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Multidimensional data visualization and synchronization for revealing hidden pandemic information 多维数据可视化与同步,揭示隐藏的流行病信息
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-09-18 DOI: 10.1177/14738716241277559
Qi Zhang, Nikhil Maram
Visualization is integral to uncovering hidden information in data and providing users with intuitive feedback for decision-making. Data visualization is crucial for transforming complex data into actionable insights across various domains. In recent years, coronavirus disease vaccines have become increasingly available to much of the population. However, the CDC (Centers for Disease Control and Prevention) often fails to consider multidimensional coronavirus pandemic data from a side-by-side perspective, limiting the ability of medical professionals and individuals to compare and interact with comprehensive data visualizations. Effectively displaying coronavirus and vaccination data collected from multiple sources is essential for interpreting pandemic transmission patterns and vaccine efficiency. This paper presents a new platform for innovative data visualizations that offers users intuitive feedback and a complete data story. We designed algorithms to seamlessly combine multiple parameters, synchronize attributes, and dynamically visualize data over time on a single webpage. Instead of integrating all attributes into a single plot, which can be overwhelming due to space limitations and make it difficult to extract crucial information from overcrowded display components, we developed algorithms to classify, enhance, and group all parameters based on their relationships and similarities. Furthermore, a side-by-side visualization method was created to dynamically link all parameters in multiple images for data exploration, trend comparison, hidden information detection, and correspondence analysis. Our platform provides real-time performance, enabling healthcare professionals to make informed decisions, communicate findings effectively, and uncover patterns that might not be apparent in raw data. The proposed multidimensional data visualization algorithms have broad applications in general data exploration and revealing hidden information.
要揭示数据中隐藏的信息,并为用户提供直观的决策反馈,可视化是不可或缺的。数据可视化对于将复杂的数据转化为各领域可操作的见解至关重要。近年来,冠状病毒疾病疫苗越来越多地为大多数人所使用。然而,美国疾病控制和预防中心(CDC)往往不能从侧面考虑冠状病毒大流行的多维数据,从而限制了医疗专业人员和个人与综合数据可视化进行比较和互动的能力。有效显示从多个来源收集的冠状病毒和疫苗接种数据对于解读流行病传播模式和疫苗接种效率至关重要。本文介绍了一个用于创新数据可视化的新平台,可为用户提供直观的反馈和完整的数据故事。我们设计的算法可在单个网页上无缝组合多个参数、同步属性并动态可视化随时间变化的数据。将所有属性整合到一张图上可能会因空间限制而令人难以承受,并且难以从过于拥挤的显示组件中提取关键信息,而我们开发的算法可以根据所有参数之间的关系和相似性对其进行分类、增强和分组。此外,我们还创建了一种并排可视化方法,可动态连接多个图像中的所有参数,以便进行数据探索、趋势比较、隐藏信息检测和对应分析。我们的平台可提供实时性能,使医疗保健专业人员能够做出明智的决策,有效地交流研究结果,并发现原始数据中可能不明显的模式。所提出的多维数据可视化算法在一般数据探索和揭示隐藏信息方面有着广泛的应用。
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引用次数: 0
Interactive visual formula composition of multidimensional data classifiers 多维数据分类器的交互式可视化公式组合
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-09-14 DOI: 10.1177/14738716241270288
Adrian Derstroff, Simon Leistikow, Ali Nahardani, Katja Gruen, Marcus Franz, Verena Hoerr, Lars Linsen
Understanding how a classification result is generated and what role individual features play in the classification is crucial in many applications and, in particular, in medical contexts such as the translation of diagnosis biomarkers into clinical practice. The goal is to find (ideally simple) relationships between the features in multi-dimensional data and the classification for an explanation of the underlying phenomenon. Mathematical formulas allow for the expression of these relationships and can serve as classifiers. However, there are infinitely many mathematical formulas for the given features and they bear an inherent trade-off between complexity and accuracy. We present an interactive visual approach that supports domain experts to mitigate the trade-off issue. Core to our approach is a novel feature selection method, from which formulas are composed using symbolic regression and where state-of-the-art classifiers serve as a reference. To evaluate our approach and compare the achieved classification performance to the performance achieved by other state-of-the-art feature selection techniques, we test our methods with well-known machine learning data sets. Our evaluation shows that our feature selection method performs better than randomly selecting features for data sets with many features or when a low number of generations in the symbolic regression is required. Moreover, it consistently matches or outperforms state-of-the-art methods. Moreover, we apply our approach in a case study to a hemodynamic cohort data set, where we report our findings and domain expert feedback. Our approach was able to find formulas containing features that are in agreement with literature. Also, we could find formulas that performed better in the micro-averaged F1 score when compared to established histological indices.
