专家系统推理的可视化研究

该文在介绍可视化基本概念及ICON系统形式化规范基础上,提出了专家系统推理过程可视化问题,并给出了一个具有可视特性的知识库系统的一般模型。     

推理过程可视化研究

本文在简述可视化的研究现状及其特点的基础上，提出了知识库系统中推理过程可视化的问题，并给出了具有可视特性的知识库系统的一般模型。     

面向对象知识库系统CAOBS／V1.2的可视化查询子系统

面向对象知识库系统ＣＡＯＢＳ／Ｖ１．２的用户界面是一个可视化知识查询子系统，它包括一个灵活、直观、方便的可视知识查询语言（ＶＫＱＬ）和一个知识库浏览编辑器。利用该可视化查询语言，用户可以通过构造查询图来表达查询要求，并最终实现查询功能。ＶＫＱＬ吸收了国际上现有的几种可视查询语言的优点，具有较强的查询功能，使用方便。文中介绍了ＶＫＱＬ的设计思想和实现技术，在最后一节将ＶＫＱＬ与同类查询语言进行了对比     

知识图谱可视化综述

知识图谱是结构化的语义网络知识库,以符号形式描述概念及其相互关系.由于知识图谱常被用于交互的知识的发现与推理,知识图谱可视化受到众多关注.总结了知识图谱可视化相关工作,介绍了知识图谱的可视表达,分析了可视表达适用的知识图谱类型;简述了大规模知识图谱可视化的挑战与可视化方法、异质网络可视分析方法;介绍了知识图谱可视化的应用案例,并预测了该领域的未来发展方向和挑战.     

联邦学习可视化:挑战与框架

联邦学习是一种保证数据隐私安全的分布式机器学习方案.与传统的机器学习的可解释性问题类似,如何对联邦学习进行解释是一个新的挑战.文中面向联邦学习方法的分布式与隐私安全性的特性,探讨联邦学习的可视化框架设计.传统的可视化任务需要使用大量的数据,而联邦学习的隐私性决定了其无法获取用户数据.因此,可用的数据主要来自服务器端的训练过程,包括服务器端模型参数和用户训练状态.基于对联邦学习可解释性的挑战的分析,文中综合考虑用户、服务器端和联邦学习模型3个方面设计可视化框架,其包括经典联邦学习模型、数据中心、数据处理和可视分析4个模块.最后,介绍并分析了2个已有的可视化案例,对未来通用的联邦学习可视分析方法提出了展望.     

DaisyVA:支持信息多面体可视分析的智能交互式可视化平台

针对目前缺乏有效地支持信息多面体可视分析的交互式信息可视化平台的问题,提出并实现了一个面向最终用户的交互式可视化平台DaisyVA.首先建立了一个支持信息多面体可视分析界面模型IMFA,并定义了IMFA的多面体数据模型、可视表征模型和交互控制模型;然后围绕IMFA设计了DaisyVA的体系架构,讨论了基于模型的系统开发机制、核心模块与运行时框架机理,以及DaisyVA组件库;最后将DaisyVA应用于大型集团制造企业物流网络瓶颈的可视分析实例中.实例分析结果表明,DaisyVA能为多面体数据提供统一建模的支持和可扩展可视化算法库的支持、提供多种所需的交互技术并可灵活定制可视分析的交互任务、支持信息多面体间的内在语义关联分析,以及能够为最终用户提供一种简单快速原型方法,因此能够为信息多面体可视分析提供一种有效的支持.     

基于交互式可视组件的分析决策环境研究

针对人类分析决策活动的分步骤、交互化特点,基于认知心理学相关理论研究了分析决策过程中的人机协同工作模型,提出了基于交互式可视组件的分析决策交互任务框架,建立了交互式可视组件的统一描述,研究了交互式可视组件的实现技术,包括运行过程中信息可视化技术、交互技术和配置过程中的交互历史复用技术等,构建了一个交互式可视组件的分析决...     

