可视化语言技术在软件开发中的应用

可视化语言技术比一维文本语言在描述软件组成方面具有优越性.由于图表和图形概念在系统建模中的广泛使用,可视化语言可以应用于需求分析、设计、测试和维护等软件开发的各个阶段.除了具有直观易见的特点之外,图文法在计算机上的精确建模和验证能力,为设计可视化语言提供了一个坚实的理论基础.讨论了可视化语言的形式理论基础,回顾了相关的可视化图形编程环境.特别提出了一种空间图文法,并且用该图文法定义了统一建模语言的行为语义.基于空间图文法,开发了一种基于模式驱动的框架,以帮助软件架构与设计.     

Web应用数据挖掘可视化界面布局的设计方法

在对Web应用挖掘的基本步骤作系统性研究的基础上,设计了一个Web应用挖掘可视化系统.该系统能够对用户访问Web时服务器方留下的访问记录进行挖掘,从中得出用户的访问模式和访问兴趣,并对所得出的结果进行可视化的处理.为了识别用户浏览模式利用Apriori算法对Web应用挖掘过程中预处理阶段所产生的用户会话文件进行了挖掘.采用Web图可视化了Web站点的拓扑结构以及各节点访问计数和登录计数信息.Web图的新颖之处在于两点:首先,为了将Web拓扑结构映射到Web图上,利用了站点拓扑结构数据和站点应用数据;其次,在绘制表示用户登录计数的信息层时允许通过使用动态布局的方法,以及为每一层的节点重新分配360度周长的方法来解决节点之间的冲突问题.文中较详细地阐述了该系统对Web应用数据挖掘可视化界面布局的具体措施.     

人物关系的可视化研究

大数据背景下,数据对于关系的呈现并不直观,因此人物关系的可视化研究对于抽取隐含知识具有重要的实用价值.从人物关系可视化的边-节点关系及相关信息展示的可视化需求出发,改进了D3.js类库,优化边-节点间的指向关系,便于数据处理,尤其是动态数据处理;采用改进的最短路径算法,能够求解无向图的经指定节点的全部最小环,应用于求取关系图中的人员团体关系;利用D3.js可视化类库的数据驱动特性,实现了具有一定数据交互功能的人物关系图可视化原型.     

基于B/S构架的网络结构可视化系统设计与实现

针对大规模网络节点数目庞大、结构复杂,直接对其可视化存在显示重叠、层次信息难以观察的问题,设计了一个基于B/S架构的网络结构可视化系统。该系统将网络转变为点、边的数据结构;引入网络结构特征参数和网络拓扑结构划分算法来构建网络模型;设计了针对不同需求的可视化方法和交互方式。根据与Gephi的比较和案例分析,表明系统具有表现方式灵活、部署简单、易扩展、跨平台等特点。     

时态实体依赖关系与度量方法研究

实体间存在各种各样的依赖关系,尤其是在软件开发过程中,软件实体间的依赖关系对软件的变更影响分析以及风险分析等都具有重大影响。依赖图作为最常用的依赖关系表示方法,其节点与边的定义与属性计算不尽相同,且大部分方法中并没有考虑到节点与边的时态属性。针对时态实体依赖图,文中系统地提出了时态实体依赖关系的形式化定义并分析了其特性,然后分析了时态实体依赖图的节点中心性、节点重要性、节点依赖度和边的重要性等4个度量指标,同时,针对MAVEN数据集分析了上述各个指标随时间变化的规律。     

基于可视化编程的自动化编程工具

根据不同管理信息系统及同一系统的不同模块之间在设计和实现上具有共通性的特点,提出了一种自动化编程工具.该工具利用可视化设计过程中生成的资源文件,将其中需要进行编程的控件读出并以表格形式显示于界面.编程者只需对照程序设计书在界面中进行设定,工具依据设定内容实现基于某一设计模式的自动化编程.实际运用证明该工具能有效提高软件开发效率.由于自动化编程的代码规范统一,系统的维护效率也得以大幅度提高.     

绘制动态图的IGP模型

针对可视化领域中动态图的绘制问题,提出一种新颖的可视化模型来绘制动态图.首先采用多层次方法生成初始静态图并对所有节点进行初始分区;当下一时刻的图数据来临后,先对新增节点使用重心合并排序算法的变体以确保其具有良好的初始位置,在此过程中,当有额外的附加边添加时,通过附加边长度调整算法重新调整附加边的大小以保证图的美观性;再引入"等级"的概念以提高图结构的稳定性,等级越高的节点越难以移动;最后对有变化的节点进行重新分区以简化力导向算法的排斥力计算,同时通过简化后的力导向算法优化布局.采用平均位移和总能量作为评价布局标准,用文中模型和力导向算法、节点牵制算法在Sync和Newcomb数据集上进行实验的结果表明,该模型具有较高的性能,可获得良好的布局效果.     

