基于Louvain算法的图数据三维树形可视化

提出一种图数据的三维树形可视化方法,基于Louvain算法对图数据中的复杂的网络关系进行层次聚类,利用三维树形映射表达聚类结果,直观展示隐含于图数据中的结构关系,通过在三维场景中旋转、缩放、移动、拾取高亮等交互操作多视角地展示数据。集成开源图数据库Neo4j研发原型系统,并开展案例数据实验。实验结果表明,该方法不仅能够简洁灵活地展示图数据的总体层次结构,还能够多样化地表达数据细节,为利用虚拟现实技术探索图数据的潜在信息提供有效的技术支持。     

Visualizing 3D flow

We discuss volume line integral convolution (LIC) techniques for effectively visualizing 3D flow, including using visibility-impeding halos and efficient asymmetric filter kernels. Specifically, we suggest techniques for selectively emphasizing critical regions of interest in a flow; facilitating the accurate perception of the 3D depth and orientation of overlapping streamlines; efficiently incorporating an indication of orientation into a flow representation; and conveying additional information about related scalar quantities such as temperature or vorticity over a flow via subtle, continuous line width and color variations     

Visualizing Web search results in 3D

The exponential growth in Web sites is making it increasingly difficult to extract useful information on the Internet using existing search engines. Despite a wide range of sophisticated indexing and data retrieval features, search engines often deliver satisfactory results only when users know precisely what they are looking for. Traditional textual interfaces present results as a list of links to Web pages. Because most users are unwilling to explore an extensive list, search engines arbitrarily reduce the number of links returned, aiming also to provide quick response times. Moreover, their proprietary ranking algorithms often do not reflect individual user preferences. Those who need comprehensive general information about a topic or have vague initial requirements instead want a holistic presentation of data related to their queries. To address this need, we have developed Periscope, a 3D search result visualization system that displays all the Web pages found in a synthetic, yet comprehensible format.     

Visualizing network data

Networks are critical to modern society, and a thorough understanding of how they behave is crucial to their efficient operation. Fortunately, data on networks is plentiful; by visualizing this data, it is possible to greatly improve our understanding. Our focus is on visualizing the data associated with a network and not on simply visualizing the structure of the network itself. We begin with three static network displays; two of these use geographical relationships, while the third is a matrix arrangement that gives equal emphasis to all network links. Static displays can be swamped with large amounts of data; hence we introduce direct manipulation techniques that permit the graphs to continue to reveal relationships in the context of much more data. In effect, the static displays are parameterized so that interesting views may easily be discovered interactively. The software to carry out this network visualization is called SeeNet     

Visualizing oceanographic data

The use of visualization to study both field-collected and simulated oceanographic data is discussed. An overview is presented of three programs in which visualization has provided important insight or technology. The diverse technology used-animation, computer vision, and synthesis of signal processing, image processing, and display-illustrate the variety of methodologies that are increasing knowledge about the ocean environment     

Visualizing 3D configuration spaces for mechanical design

We describe configuration space visualization methods for mechanical design. The research challenge is to relate the configuration space geometry to the mechanical function of the parts. Our research addresses the fundamental design task of contact analysis. Contacts are the physical primitives that make mechanical systems out of collections of parts. Systems perform functions by transforming motions via part contacts. The shapes of the interacting parts impose constraints on their motions that largely determine the system function. Contact analysis involves deriving and analyzing these constraints. Designers use contact analysis to ensure correct function and to optimize performance. We illustrate contact analysis on the film advance of a movie camera     

空间科学卫星数据快速处理方法

针对卫星获取的大规模数据进行快速数据处理一直是空间信息处理系统建设中的关键。面对空间科学卫星全天候观测、探测载荷类型多、处理算法多样带来的数据处理难题,现有基于CCSDS标准格式的数据分析方法,难以满足目前在轨的多颗空间科学卫星数据处理系统在正确性和时效性方面的要求。针对空间科学卫星探测数据处理特点,提出了一种空间科学数据快速处理方法,设计两层联合索引结构,将空间科学大数据处理问题转化为索引表和源包数据单元的处理问题,提高了数据处理效率;采用科学工作流技术设计了数据驱动和业务驱动协同的处理框架,支持多样化的空间科学卫星数据处理流程,各类载荷数据处理任务并行调度。实验结果表明,这种方法处理速度可扩展,内存使用较少,已应用于空间科学卫星地面系统中,取得了良好的效果。     