扩充逻辑程序设计的R－演算──知识库维护的操作方法

本文首先介绍了知识库维护过程中诸如知识库序列、新规则、用户反驳以及重构等概念；然后给出了一个扩充逻辑程序设计的框架，在这一框架下，每个逻辑程序等价于一个知识库；进一步定义了一个转换系统，称为扩充逻辑程序设计的Ｒ－演算，对一个给定的知识库和用户反驳，此演算可以导出知识库的最佳修正；同时证明了该演算的可靠性和完备性；另外，对本文的工作与其他相关工作进行了比较；最后，给出了本文的结论．     

大规模接警日志语义时空模式交互探索

接警日志包含时间、空间和案件描述信息,属于非结构时空数据.与时空社交媒体相比,接警日志的数据项之间存在较少的联系,数据项之间不能形成复杂网络关系,在挖掘其数据模式时难以提供有价值的线索,因此,其分析更加依赖于其中的语义挖掘和语义时空模式探索.针对这一问题,提出了一个可视分析框架支持对大规模非结构接警日志时空模式的交互探索.首先,提出了一种基于主题模型集成的方法,实现从异构文本中抽取主题;其次,该框架包含一个数据立方体,实现快速响应用户的查询请求;第三,设计并实现了一个可视化交互系统,支持对数据立方体的可视化交互探索.最后,使用国内某城市真实接警日志进行实验,找到的丰富的模式和主题预测准确性证明了方法的有效性.     

扩充逻辑程序设计的R—演算—知识库维护的操作方法

本文首先介绍了知识库维护过程中诸如知识库序列、新规则、用户反驳以及重构等概念；然后给出了一个扩充逻辑程序设计的框架，在这一框架下，每个逻辑程序等价于一个知识库；进一步定义了一个转换系统，称为扩充逻辑程序设计的Ｒ－演算，对一个给定的知识库和用户反驱，此演算可以导出知识库的最佳修正；同时证明了该演算的可靠性和完备性；另外，对本文的工作与其他相关工作进行了比较；最后，给出了本文的结论。     

In situ exploration of large dynamic networks

The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.     

Knowledge visualization for evaluation tasks

Although various methods for the evaluation of intelligent systems have been proposed in the past, almost no techniques are present that support the manual inspection of knowledge bases by the domain specialist. Manual knowledge base inspection is an important and frequently applied method in knowledge engineering. Since it can hardly be performed in an automated manner, it is a time-consuming and costly task. In this paper, we discuss a collection of appropriate visualization techniques that help developers to interactively browse and analyze the knowledge base in order to find deficiencies and semantic errors in their implementation. We describe standard visualization methods adapted to specifically support the analysis of the static knowledge base structure, but also of the usage of knowledge base objects such as questions or solutions. Additionally, we introduce a novel visualization technique that supports the validation of the derivation and interview behavior of a knowledge system in a semi-automatic manner. The application of the presented methods was motivated by the daily practice of knowledge base development.     

Evaluating 2D Flow Visualization Using Eye Tracking

Flow visualization is recognized as an essential tool for many scientific research fields and different visualization approaches are proposed. Several studies are also conducted to evaluate their effectiveness but these studies rarely examine the performance from the perspective of visual perception. In this paper, we aim at exploring how users’ visual perception is influenced by different 2D flow visualization methods. An eye tracker is used to analyze users’ visual behaviors when they perform the free viewing, advection prediction, flow feature detection, and flow feature identification tasks on the flow field images generated by different visualizations methods. We evaluate the illustration capability of five representative visualization algorithms. Our results show that the eye‐tracking‐based evaluation provides more insights to quantitatively analyze the effectiveness of these visualization methods.     

Quality Metrics for Information Visualization

The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi‐ and high‐dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.     

一种基于视觉知识加工模型的目标识别方法

提出了一种基于视觉知识加工模型的目标识别方法. 该加工模型结合目标定位、模板筛选和MFF-HMAX (Hierarchical model and X based on multi-feature fusion)方法对图像进行学习, 形成相应的视觉知识库, 并用于指导目标的识别. 首先, 利用Itti模型获取图像的显著区, 结合视觉通路中What和Where通道的位置、大小等特征以及视觉知识库中的定位知识确定初期候选目标区域; 然后, 采用二步去噪处理获取候选目标区域, 利用MFF-HMAX模型提取目标区域的颜色、亮度、纹理、轮廓、大小等知识特征, 并采用特征融合思想将各项特征融合供目标识别; 最后, 与单一特征以及目前的流行方法进行对比实验, 结果表明本文方法不仅具备较高的识别效果, 同时能够模仿人脑学习视觉知识的过程形成视觉知识库.     