了解分类结果是如何产生的,以及各个特征在分类过程中发挥了什么作用,这在许多应用中都至关重要,尤其是在医疗领域,例如将诊断生物标记物转化为临床实践。我们的目标是找到多维数据中的特征与分类之间的(理想情况下是简单的)关系,以解释潜在的现象。数学公式可以表达这些关系,并可作为分类器。然而,给定特征的数学公式无穷无尽,它们在复杂性和准确性之间存在固有的权衡。我们提出了一种支持领域专家的交互式可视化方法,以缓解权衡问题。我们的方法的核心是一种新颖的特征选择方法,使用符号回归法组成公式,并以最先进的分类器作为参考。为了评估我们的方法,并将其分类性能与其他最先进的特征选择技术进行比较,我们用著名的机器学习数据集测试了我们的方法。评估结果表明,对于特征较多的数据集或需要较少代数的符号回归时,我们的特征选择方法比随机选择特征的方法性能更好。而且,它的性能始终与最先进的方法相匹配或更胜一筹。此外,我们还在血液动力学队列数据集的案例研究中应用了我们的方法,并报告了我们的发现和领域专家的反馈意见。我们的方法能够找到包含与文献一致的特征的公式。此外,与已有的组织学指数相比,我们还能找到在微观平均 F1 分数上表现更好的公式。
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引用次数: 0
Exploring annotation taxonomy in grouped bar charts: A qualitative classroom study 探索分组条形图中的注释分类法:课堂定性研究
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-09-07 DOI: 10.1177/14738716241270247
Md Dilshadur Rahman, Ghulam Jilani Quadri, Danielle Albers Szafir, Paul Rosen
Annotations are an essential part of data analysis and communication in visualizations, which focus a readers attention on critical visual elements (e.g. an arrow that emphasizes a downward trend in a bar chart). Annotations enhance comprehension, mental organization, memorability, user engagement, and interaction and are crucial for data externalization and exploration, collaborative data analysis, and narrative storytelling in visualizations. However, we have identified a general lack of understanding of how people annotate visualizations to support effective communication. In this study, we evaluate how visualization students annotate grouped bar charts when answering high-level questions about the data. The resulting annotations were qualitatively coded to generate a taxonomy of how they leverage different visual elements to communicate critical information. We found that the annotations used significantly varied by the task they were supporting and that whereas several annotation types supported many tasks, others were usable only in special cases. We also found that some tasks were so challenging that ensembles of annotations were necessary to support the tasks sufficiently. The resulting taxonomy of approaches provides a foundation for understanding the usage of annotations in broader contexts to help visualizations achieve their desired message.