Visualizing multiwavelength astrophysical data

With recent advances in the measurement technology for allsky astrophysical imaging, our view of the sky is no longer limited to the tiny visible spectral range over the 2D Celestial sphere. We now can access a third dimension corresponding to a broad electromagnetic spectrum with a wide range of allsky surveys; these surveys span frequency bands including long wavelength radio, microwaves, very short X-rays, and gamma rays. These advances motivate us to study and examine multiwavelength visualization techniques to maximize our capabilities to visualize and exploit these informative image data sets. In this work, we begin with the processing of the data themselves, uniformizing the representations and units of raw data obtained from varied detector sources. Then we apply tools to map, convert, color-code, and format the multiwavelength data in forms useful for applications. We explore different visual representations for displaying the data, including such methods as textured image stacks, the horseshoe representation, and GPU-based volume visualization. A family of visual tools and analysis methods is introduced to explore the data, including interactive data mapping on the graphics processing unit (GPU), the mini-map explorer, and GPU-based interactive feature analysis.     

Visualizing production planning data

Visualization of production planning data for decision making in manufacturing is nearly nonexistent. The planning problems at the Electronic Card Assembly and Test plant (ECAT) at IBM Austin, Texas, prompted the research project reported in this article. The project studied the characteristics of this type of managerial data, then developed special visualization techniques for constructing visual representations to support planners in developing superior production plans. A visualization prototype called VIZ planner was designed, implemented and empirically evaluated in a laboratory setting. Using this tool, hundreds of products, thousands of components, and many other factors can be visualized to provide planners with production planning insight     

Visualizing spatial multivalue data

We introduce multivalue data as a new data type in the context of scientific visualization. While this data type has existed in other fields, the visualization community has largely ignored it. Formally, a multivalue datum is a collection of values about a single variable. Multivalue data sets can be defined for multiple dimensions. A spatial multivalue data set consists of a multivalue datum at each physical location in the domain. The time dimension is equally valid. This leads to spatio-temporal multivalue data sets where there is time varying, multidimensional data with a multivalue datum at each location and time. The spatial multivalue data type captures multiple instances of the same variable at each location in space. Visualizing spatial multivalue data sets is a new challenge.     

Visualizing large-scale uncertainty in astrophysical data

Visualization of uncertainty or error in astrophysical data is seldom available in simulations of astronomical phenomena, and yet almost all rendered attributes possess some degree of uncertainty due to observational error. Uncertainties associated with spatial location typically vary signicantly with scale and thus introduce further complexity in the interpretation of a given visualization. This paper introduces effective techniques for visualizing uncertainty in large-scale virtual astrophysical environments. Building upon our previous transparently scalable visualization architecture, we develop tools that enhance the perception and comprehension of uncertainty across wide scale ranges. Our methods include a unified color-coding scheme for representing log-scale distances and percentage errors, an ellipsoid model to represent positional uncertainty, an ellipsoid envelope model to expose trajectory uncertainty, and a magic-glass design supporting the selection of ranges of log-scale distance and uncertainty parameters, as well as an overview mode and a scalable WIM tool for exposing the magnitudes of spatial context and uncertainty.     

Visualizing sparse gridded data sets

Gridded data sets with many missing values pose a problem because most visualization algorithms fail when presented with incomplete cells. We discuss visualization methods that handle this problem. Our primary interest is developing 3D images for Next-Generation Radar (Nexrad), a weather radar that makes a series of conical scans. Most of the time it has extremely sparse returns. Current visualization techniques for Nexrad simply discard the 3D nature of the data set and provide 2D plots of the grid's lowest layer, leaving the missing data colored black or transparent. Most standard visualization packages either fail or give incorrect visualizations in 3D because of the unusual nature of the data set     

Visualizing and modeling unstructured data

Scientific data are often sampled at unstructured spatial locations because of physical constraints, yet most visualization software applies only to gridded or regular data. We discuss several effective techniques for representing scalar and vector-valued functions that interpolate to irregularly located data. Special attention is given to the situations in which the sampling domain is a 2D plane, a 3D volume, or a closed 3D surface. The interpolants can be evaluated on a fine regular grid and they can then be visualized with conventional techniques. Instead of giving a comprehensive survey of this subject, we concentrate on several methods that were developed in the last couple of years.     

