViz,这是一个可视化工具,旨在帮助金融研究人员探索纳斯达克交易所提供的HFT数据集。HFTViz提供了一个全面的仪表盘,旨在促进HFT数据探索。HFTViz包含两个部分。它首先提出了一个特定日期的市场概述。在从概览可视化中选择所需股票进行详细调查后,HFTViz还提供了交易信息、交易量和流动性指标的详细视图。在一个收集了五位领域专家的案例研究中,我们说明了HFTViz的有用性。
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引用次数: 0
Design guidelines for narrative maps in sensemaking tasks 在意义生成任务中叙述地图的设计准则
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-12-22 DOI: 10.1177/14738716221079593
Brian Felipe Keith Norambuena, Tanushree Mitra, Chris North
Narrative sensemaking is a fundamental process to understand sequential information. Narrative maps are a visual representation framework that can aid analysts in their narrative sensemaking process. Narrative maps allow analysts to understand the big picture of a narrative, uncover new relationships between events, and model the connection between storylines. We seek to understand how analysts create and use narrative maps in order to obtain design guidelines for an interactive visualization tool for narrative maps that can aid analysts in narrative sensemaking. We perform two experiments with a data set of news articles. The insights extracted from our studies can be used to design narrative maps, extraction algorithms, and visual analytics tools to support the narrative sensemaking process. The contributions of this paper are three-fold: (1) an analysis of how analysts construct narrative maps; (2) a user evaluation of specific narrative map features; and (3) design guidelines for narrative maps. Our findings suggest ways for designing narrative maps and extraction algorithms, as well as providing insights toward useful interactions. We discuss these insights and design guidelines and reflect on the potential challenges involved. As key highlights, we find that narrative maps should avoid redundant connections that can be inferred by using the transitive property of event connections, reducing the overall complexity of the map. Moreover, narrative maps should use multiple types of cognitive connections between events such as topical and causal connections, as this emulates the strategies that analysts use in the narrative sensemaking process.
叙事意义是理解序列信息的基本过程。叙事地图是一种视觉表现框架,可以帮助分析人员在叙事过程中理解意义。叙事地图使分析人员能够理解叙事的大局,揭示事件之间的新关系,并为故事情节之间的联系建立模型。我们试图了解分析人员如何创建和使用叙事地图,以便获得叙事地图的交互式可视化工具的设计指南,该工具可以帮助分析人员进行叙事意义构建。我们用一组新闻文章的数据进行了两个实验。从我们的研究中提取的见解可用于设计叙事地图,提取算法和视觉分析工具,以支持叙事意义构建过程。本文的贡献有三个方面:(1)分析了分析师如何构建叙事地图;(2)用户对具体叙事地图特征的评价;(3)叙事地图的设计准则。我们的发现为设计叙事地图和提取算法提供了方法,并为有用的交互提供了见解。我们将讨论这些见解和设计指南,并反思所涉及的潜在挑战。作为重点,我们发现叙事地图应该避免通过使用事件连接的传递属性推断出的冗余连接,从而降低地图的整体复杂性。此外,叙事地图应该在事件之间使用多种类型的认知联系,如主题和因果联系,因为这模仿了分析师在叙事意义构建过程中使用的策略。
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引用次数: 3
Virtual-Reality graph visualization based on Fruchterman-Reingold using Unity and SteamVR 基于Unity和SteamVR的Fruchterman-Reingold虚拟现实图形可视化
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-12-15 DOI: 10.1177/14738716211060306
G. Kortemeyer
The paper describes a method for the immersive, dynamic visualization of undirected, weighted graphs. Using the Fruchterman-Reingold method, force-directed graphs are drawn in a Virtual-Reality system. The user can walk through the data, as well as move vertices using controllers, while the network display rearranges in realtime according to Newtonian physics. In addition to the physics behind the employed method, the paper explains the most pertinent computational mechanisms for its implementation, using Unity, SteamVR, and a Virtual-Reality system such as HTC Vive (the source package is made available for download). It was found that the method allows for intuitive exploration of graphs with on the order of 10 2 vertices, and that dynamic extrusion of vertices and realtime readjustment of the network structure allows for developing an intuitive understanding of the relationship of a vertex to the remainder of the network. Based on this observation, possible future developments are suggested.
本文描述了一种无向加权图的沉浸式动态可视化方法。使用Fruchterman-Reingold方法,在虚拟现实系统中绘制了力定向图。用户可以浏览数据,也可以使用控制器移动顶点,而网络显示则根据牛顿物理学实时重新排列。除了所用方法背后的物理原理外,本文还解释了其实现的最相关的计算机制,使用Unity、SteamVR和HTC Vive等虚拟现实系统(源程序包可供下载)。已经发现,该方法允许对具有10个2个顶点数量级的图进行直观的探索,并且顶点的动态挤出和网络结构的实时重新调整允许对顶点与网络其余部分的关系进行直观的理解。基于这一观察,提出了未来可能的发展。
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引用次数: 5
VisRuler: Visual analytics for extracting decision rules from bagged and boosted decision trees VisRuler:用于从袋装和增强决策树中提取决策规则的可视化分析
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-12-01 DOI: 10.1177/14738716221142005
Angelos Chatzimparmpas, R. M. Martins, A. Kerren
Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and health care. Thus, the interpretability of bagging and boosting algorithms—such as random forest and adaptive boosting—reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.