注释是可视化数据分析和交流的重要组成部分,可将读者的注意力集中在关键的可视化元素上(例如,在柱状图中强调下降趋势的箭头)。注释可以增强理解力、心理组织能力、记忆力、用户参与度和互动性,对于数据外部化和探索、协作数据分析以及可视化叙事至关重要。然而,我们发现人们对如何为可视化添加注释以支持有效交流普遍缺乏了解。在本研究中,我们评估了可视化专业学生在回答有关数据的高层次问题时如何为分组条形图添加注释。我们对由此产生的注释进行了定性编码,以便对他们如何利用不同的可视化元素来传达关键信息进行分类。我们发现,所使用的注释因所支持的任务而有很大不同,有几种注释类型支持许多任务,而另一些注释类型仅在特殊情况下可用。我们还发现,有些任务极具挑战性,必须使用多种注释才能充分支持这些任务。由此产生的方法分类法为我们了解在更广泛的背景下如何使用注释来帮助可视化实现预期信息奠定了基础。
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引用次数: 0
Designing complex network visualisations using the wayfinding map metaphor 利用寻路地图隐喻设计复杂的网络可视化图示
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-08-26 DOI: 10.1177/14738716241270341
Nicola Cerioli, Rupesh Vyas, Mary Pat Reeve, Masood Masoodian
Despite their widespread use, network visualisations can be rather challenging to design and use. This is due to the fact that such visualisations are generally used to represent highly complex underlying data sets. As such, the resulting charts often include a very large number of visual elements and many non-linear relations between them that must be displayed. More effective design-oriented approaches are therefore needed to better support designers in creating network visualisations for complex data sets that are more understandable and usable for their users. The use of visual metaphors seems to offer such an approach to designing visualisations of complex data. In this article, we propose the use of wayfinding map metaphor in network diagrams to support both the designers and users of this type of data visualisation. We also provide a mapping of the three common map wayfinding tasks – orientation, exploration, and navigation – to three categories of network diagram user interactions. To demonstrate the potential of our proposed approach, we provide an example case study using a prototype network diagram visualisation tool – Colocalisation Network Explorer – which we have developed to support the exploration of relationships between various diseases and the portion of the human genome involved in their onset.
尽管网络可视化应用广泛,但设计和使用网络可视化却相当具有挑战性。这是因为此类可视化通常用于表示高度复杂的基础数据集。因此,生成的图表通常包含大量的可视化元素和它们之间必须显示的许多非线性关系。因此,我们需要更有效的面向设计的方法,以更好地支持设计人员为复杂的数据集创建网络可视化图表,使用户更容易理解和使用。视觉隐喻的使用似乎为复杂数据的可视化设计提供了这样一种方法。在本文中,我们建议在网络图中使用寻路地图隐喻,为这类数据可视化的设计者和用户提供支持。我们还将三种常见的地图寻路任务--定向、探索和导航--映射为三类网络图用户交互。为了展示我们提出的方法的潜力,我们提供了一个使用原型网络图可视化工具--"共定位网络资源管理器"--进行案例研究的示例。
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引用次数: 0
Graph & Network Visualization and Beyond 图表和网络可视化及其他
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-08-17 DOI: 10.1177/14738716241270224
Quang Vinh Nguyen, Mabule Samuel Mabakane, Adrian Rusu, Ebad Banissi
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引用次数: 0
A framework to improve causal inferences from visualizations using counterfactual operators 利用反事实运算符改进可视化因果推断的框架
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-08-06 DOI: 10.1177/14738716241265120
Arran Zeyu Wang, David Borland, David Gotz
Exploratory data analysis of high-dimensional datasets is a crucial task for which visual analytics can be especially useful. However, the ad hoc nature of exploratory analysis can also lead users to draw incorrect causal inferences. Previous studies have demonstrated this risk and shown that integrating counterfactual concepts within visual analytics systems can improve users’ understanding of visualized data. However, effectively leveraging counterfactual concepts can be challenging, with only bespoke implementations found in prior work. Moreover, it can require expertise in both counterfactual subset analysis and visualization to implement the functionalities practically. This paper aims to help address these challenges in two ways. First, we propose an operator-based conceptual model for the use of counterfactuals that is informed by prior work in visualization research. Second, we contribute the Co-op library, an open and extensible reference implementation of this model that can support the integration of counterfactual-based subset computation with visualization systems. To evaluate the effectiveness and generalizability of Co-op, the library was used to construct two different visual analytics systems each supporting a distinct user workflow. In addition, expert interviews were conducted with professional visual analytics researchers and engineers to gain more insights regarding how Co-op could be leveraged. Finally, informed in part by these evaluation results, we distil a set of key design implications for effectively leveraging counterfactuals in future visualization systems.