基于思维方式的软件开发

给出一个用于软件开发的实验性原型ＷＹＨ系统，该系统通过将面向对象的分析与设计同思维方式相结合来分析需求和设计３软件。并采用分层次的功能结构图，面向对象、操作图、类层次图和规范说明语言ＯＯＬＯ作为开发人员与用户的媒介。     

融合眼动数据和文本信息的可视化阅读辅助

研究阅读过程中的视觉注意行为特征,基于眼动数据和文本主题进行阅读行为特征和文档结构的分析,设计了眼动热区图、圆环图、节点链接图、词云等可视化形式.在此基础上开发了面向阅读辅助的可视分析原型系统,该系统记录专家用户（如老师）的眼动数据,然后通过可视化形式分享给新手用户（如学生）.用户实验结果表明,实验组用户的阅读理解客观题和主观题得分平均值分别比对照组用户提高了31.8%和55.0%,阅读和答题总用时比对照组用户平均减少了9.7%,可以有效帮助读者提高阅读效率、快速抓住文章重点和更好地理解文章内容,具有一定的有效性和可行性.     

结合短文本语义的图查询可视分析系统

异构信息网络中不同类型的节点与边能够形成丰富的语义关系,同时节点的文本属性也会对这些关系模式造成影响.相比于同构网络,异构网络的数据挖掘可以获得更有价值的结果,但是也因为异构网络节点和边的多样性使得异构网络挖掘更具有挑战性.设计有效的查询技术可以对异构网络进行网络结构和语义的分析.以往的异构网络查询方法通常采用基于元路径的图查询方法,但是如何更好地计算元路径的重要度并结合异构网络的节点文本属性进行准确率更高的查询仍然是一个需要解决的重要问题.此外,如何对查询的多个结果有效展示它们之间的语义关联和特征,对用户快速理解网络的异构关系模式也非常重要.本文受到图查询输入、子图查询和结果分析三个阶段任务的驱动,提出了一种结合短文本语义的图查询方法,并基于该方法实现了一个面向异构网络的图查询可视分析系统.本文首先从查询输入中提取可能的关系模式,使用元路径来表示不同语义的关系模式并结合用户输入的短文本计算重要度;然后本文根据元路径的重要度将多条元路径结合为用于查询的关系模式;再对查询得到的结果子图的特征向量进行降维和聚类,在此基础上对结果子图的结构特征、语义特征和节点属性进行可视化;最后本文设计并实...     

Divided edge bundling for directional network data

The node-link diagram is an intuitive and venerable way to depict a graph. To reduce clutter and improve the readability of node-link views, Holten & van Wijk's force-directed edge bundling employs a physical simulation to spatially group graph edges. While both useful and aesthetic, this technique has shortcomings: it bundles spatially proximal edges regardless of direction, weight, or graph connectivity. As a result, high-level directional edge patterns are obscured. We present divided edge bundling to tackle these shortcomings. By modifying the forces in the physical simulation, directional lanes appear as an emergent property of edge direction. By considering graph topology, we only bundle edges related by graph structure. Finally, we aggregate edge weights in bundles to enable more accurate visualization of total bundle weights. We compare visualizations created using our technique to standard force-directed edge bundling, matrix diagrams, and clustered graphs; we find that divided edge bundling leads to visualizations that are easier to interpret and reveal both familiar and previously obscured patterns.     

TreeMatrix: A Hybrid Visualization of Compound Graphs

We present a hybrid visualization technique for compound graphs (i.e. networks with a hierarchical clustering defined on the nodes) that combines the use of adjacency matrices, node‐link and arc diagrams to show the graph, and also combines the use of nested inclusion and icicle diagrams to show the hierarchical clustering. The graph visualized with our technique may have edges that are weighted and/or directed. We first explore the design space of visualizations of compound graphs and present a taxonomy of hybrid visualization techniques. We then present our prototype, which allows clusters (i.e. subtrees) of nodes to be grouped into matrices or split apart using a radial menu. We also demonstrate how our prototype can be used in the software engineering domain, and compare it to the commercial matrix‐based visualization tool Lattix using a qualitative user study.     

一种用于Marching-Graph图形绘制的快速收敛布局算法

Marching-Graph是一种将图形隐喻技术和空间隐喻技术集成为一体的新的可视化方法.它为用户提供了高度可交互性地图,使用户可访问那些具有地理属性的信息的逻辑结构.它通过有效的人图交互和跨空间浏览为用户提供了一种可视分析和挖掘未知信息的机制,而不是将已知的信息呈现在地图上.然而,传统的力导向布局算法在达到力量均衡配置方面非常慢.为使一个图形布局收敛,它们通常需花费几十秒的时间.因此。当用户快速行进于地理区间时,那些力导向布局算法就不能满足快速绘制一系列图形的要求.提出了一种快速收敛布局方法,当用户在Marching-Graph中通过力导向布局逐步探究一系列图形时,它可以加速交互时间.通过结合辐射树绘图技术和力导向图形绘制方法来取得能量最小化的快速收敛.     