Bagging和boosting是机器学习中两种流行的集成方法,它们产生许多单独的决策树。由于这些方法固有的集成特性,它们的预测性能通常优于单决策树或其他ML模型。然而,每个决策树都会生成许多决策路径,这增加了模型的总体复杂性,并阻碍了它在需要可靠和可解释决策的领域中的使用,如金融、社会护理和医疗保健。因此,装袋和提升算法(如随机森林和自适应提升)的可解释性随着决策数量的增加而降低。在本文中,我们提出了一种视觉分析工具,旨在帮助用户通过全面的视觉检查工作流程从此类ML模型中提取决策,该工作流程包括选择一组稳健且多样化的模型(源自不同的集成学习算法),根据其全局贡献选择重要特征,以及决定哪些决定对于全局解释(或对于特定情况在本地解释)至关重要。结果是基于几个模型的类协议和用户导出的已探索的手动决策的最终决策。我们通过用例、使用场景和用户研究评估了VisRuler的适用性和有效性。评估显示,大多数用户成功地使用我们的系统直观地探索决策规则,以令人满意的方式执行所提出的任务并回答给定的问题。
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引用次数: 3
Community Fabric: Visualizing communities and structure in dynamic networks 社区结构:动态网络中可视化社区和结构
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-10-29 DOI: 10.1177/14738716211056036
Evan Ezell, Seung-Hwan Lim, D. Anderson, R. Stewart
We present Community Fabric, a novel visualization technique for simultaneously visualizing communities and structure within dynamic networks. In dynamic networks, the structure of the network is continuously evolving throughout time and these underlying topological shifts tend to lead to communal changes. Community Fabric helps the viewer more easily interpret and understand the interplay of structural change and community evolution in dynamic graphs. To achieve this, we take a new approach, hybridizing two popular network and community visualizations. Community Fabric combines the likes of the Biofabric static network visualization method with traditional community alluvial flow diagrams to visualize communities in a dynamic network while also displaying the underlying network structure. Our approach improves upon existing state-of-the-art techniques in several key areas. We describe the methodologies of Community Fabric, implement the visualization using modern web-based tools, and apply our approach to three example data sets.
我们提出了CommunityFabric,这是一种新的可视化技术,用于同时可视化动态网络中的社区和结构。在动态网络中,网络的结构在整个时间内不断演变,这些潜在的拓扑变化往往会导致公共变化。CommunityFabric有助于观察者更容易地理解和理解动态图中结构变化和社区进化的相互作用。为了实现这一点,我们采取了一种新的方法,将两种流行的网络和社区可视化相结合。Community Fabric将Biofabric静态网络可视化方法与传统的社区冲积流图相结合,在动态网络中可视化社区,同时显示底层网络结构。我们的方法在几个关键领域改进了现有的最先进技术。我们描述了CommunityFabric的方法,使用现代基于web的工具实现可视化,并将我们的方法应用于三个示例数据集。
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引用次数: 0
Topology-aware space distortion for structured visualization spaces 结构化可视化空间的拓扑感知空间畸变
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-10-25 DOI: 10.1177/14738716211053579
Weihang Wang, Sriram Karthik Badam, N. Elmqvist
We propose topology-aware space distortion (TASD), a family of interactive layout techniques for non-linearly distorting geometric space based on user attention and on the structure of the visual representation. TASD seamlessly adapts the visual substrate of any visualization to give more screen real estate to important regions of the representation at the expense of less important regions. In this paper, we present a concrete TASD technique that we call ZoomHalo for interactively distorting a two-dimensional space based on a degree-of-interest (DOI) function defined for the space. Using this DOI function, ZoomHalo derives several areas of interest, computes the available space around each area in relation to other areas and the current viewport extents, and then dynamically expands (or shrinks) each area given user input. We use our prototype to evaluate the technique in two user studies, as well as showcase examples of TASD for node-link diagrams, word clouds, and geographical maps.
