A dimensionality reduction algorithm and its application for interactive visualization

Visualization is one of the most effective methods for analyzing how high-dimensional data are distributed. Dimensionality reduction techniques, such as PCA, can be used to map high dimensional data to a two- or three-dimensional space. In this paper, we propose an algorithm called HyperMap that can be effectively applied to visualization. Our algorithm can be seen as a generalization of FastMap. It preserves its linear computation complexity, and overcomes several main shortcomings, especially in visualization. Since there are more than two pivot objects in each axis of a target space, more distance information needs to be preserved in each dimension. Then in visualization, the number of pivot objects can go beyond the limitation of six (2-pivot objects × 3-dimensions). Our HyperMap algorithm also gives more flexibility to the target space, such that the data distribution can be observed from various viewpoints. Its effectiveness is confirmed by empirical evaluations on both real and synthetic datasets.     

On numerical algorithm and interactive visualization for optimal control problems

We present methods for the visualization of the numerical solution of optimal control problems. The solution is based on dynamic programming techniques where the corresponding optimal value function is approximated on an adaptively refined grid. This approximation is then used in order to compute approximately optimal solution trajectories. We discuss requirements for the efficient visualization of both the optimal value functions and the optimal trajectories and develop graphic routines that in particular support adaptive, hierarchical grid structures, interactivity and animation. Several implementational aspects using the Graphics Programming Environment ‘GRAPE’ are discussed. Received: 4 December 1997 / Accepted: 5 August 1998     

A streaming edge sampling method for network visualization

Knowledge and Information Systems - Visualization strategies facilitate streaming network analysis by allowing its exploration through graphical and interactive layouts. Depending on the strategy...     

基于Web的PSE-Bio交互式可视化

PSE-Bio是一个基于Web服务面向生物信息学的问题求解平台。为了方便用户仅使用浏览器就能分析调用服务返回的结果,采用SVG技术实现基于WEB的可视化,设计了面向对象的统一可视化数据对象以封装调用不同Web服务返回的消息;设计了面向对象的可视化图形对象以利于交互可视化的实现;并用SVG脚本语言实现了用户与图形的交互以及可视化结果与服务的交互。实验结果表明,基于SVG的可视化方案能够满足生物信息数据的Web可视化需求。     

OpinionSeer: interactive visualization of hotel customer feedback

The rapid development of Web technology has resulted in an increasing number of hotel customers sharing their opinions on the hotel services. Effective visual analysis of online customer opinions is needed, as it has a significant impact on building a successful business. In this paper, we present OpinionSeer, an interactive visualization system that could visually analyze a large collection of online hotel customer reviews. The system is built on a new visualization-centric opinion mining technique that considers uncertainty for faithfully modeling and analyzing customer opinions. A new visual representation is developed to convey customer opinions by augmenting well-established scatterplots and radial visualization. To provide multiple-level exploration, we introduce subjective logic to handle and organize subjective opinions with degrees of uncertainty. Several case studies illustrate the effectiveness and usefulness of OpinionSeer on analyzing relationships among multiple data dimensions and comparing opinions of different groups. Aside from data on hotel customer feedback, OpinionSeer could also be applied to visually analyze customer opinions on other products or services.     

Knowledge visualization: A new framework for interactive graphic interface design

Diagrams communicate massive amounts of information at a glance. Complex domains can be simplified and extended with diagrammatic notations. Computational systems can certainly benefit from the use of diagrams. However, graphic interfaces are difficult and time consuming to write. We need a way of shortening the graphic-interface building cycle so that it is relatively easy and fast to add a graphic interface to any application that may benefit from it.A general-purpose, graphic-interface-building tool kit that a designer or user, not a programmer, can use to design and attach graphic interfaces to applications can greatly speed up and lower the costs of adding graphics to systems. In this paper, I describe a new framework for interactive graphic interface design. The framework will enable graphic-interface-building tools which are general purpose, inter-active, and application-specific.The framework consists of a taxonomy (ontology) of visual properties that span sub-object properties, full objects, and the relationships between objects. The taxonomy forms a skeleton on which to hang methods for manipulating these visual properties, objects, relations, and composites. The methods consist of the generation of prototypes, the recognition of properties in objects, and mouse manipulation functions for modifying properties in an object. Further characteristics of the framework are that the properties are composable, that objects can be explicitly, incrementally described through repeated composition and application of recognition methods, and that the composition of properties to form more fully described and more complex objects is recursive. This makes the framework and the objects within it quite flexible, incremental, uniform, and modular.     