对高维数据集进行探索性数据分析是一项至关重要的任务,可视化分析在这方面尤其有用。然而,探索性分析的临时性也可能导致用户得出错误的因果推论。以往的研究已经证明了这种风险,并表明在可视化分析系统中整合反事实概念可以提高用户对可视化数据的理解。然而,有效利用反事实概念可能具有挑战性,在之前的工作中只发现了定制的实现方法。此外,还需要反事实子集分析和可视化方面的专业知识,才能切实实现这些功能。本文旨在通过两种方式帮助应对这些挑战。首先,我们为反事实的使用提出了一个基于运算符的概念模型,该模型参考了可视化研究方面的前期工作。其次,我们贡献了 Co-op 库,它是该模型的一个开放且可扩展的参考实现,可支持基于反事实的子集计算与可视化系统的集成。为了评估 Co-op 的有效性和可推广性,我们使用该库构建了两个不同的可视化分析系统,每个系统都支持不同的用户工作流程。此外,还对专业的可视化分析研究人员和工程师进行了专家访谈,以获得更多关于如何利用 Co-op 的见解。最后,根据这些评估结果,我们提炼出一套关键的设计理念,以便在未来的可视化系统中有效利用反事实。
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引用次数: 0
Two-layer visual analytics of truckers’ risk-coping social network 卡车司机风险应对社交网络的双层可视化分析
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-08-03 DOI: 10.1177/14738716241265110
Qi Huang, Mao Lin Huang, Yi-Na Li
Within organizations, managers’ specific responsibilities and domain expertise shape their interests in the output of social network analysis. Our proposed visualization approach is tailored to meet the operation-directed needs and preferences for visual analysis of specific tasks. This method prioritizes an overall geographical map with focal-contextual dynamics within the network. To enable a comprehensive and in-depth understanding of pinpointed focal areas, we customize an analytical framework for analyzing inter-community networks. We extract focal sub-networks from specific nodes to create graph visualization for detailed analysis, represent rich types of domain-specific graphic properties, and provide direct zoom+filtering interactions to allow easy pattern recognition and knowledge discovery. We applied our approach to visualizing the data from interactions among 300 city-based truck communities on the largest occupational platform for truckers in China. We also conduct a case study to demonstrate that our approach is effective in supporting managers’ network analysis and knowledge discovery.
在组织内部,管理人员的具体职责和领域专长决定了他们对社会网络分析结果的兴趣。我们提出的可视化方法是为满足特定任务可视化分析的操作需求和偏好而量身定制的。这种方法优先考虑网络内具有焦点-上下文动态的整体地理图。为了能够全面深入地了解精确定位的重点区域,我们定制了一个用于分析社区间网络的分析框架。我们从特定节点中提取焦点子网络,创建用于详细分析的图形可视化,表现丰富的特定领域图形属性类型,并提供直接缩放和过滤互动,以方便模式识别和知识发现。我们将这一方法应用于中国最大的卡车司机职业平台上的 300 个城市卡车社区互动数据的可视化。我们还进行了一项案例研究,以证明我们的方法能够有效支持管理人员的网络分析和知识发现。
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引用次数: 0
Are pie charts evil? An assessment of the value of pie and donut charts compared to bar charts 饼图邪恶吗?饼图和甜甜圈图与条形图相比的价值评估
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-06-25 DOI: 10.1177/14738716241259432
Andrew Hill
Many data visualization experts recommend the use of bar charts over pie charts because they consider comparing the area or angle of segments to be less accurate than comparing bars on a bar chart. However, many studies show that when the pie chart is used to estimate proportions (arguably its main function) it is as accurate as the bar chart. A major issue is that most previous studies have only looked at one method of extracting information from pie charts, for example either by comparing the segment to the circle (the part-whole relationship) or one segment to another (relative magnitude estimation). Therefore, in this study I test multiple metrics to provide a more holistic assessment of the pie and donut chart against the bar chart. I also measured cognitive load through pupillometry. In summary, bar charts were more precise than pie and donut charts for ranking elements, but all charts were equally accurate for extracting the part-whole relationship. There was little difference in cognitive load between chart types, although bar charts were consistently faster to use on average. Overall, the bar chart was more flexible, but where there were statistically significant differences between charts, the effect sizes were often small, and unlikely to prevent effective extraction of quantitative information. That is, as long as they were used appropriately, all chart types were arguably acceptable for displaying simple, categorical data.