Normal estimation in 3 D discrete space

Three-dimensional voxel-based objects are inherently discrete and do not maintain any notion of a continuous surface or normal values, which are crucial for the simulation of light behavior. Thus, in volume rendering, the normal vector of the displayed surfaces must be estimated prior to rendering. We survey several methods for normal estimation and analyze their performance. One unique method, the context-sensitive approach, employs segmentation and segment-bounded operators that are based on object and slope discontinuities in order to achieve high fidelity normal estimation for rendering volumetric objects.     

Visualizing large telecommunication data sets

Global telecommunication services create an enormous volume of real time data. Long distance voice networks, for example, can complete more than 250 million calls a day; wide area data networks can support many hundreds of thousands of virtual circuits and millions of Internet protocol (IP) flows and Web server sessions. Unlike terabyte databases, which typically contain images or multimedia streams, telecommunication databases mainly contain numerous small records describing transactions and network status events. The data processing involved therefore differs markedly, both in the number of records and the data items interpreted. To efficiently configure and operate these networks, as well as manage performance and reliability for the user, these vast data sets must be understandable. Increasingly, visualization proves key to achieving this goal. AT&T Infolab is an interdisciplinary project created in 1996 to explore how software, data management and analysis, and visualization can combine to attack information problems involving large scale networks. The data Infolab collects daily reaches tens of gigabytes. The Infolab project Swift-3D uses interactive 3D maps with statistical widgets, topology diagrams, and pixel oriented displays to abstract network data and let users interact with it. We have implemented a full scale Swift-3D prototype, which generated the examples presented     

Curvature approximation of 3D manifolds in 4D space

A method for the approximation of the three principal curvatures at points on a discretized, triangulated 3D manifold in 4D space (referred to as 3-surface) is presented. The approximation scheme is based on the fact that a parametric 3-surface can locally be approximated by the graph of a trivariate function. Using a local coordinate system, a least square polynomial approximation is constructed for the estimation of the principal curvatures at each 3-surface point. Curvature is extremely useful for the analysis of qualitative characteristics of surfaces. The technique presented is an example of extending existing surface interrogation methods to multivariate data. Such a generalization is valuable for scientific visualization.     

一种基于WebVR实现3D音效可视化的方法

由于VR场景中用户可以自主选择观看的方向和角度,为了获得最佳的体验效果,VR音频技术要求通过声音辨别方向、距离等反馈更多的空间环境状态。本文主要论述了一种基于WebVR实现音频空间可视化的方法,核心是运用了Web Audio API的PannerNode和AudioListener两个对象处理音频源,其中Three.js提供了完善的音频空间化支持。最后通过VR Audio的案例进行测试,实验表明根据用户的位置和旋转矩阵,可更新音频空间信息。     

基于流式计算的空间科学卫星数据实时处理

针对空间科学卫星探测数据的实时处理要求越来越高的问题,提出一种基于流计算框架的空间科学卫星数据实时处理方法。首先,根据空间科学卫星数据处理特点对数据流进行抽象分析;然后,对各处理单元的输入输出数据结构进行重新定义;最后,基于流计算框架Storm设计数据流处理并行结构,以适应大规模数据并行处理和分布式计算的要求。对应用该方法开发的空间科学卫星数据处理系统进行测试分析,测试结果显示,在相同条件下数据处理时间比原有系统缩短了一半;数据局部性策略比轮询策略具有更高的吞吐率,数据元组吞吐率平均提高29%。可见采用流式计算框架能够大幅缩短数据处理延迟,提高空间科学卫星数据处理系统的实时性。