MViewer: mobile phone spatiotemporal data viewer

Nowadays movement patterns and people’s behavioral models are needed for traffic engineers and city planners. These observations could be used to reason about mobility and its sustainability and to support decision makers with reliable information. The very same knowledge about human diaspora and behavior extracted from these data is also valuable to the urban planner, so as to localize new services, organize logistics systems and to detect changes as they occur in the movement behavior. Moreover, it is interesting to investigate movement in places like a shopping area or a working district either for commercial purposes or for improving the service quality. These kinds of tracking data are made available by wireless and mobile communication technologies. It is now possible to record and collect a large amount of mobile phone calls in a city. Technologies for object tracking have recently become affordable and reliable and hence we were able to collect mobile phone data from a city in China from January 1, 2008 to December 31, 2008. The large amount of phone call records from mobile operators can be considered as life mates and sensors of persons to inform howmany people are present in any given area and how many are entering or leaving. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by analyzing mobile phone data. Through mobile phones, we can learn the information about locations, communications between mobile phone users during their daily lives. In this work, we propose a comprehensive visual analysis system named as MViewer, Mobile phone spatiotemporal data Viewer, which is the first system to visualize and analyze the population’smobility patterns from millions of phone call records. Our system consists of three major components: 1) visual analysis of user groups in a base station; 2) visual analysis of the mobility patterns on different user groups making phone calls in certain base stations; 3) visual analysis of handoff phone call records. Some well-established visualization techniques such as parallel coordinates and pixelbased representations have been integrated into our system. We also develop a novel visualization schemes, Voronoidiagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone datasets that are kindly provided by our project partners and obtained some interesting findings regarding people’s mobility patterns.     

面向军事群体的聚合及解聚可视化控制模型

作战编队是军事领域特有的军事群体组织方式,具有广泛的军事应用,针对战场态势中作战编队的聚合/解聚可视化问题,基于知识图谱及模型-视图-控制器(MVC)设计模式,提出了面向作战编队的聚合/解聚可视化控制模型,主要研究作战编队基于MVC的架构设计、作战编队基于知识图谱语义建模、基于主成分分析（PCA）的作战编队区域几何辅助对象构建、作战编队解聚/聚合ADLOD显示及地图比例尺控制以及作战编队聚合/解聚可视化模型的向量形式表征,该模型既弥补了军事标绘相关标准中作战群体方面研究的空白,也为联合作战态势多分辨率显示优化提供了基于作战编队聚合简化的新技术途径,同时,提供的可视化手段可加深对战场综合态势的认知,提升作战指挥决策水平,军事应用前景良好。     

A Study On Designing Effective Introductory Materials for Information Visualization

Designing introductory materials is extremely important when developing new information visualization techniques. All users, regardless of their domain knowledge, first must learn how to interpret the visually encoded information in order to infer knowledge from visualizations. Yet, despite its significance, there has been little research on how to design effective introductory materials for information visualization. This paper presents a study on the design of online guides that educate new users on how to utilize information visualizations, particularly focusing on the employment of exercise questions in the guides. We use two concepts from educational psychology, learning type (or learning style) and teaching method, to design four unique types of online guides. The effects of the guides are measured by comprehension tests of a large group of crowdsourced participants. The tests covered four visualization types (graph, scatter plot, storyline, and tree map) and a complete range of visual analytics tasks. Our statistical analyses indicate that online guides which employ active learning and the top‐down teaching method are the most effective. Our study provides quantitative insight into the use of exercise questions in online guides for information visualizations and will inspire further research on design considerations for other elements in introductory materials.     

Attention and visual memory in visualization and computer graphics

A fundamental goal of visualization is to produce images of data that support visual analysis, exploration, and discovery of novel insights. An important consideration during visualization design is the role of human visual perception. How we "see" details in an image can directly impact a viewer's efficiency and effectiveness. This paper surveys research on attention and visual perception, with a specific focus on results that have direct relevance to visualization and visual analytics. We discuss theories of low-level visual perception, then show how these findings form a foundation for more recent work on visual memory and visual attention. We conclude with a brief overview of how knowledge of visual attention and visual memory is being applied in visualization and graphics. We also discuss how challenges in visualization are motivating research in psychophysics.