许多数据可视化专家建议使用柱形图而不是饼图,因为他们认为比较线段的面积或角度不如比较柱形图上的条形图准确。然而,许多研究表明,当饼状图用于估计比例时(可以说是其主要功能),其准确性不亚于条形图。一个主要的问题是,以前的大多数研究都只研究了从饼图中提取信息的一种方法,例如,要么是通过比较分段与圆圈(部分与整体的关系),要么是通过比较一个分段与另一个分段(相对大小估计)。因此,在本研究中,我测试了多个指标,以便对饼状图和甜甜圈图与条形图进行更全面的评估。我还通过瞳孔测量法测量了认知负荷。总之,条形图比饼状图和甜甜圈图在元素排序方面更精确,但所有图表在提取部分与整体的关系方面同样精确。虽然条形图的平均使用速度更快,但不同类型图表之间的认知负荷差别不大。总体而言,条形图更灵活,但如果图表之间存在统计学意义上的显著差异,其影响大小往往很小,不太可能妨碍有效提取定量信息。也就是说,只要使用得当,所有图表类型都可以用来显示简单的分类数据。
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引用次数: 0
Visually encoding orthogonal planar graph drawings as graph mazes: An eye tracking study 将正交平面图形图画作为图形迷宫进行视觉编码:眼动追踪研究
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-06-25 DOI: 10.1177/14738716241255598
Michael Burch, Marco Lemmenmeier, Robert Ospelt, Zilka Bajraktarevic
There are various styles to visually represent relational data in the form of node-link diagrams. In particular, for planar graphs we can find orthogonal node-link diagrams consisting of links bending only at ninety degrees a successful and prominent variant. One of the benefits of such drawings is the tracking of longer paths through a network with the eyes due to their limited number of link orientations, changes, and variations, but on the negative side the links can have arbitrary bending shapes. In this article we developed a novel way to visualize such orthogonal planar drawings by making use of mazes that look more natural to the human eye due to the street-like visual metaphor that many people are familiar with. Tracking paths is one of the major tasks in such graph visualizations, similar to orthogonal node-link diagrams, however, we argue that mazes are a more natural way to find paths. To get insights in the visual scanning behavior when reading graph mazes we conducted a comparative eye tracking study with 26 male versus female participants of different experience levels while also alternating between orthogonal node-link drawings and graph mazes as well as different graph size levels. The major result of this comparative study is that the participants can track paths in both representation styles, including a geodesic path tendency in their visual search behavior, but typically have a longer fixation duration at branching nodes and locations in the mazes that lead to opposite directions to the geodesic path tendency, maybe the viewers had to start a reorientation phase in their visual scanning behavior. We also found out that the size, that is the number of graph vertices has an impact on the visual scanning behavior for both orthogonal node-link diagrams as well as street-like maze representations, but for the mazes we found this impact to be less strong (in terms of the eye movement data metrics fixation durations and saccade lengths) compared to the node-link diagrams. To conclude the article, we discuss limitations and scalability issues of our approach. Moreover, we give an outlook and future work for possible extensions.