我们提出了拓扑感知空间失真(TAD),这是一系列基于用户注意力和视觉表示结构的非线性失真几何空间的交互式布局技术。TAD无缝地适应任何可视化的视觉基底,以牺牲不太重要的区域为代价,为表示的重要区域提供更多的屏幕真实感。在本文中,我们提出了一种具体的TAD技术,我们称之为ZoomHalo,用于基于为二维空间定义的兴趣度(DOI)函数交互式扭曲二维空间。使用这个DOI函数,ZoomHalo导出几个感兴趣的区域,计算每个区域周围相对于其他区域和当前视口范围的可用空间,然后在给定用户输入的情况下动态扩展(或缩小)每个区域。我们使用我们的原型在两项用户研究中评估了该技术,并展示了节点链接图、单词云和地理地图的TAD示例。
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引用次数: 1
PeaGlyph: Glyph design for investigation of balanced data structures PeaGlyph:用于研究平衡数据结构的Glyph设计
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-10-10 DOI: 10.1177/14738716211050602
Kenan Koc, A. McGough, Sara Johansson Fernstad
For many data analysis tasks, such as the formation of well-balanced groups for a fair race or collaboration in learning settings, the balancing between data attributes is at least as important as the actual values of items. At the same time, comparison of values is implicitly desired for these tasks. Even with statistical methods available to measure the level of balance, human judgment, and domain expertise plays an important role in judging the level of balance, and whether the level of unbalance is acceptable or not. Accordingly, there is a need for techniques that improve decision-making in the context of group formation that can be used as a visual complement to statistical analysis. This paper introduces a novel glyph-based visualization, PeaGlyph, which aims to support the understanding of balanced and unbalanced data structures, for instance by using a frequency format through countable marks and salient shape characteristics. The glyph was designed particularly for tasks of relevance for investigation of properties of balanced and unbalanced groups, such as looking-up and comparing values. Glyph-based visualization methods provide flexible and useful abstractions for exploring and analyzing multivariate data sets. The PeaGlyph design was based on an initial study that compared four glyph visualization methods in a joint study, including two base glyphs and their variations. The performance of the novel PeaGlyph was then compared to the best “performers” of the first study through evaluation. The initial results from the study are encouraging, and the proposed design may be a good alternative to the traditional glyphs for depicting multivariate data and allowing viewers to form an intuitive impression as to how balanced or unbalanced a set of objects are. Furthermore, a set of design considerations is discussed in context of the design of the glyphs.
对于许多数据分析任务,例如为公平竞争或学习环境中的合作组建平衡良好的小组,数据属性之间的平衡至少与项目的实际价值一样重要。同时,这些任务隐含地需要对值进行比较。即使有可用的统计方法来衡量平衡水平,人类的判断和领域专业知识在判断平衡水平以及不平衡水平是否可接受方面也发挥着重要作用。因此,需要在群体形成的背景下改进决策的技术,这些技术可以用作统计分析的视觉补充。本文介绍了一种新的基于字形的可视化,PeaGlyph,旨在支持对平衡和不平衡数据结构的理解,例如通过使用可计数标记和显著形状特征的频率格式。字形是专门为研究平衡和不平衡组的特性的相关任务设计的,例如查找和比较值。基于Glyph的可视化方法为探索和分析多变量数据集提供了灵活而有用的抽象。PeaGlyph的设计基于一项初步研究,该研究在一项联合研究中比较了四种字形可视化方法,包括两种基本字形及其变体。通过评估,将小说《豌豆雕文》的表现与第一项研究中表现最好的“演员”进行了比较。该研究的初步结果令人鼓舞,所提出的设计可能是传统字形的一个很好的替代方案,用于描绘多元数据,并允许观众对一组对象的平衡或不平衡程度形成直观印象。此外,在字形设计的背景下讨论了一组设计注意事项。
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引用次数: 3
MING: An interpretative support method for visual exploration of multidimensional data MING:一种用于多维数据可视化探索的解释性支持方法
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2021-10-01 DOI: 10.1177/14738716221079589
Benoît Colange, L. Vuillon, S. Lespinats, D. Dutykh
Dimensionality reduction enables analysts to perform visual exploration of multidimensional data with a low-dimensional map retaining as much as possible of the original data structure. The interpretation of such a map relies on the hypothesis of preservation of neighborhood relations. Namely, distances in the map are assumed to reflect faithfully dissimilarities in the data space, as measured with a domain-related metric. Yet, in most cases, this hypothesis is undermined by distortions of those relations by the mapping process, which need to be accounted for during map interpretation. In this paper, we describe an interpretative support method called Map Interpretation using Neighborhood Graphs (MING) displaying individual neighborhood relations on the map, as edges of nearest neighbors graphs. The level of distortion of those relations is shown through coloring of the edges. This allows analysts to assess the reliability of similarity relations inferred from the map, while hinting at the original structure of data by showing the missing relations. Moreover, MING provides a local interpretation for classical map quality indicators, since the quantitative measure of distortions is based on those indicators. Overall, the proposed method alleviates the mapping-induced bias in interpretation while constantly reminding users that the map is not the data.
降维使分析人员能够使用尽可能多地保留原始数据结构的低维地图对多维数据进行可视化探索。这种地图的解释依赖于邻里关系保持的假设。也就是说,假设地图中的距离忠实地反映了数据空间中的差异,用与域相关的度量来测量。然而,在大多数情况下,由于制图过程对这些关系的扭曲,这一假设被破坏了,这需要在地图解释期间加以考虑。本文描述了一种使用邻域图(MING)在地图上显示单个邻域关系作为最近邻图的边的解释支持方法。这些关系的扭曲程度通过边缘的着色来显示。这使得分析人员可以评估从地图中推断出的相似关系的可靠性,同时通过显示缺失的关系来暗示数据的原始结构。此外,MING提供了经典地图质量指标的局部解释,因为扭曲的定量测量是基于这些指标。总的来说,本文提出的方法在不断提醒用户地图不是数据的同时,减轻了地图在解释中引起的偏见。
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引用次数: 3
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