Web-based dynamic and interactive environmental visualization

The development of novel interactive visualizations can substantially contribute to environmental modeling and understanding of complex environmental processes. To allow the Web Map Service (WMS) to support this, this paper explores a generic framework in which a dynamic environmental modeling process can be interactively visualized, controlled and tuned on the Internet by using Java 3D-based hybrid Internet computation through the simulation steering technique. A hydrological model, TOPMODEL, is employed to illustrate the simulation of a rainfall-run off process within a computational and steering environment. This environment allows users to visually interact and experiment with model computation, thereby facilitating the exploration of process-based environmental applications.     

A general streaming algorithm for pattern discovery

Discovering frequent patterns over event sequences is an important data mining problem. Existing methods typically require multiple passes over the data, rendering them unsuitable for streaming contexts. We present the first streaming algorithm for mining frequent patterns over a window of recent events in the stream. We derive approximation guarantees for our algorithm in terms of: (i) the separation of frequent patterns from the infrequent ones, and (ii) the rate of change of stream characteristics. Our parameterization of the problem provides a new sweet spot in the tradeoff between making distributional assumptions over the stream and algorithmic efficiencies of mining. We illustrate how this yields significant benefits when mining practical streams from neuroscience and telecommunications logs.     

ESPSA: A prediction-based algorithm for streaming time series segmentation

Streaming time series segmentation is one of the major problems in streaming time series mining, which can create the high-level representation of streaming time series, and thus can provide important supports for many time series mining tasks, such as indexing, clustering, classification, and discord discovery. However, the data elements in streaming time series, which usually arrive online, are fast-changing and unbounded in size, consequently, leading to a higher requirement for the computing efficiency of time series segmentation. Thus, it is a challenging task how to segment streaming time series accurately under the constraint of computing efficiency. In this paper, we propose exponential smoothing prediction-based segmentation algorithm (ESPSA). The proposed algorithm is developed based on a sliding window model, and uses the typical exponential smoothing method to calculate the smoothing value of arrived data element of streaming time series as the prediction value of the future data. Besides, to determine whether a data element is a segmenting key point, we study the statistical characteristics of the prediction error and then deduce the relationship between the prediction error and the compression rate. The extensive experiments on both synthetic and real datasets demonstrate that the proposed algorithm can segment streaming time series effectively and efficiently. More importantly, compared with candidate algorithms, the proposed algorithm can reduce the computing time by orders of magnitude.     

GO: A cluster algorithm for graph visualization

As we are in the big data age, graph data such as user networks in Facebook and Flickr becomes large. How to reduce the visual complexity of a graph layout is a challenging problem. Clustering graphs is regarded as one of effective ways to address this problem. Most of current graph visualization systems, however, directly use existing clustering algorithms that are not originally developed for the visualization purpose. For graph visualization, a clustering algorithm should meet specific requirements such as the sufficient size of clusters, and automatic determination of the number of clusters. After identifying the requirements of clustering graphs for visualization, in this paper we present a new clustering algorithm that is particularly designed for visualization so as to reduce the visual complexity of a layout, together with a strategy for improving the scalability of our algorithm. Experiments have demonstrated that our proposed algorithm is capable of detecting clusters in a way that is required in graph visualization.     

色彩渐进插值的矿井预警数据集三维可视化算法

针对矿井预警数据信息表达不完全、基于视觉的统计分析工作繁重、预警数据集庞杂等问题,提出了一种基于色彩渐进插值的矿井预警数据集三维可视化算法。在该算法中,首先根据矿井预警数据集的测点位置和测量值信息进行三维空间模型构造;然后根据灰度级与彩色空间系统的映射关系对矿井预警数据集与彩色空间模型进行颜色映射及三维空间层次分割,对每个层片依据伪图像编码算法及颜色聚类参数特征进行矿井预警数据集的三维可视化伪图像编码;最后根据色彩渐进插值算法对伪图像中相邻层片进行平滑过渡处理。实验证明,该算法处理的矿井预警数据集伪图像色彩渲染层次感强,色彩过渡平滑,有利于矿井预警数据集的信息表达。     

TimeLine and visualization of multiple-data sets and the visualization querying challenge

Data in its raw form can potentially contain valuable information, but much of that value is lost if it cannot be presented to a user in a way that is useful and meaningful. Data visualization techniques offer a solution to this issue. Such methods are especially useful in spatial data domains such as medical scan data and geophysical data. However, to properly see trends in data or to relate data from multiple sources, multiple-data set visualization techniques must be used. In research with the time-line paradigm, we have integrated multiple streaming data sources into a single visual interface. Data visualization takes place on several levels, from the visualization of query results in a time-line fashion to using multiple visualization techniques to view, analyze, and compare the data from the results. A significant contribution of this research effort is the extension and combination of existing research efforts into the visualization of multiple-data sets to create new and more flexible techniques. We specifically address visualization issues regarding clarity, speed, and interactivity. The developed visualization tools have also led recently to the visualization querying paradigm and challenge highlighted herein.     