Augmenting Node-Link Diagrams with Topographic Attribute Maps

We propose a novel visualization technique for graphs that are attributed with scalar data. In many scenarios, these attributes (e.g., birth date in a family network) provide ambient context information for the graph structure, whose consideration is important for different visual graph analysis tasks. Graph attributes are usually conveyed using different visual representations (e.g., color, size, shape) or by reordering the graph structure according to the attribute domain (e.g., timelines). While visual encodings allow graphs to be arranged in a readable layout, assessing contextual information such as the relative similarities of attributes across the graph is often cumbersome. In contrast, attribute-based graph reordering serves the comparison task of attributes, but typically strongly impairs the readability of the structural information given by the graph's topology. In this work, we augment force-directed node-link diagrams with a continuous ambient representation of the attribute context. This way, we provide a consistent overview of the graph's topological structure as well as its attributes, supporting a wide range of graph-related analysis tasks. We resort to an intuitive height field metaphor, illustrated by a topographic map rendering using contour lines and suitable color maps. Contour lines visually connect nodes of similar attribute values, and depict their relative arrangement within the global context. Moreover, our contextual representation supports visualizing attribute value ranges associated with graph nodes (e.g., lifespans in a family network) as trajectories routed through this height field. We discuss how user interaction with both the structural and the contextual information fosters exploratory graph analysis tasks. The effectiveness and versatility of our technique is confirmed in a user study and case studies from various application domains.     

Force-Directed Edge Bundling for Graph Visualization

Graphs depicted as node-link diagrams are widely used to show relationships between entities. However, node-link diagrams comprised of a large number of nodes and edges often suffer from visual clutter. The use of edge bundling remedies this and reveals high-level edge patterns. Previous methods require the graph to contain a hierarchy for this, or they construct a control mesh to guide the edge bundling process, which often results in bundles that show considerable variation in curvature along the overall bundle direction. We present a new edge bundling method that uses a self-organizing approach to bundling in which edges are modeled as flexible springs that can attract each other. In contrast to previous methods, no hierarchy is used and no control mesh. The resulting bundled graphs show significant clutter reduction and clearly visible high-level edge patterns. Curvature variation is furthermore minimized, resulting in smooth bundles that are easy to follow. Finally, we present a rendering technique that can be used to emphasize the bundling.     

一种面向交互的渐进式图布局算法

针对力导引算法缺乏交互性、效率低、美观性不足,提出一种面向交互的渐进式图布局算法IIA(Interaction Oriented and Incremental Algorithm)。该算法先对一小部分图数据进行可视化,然后根据用户的选择从已可视化的部分渐进式扩展,再利用改进的算法对扩展部分进行可视化。实验结果表明:IIA算法不仅能与用户交互,而且提高了可视化的效率,在美观性上也有所改善。     

Flip-Book Visualization of Dynamic Graphs

Dynamic graph visualization techniques can be based on animated or static diagrams showing the evolution over time. In this paper, we apply the concept of small multiples to visually illustrate the dynamics of a graph. Node-link, adjacency matrix, and adjacency list visualizations are used as basic visual metaphors for displaying individual graphs of the sequence. For node-link diagrams, we apply edge splatting to improve readability and reduce visual clutter caused by overlaps and link crossings. Additionally, to obtain a more scalable dynamic graph visualization in the time dimension, we integrate an interactive Rapid Serial Visual Presentation (RSVP) feature to rapidly °ip between the sequences of displayed graphs, similar to the concept of flipping a book''s pages. Our visualization tool supports the focus-and-context design principle by providing an overview of a longer time sequence as small multiples in a grid while also showing a graph in focus as a large single representation in a zoomed in and more detailed view. The usefulness of the technique is illustrated in two case studies investigating a dynamic directed call graph and an evolving social network that consists of more than 1,000 undirected graphs.     

The FlowVizMenu and parallel scatterplot matrix: hybrid multidimensional visualizations for network exploration

A standard approach for visualizing multivariate networks is to use one or more multidimensional views (for example, scatterplots) for selecting nodes by various metrics, possibly coordinated with a node-link view of the network. In this paper, we present three novel approaches for achieving a tighter integration of these views through hybrid techniques for multidimensional visualization, graph selection and layout. First, we present the FlowVizMenu, a radial menu containing a scatterplot that can be popped up transiently and manipulated with rapid, fluid gestures to select and modify the axes of its scatterplot. Second, the FlowVizMenu can be used to steer an attribute-driven layout of the network, causing certain nodes of a node-link diagram to move toward their corresponding positions in a scatterplot while others can be positioned manually or by force-directed layout. Third, we describe a novel hybrid approach that combines a scatterplot matrix (SPLOM) and parallel coordinates called the Parallel Scatterplot Matrix (P-SPLOM), which can be used to visualize and select features within the network. We also describe a novel arrangement of scatterplots called the Scatterplot Staircase (SPLOS) that requires less space than a traditional scatterplot matrix. Initial user feedback is reported.     

基于力导向模型的网络图自动布局算法综述

实现网络图形中节点和边自动布局一直是可视化研究中一个重要内容,基于力导向模型的自动布局算法则是该类研究中应用最广、文献最多的一类方法。根据研究方向出现的时间顺序,从基本模型、基于多维尺度分析的布局算法、多层迭代布局算法、非欧空间节点布局算法、受约束图形自动布局算法等五个方面对基于力引导模型的网络图自动布局算法的典型方法、研究进展、分支情况等进行了描述,并对发展前沿进行了讨论。