以节点链接图的形式直观表示关系数据有多种样式。特别是对于平面图,我们可以发现正交节点链接图是一种成功而突出的变体,它由仅呈九十度弯曲的链接组成。这种图的好处之一是,由于链接方向、变化和变异的数量有限,可以用眼睛追踪网络中的较长路径,但不利的一面是,链接可以有任意的弯曲形状。在这篇文章中,我们开发了一种新方法,利用迷宫将这种正交平面图可视化,由于许多人都熟悉类似街道的视觉隐喻,这种迷宫对人眼来说看起来更自然。追踪路径是此类图形可视化的主要任务之一,与正交节点链接图类似,但我们认为迷宫是一种更自然的寻找路径的方法。为了深入了解阅读图形迷宫时的视觉扫描行为,我们对 26 名不同经验水平的男性和女性参与者进行了眼动跟踪比较研究,同时交替使用正交节点链接图和图形迷宫以及不同的图形大小水平。这项比较研究的主要结果是,参与者可以追踪两种表现方式中的路径,包括视觉搜索行为中的测地路径倾向,但在迷宫中通向测地路径倾向相反方向的分支节点和位置,通常会有较长的固定时间,这可能是观看者在视觉扫描行为中不得不开始一个重新定向阶段。我们还发现,图形顶点的大小(即图形顶点的数量)对正交节点链接图和街道迷宫的视觉扫描行为都有影响,但与节点链接图相比,我们发现对迷宫的影响较小(从眼动数据指标固定持续时间和囊状移动长度来看)。最后,我们讨论了我们方法的局限性和可扩展性问题。此外,我们还对未来可能的扩展工作进行了展望。
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引用次数: 0
Visualisation of Random Forest classification 随机森林分类的可视化
IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-06-25 DOI: 10.1177/14738716241260745
Catarina Maçãs, João R Campos, Nuno Lourenço, Penousal Machado
Decision Trees (DTs) stand out as a prevalent choice among supervised Machine Learning algorithms. These algorithms form binary structures, effectively dividing data into smaller segments based on distinct rules. Consequently, DTs serve as a learning mechanism to identify optimal rules for the separation and classification of all elements within a dataset. Due to their resemblance to rule-based decisions, DTs are easy to interpret. Additionally, their minimal need for data pre-processing and versatility in handling various data types make DTs highly practical and user-friendly across diverse domains. Nevertheless, when confronted with extensive datasets or ensembles involving multiple trees, such as Random Forests, its analysis can become challenging. To facilitate the examination and validation of these models, we have developed a visual tool that incorporates a range of visualisations providing both an overview and detailed insights into a set of DTs. Our tool is designed to offer diverse perspectives on the same data, enabling a deeper understanding of the decision-making process. This article outlines our design approach, introduces various visualisation models, and details the iterative validation process. We validate our methodology through a telecommunications use case, specifically employing the visual tool to decipher how a DT-based model determines the optimal communication channel (i.e. phone call, email, SMS) for a telecommunication operator to use when contacting a client.
在有监督的机器学习算法中,决策树(DTs)是最普遍的选择。这些算法形成二进制结构,根据不同的规则有效地将数据划分为更小的部分。因此,DT 树可作为一种学习机制,用于识别数据集中所有元素的最佳分离和分类规则。由于 DTs 类似于基于规则的决策,因此很容易解释。此外,DTs 对数据预处理的需求极低,而且在处理各种数据类型方面具有多功能性,因此在不同领域都具有很强的实用性和用户友好性。然而,当面对大量数据集或涉及多棵树的集合(如随机森林)时,其分析可能会变得具有挑战性。为了便于检查和验证这些模型,我们开发了一种可视化工具,其中包含一系列可视化内容,既能提供一组 DT 的概览,也能提供详细的洞察。我们的工具旨在为相同的数据提供不同的视角,从而加深对决策过程的理解。本文概述了我们的设计方法,介绍了各种可视化模型,并详细介绍了迭代验证过程。我们通过一个电信用例来验证我们的方法,特别是利用可视化工具来解读基于 DT 的模型如何确定电信运营商在联系客户时使用的最佳通信渠道(即电话、电子邮件、短信)。
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引用次数: 0
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Information Visualization
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