DaisyViz: A model-based user interface toolkit for interactive information visualization systems

While information visualization technologies have transformed our life and work, designing information visualization systems still faces challenges. Non-expert users or end-users need toolkits that allow for rapid design and prototyping, along with supporting unified data structures suitable for different data types (e.g., tree, network, temporal, and multi-dimensional data), various visualization, interaction tasks. To address these issues, we designed DaisyViz, a model-based user interface toolkit, which enables end-users to rapidly develop domain-specific information visualization applications without traditional programming. DaisyViz is based on a user interface model for information (UIMI), which includes three declarative models: data model, visualization model, and control model. In the development process, a user first constructs a UIMI with interactive visual tools. The results of the UIMI are then parsed to generate a prototype system automatically. In this paper, we discuss the concept of UIMI, describe the architecture of DaisyViz, and show how to use DaisyViz to build an information visualization system. We also present a usability study of DaisyViz we conducted. Our findings indicate DaisyViz is an effective toolkit to help end-users build interactive information visualization systems.     

Pursuing interactive visualization of irregular grids

Many branches of the sciences produce sample data not arranged on rectilinear grids, and visualization of such data is generally more complex than visualizing more regular data. While isosurface algorithms such as marching cubes can be adapted with relatively little performance penalty, direct volume rendering presents more difficulties. This paper explores these issues, concentrating on algorithms for a particular kind of irregular grid known as a curvilinear grid, composed of six-sided cells that may be considered a result of the deformation of a regular rectilinear grid.     

分层交互式进化计算及其应用

针对目前交互式进化计算存在的局部搜索能力不强、效宰低下等问题，将分层的思想引入交互式进化计算，提出了分层交互式进化计算．给出了算法实施的关键问题，分析了算法的效宰．将其应用于服装设计，通过算例验证了算法的有效性．     

A particle system for interactive visualization of 3D flows

We present a particle system for interactive visualization of steady 3D flow fields on uniform grids. For the amount of particles we target, particle integration needs to be accelerated and the transfer of these sets for rendering must be avoided. To fulfill these requirements, we exploit features of recent graphics accelerators to advect particles in the graphics processing unit (GPU), saving particle positions in graphics memory, and then sending these positions through the GPU again to obtain images in the frame buffer. This approach allows for interactive streaming and rendering of millions of particles and it enables virtual exploration of high resolution fields in a way similar to real-world experiments. The ability to display the dynamics of large particle sets using visualization options like shaded points or oriented texture splats provides an effective means for visual flow analysis that is far beyond existing solutions. For each particle, flow quantities like vorticity magnitude and A2 are computed and displayed. Built upon a previously published GPU implementation of a sorting network, visibility sorting of transparent particles is implemented. To provide additional visual cues, the GPU constructs and displays visualization geometry like particle lines and stream ribbons.     

基于节点集计算能力差异的Hadoop自适应任务调度算法

针对异构集群任务推测式执行算法存在的任务进度比例固定、落后任务被动选取等问题,提出基于快慢节点集计算能力差异的自适应任务调度算法。该算法量化节点集计算能力差异实现分集调度,并通过节点与任务速率的动态反馈及时更新快慢节点集,提高节点集资源利用率与任务并行度。在两节点集中,利用动态调整任务进度比例判别落后任务,主动选择采用替代执行方式为落后任务执行备份任务的快节点,从而提升任务执行效率。与最长近似结束时间(LATE)算法的实验对比结果表明,该算法在短作业集、混合型作业集、出现节点性能下降的混合型作业集执行时间上比LATE算法分别缩短了5.21%、20.51%、23.86%,启用的备份任务数比LATE算法明显减少。所提算法可使任务主动适应节点差异,在减少备份任务的同时有效提高作业整体执行效率。     

The computation of hitting sets: Review and new algorithms

In model-based diagnosis or other research fields, the hitting sets of a set cluster are usually used. In this paper we introduce some algorithms, including the new BHS-tree and Boolean algebraic algorithms. In the BHS-tree algorithm, a binary-tree is used for the computation of hitting sets, and in the Boolean algebraic algorithm, components are represented by Boolean variables. It runs just for one time to catch the minimal hitting sets. We implemented the algorithms and present empirical results in order to show their superiority over other algorithms for computing hitting sets.     

JHAVE: supporting algorithm visualization

JHAVE fosters the use of algorithm visualization as an effective pedagogical tool for computer science educators, helping students to better understand algorithms. The Java-hosted algorithm visualization environment (JHAVE) is not an AV system itself but rather a support environment for a variety of AV systems (called AV engines by JHAVE). In broad terms, JHAVE gives such an engine a drawing context on which it can render its pictures in any way. In return, JHAVE provides the engine with effortless ways to synchronize its graphical displays with i) a standard set of VCR-like controls, ii) information and pseudocode windows, iii) input generators, iv) stop-and-think questions, and v) meaningful content generation tools.