The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High‐Dimensional Data

Linear projections are one of the most common approaches to visualize high‐dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. The proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structures of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.     

Multidimensional Projections for Visual Analysis of Social Networks

Visual analysis of social networks is usually based on graph drawing algorithms and tools.However,social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context.Context,in its turn,is given by attributes associated with graph elements,such as individual nodes,edges,and groups of edges,as well as by the nature of the connections between individuals.In most systems,attributes of individuals and communities are not taken into consideration during graph layout,except to derive weights for force-based placement strategies.This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings.These properties are employed to layout nodes on the plane via multidimensional projection techniques.For the attribute mapping,we show that node proximity in the layout corresponds to similarity in attribute,leading to easiness in locating similar groups of nodes.The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm,reaching a meaningful layout in one pass.When a force algorithm is then applied to this initial mapping,the final layout presents better properties than conventional force-based approaches.Numerical evaluations show a number of advantages of pre-mapping points via projections.User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone.In order to allow better space usage for complex networks,a graph mapping on the surface of a sphere is also implemented.     

OLAP中一种多维数据模型

分析了传统OLAP(联机分析处理)模型在商业应用领域中存在的不足，在此基础上，针对某一超市的OLAP的设计过程中所碰到的问题，提出了一种多维数据模型，给出了模型定义和基于关系数据库模型实现方法。     

一个多维数据搜索算法

本文提出了一种使用二叉树组织多维数据的数据结构,在这种结构下提出了一个区域搜索算法,确定空间中给定点的区域内所有在给定数据集中的点,搜索效率与维数无关,算法的平均时间复杂性为O(logn),为了保证算法的效率,提出了一种平衡树操作算法。     

基于VTK三维地震数据体绘制系统框架

针对地震数据信息量大的特点,文中提出了一种针对三维地震数据体绘制系统的框架。解决的方法是将数据处理、用户交互、渲染绘制分离成三个模块,并且设计了层次结构来保持模块之间的独立性,便于各个模块的修改、更换和扩展,提高了可视化绘制系统快速开发的灵活性。基于三维可视化开发工具包VTK,设计并编程实现了一个地震数据体绘制系统界面。通过实验结果证明,此体绘制方案是可行的、有效的。     

应用敏捷开发的数据三维可视化开发框架初探

在涵盖广泛内容的可视化技术发展迅猛的大趋势下,三维可视化开发竞赛将会越发激烈,为了在竞争中获得优势,缩小开发周期,减少成本投入是非常必要的.敏捷开发正好适应当下这一形式特点,选择一种合适的开发框架对于敏捷开发尤为重要,文章通过分析和运用时下的各种技术框架对数据三维可视化开发框架进行了初步的探索,提出了一种基于Web的开发框架,并介绍了应用此框架的系统实现.     

GRAAL: A Framework for Low-Power 3D Graphics Accelerators

The GRAphics AcceLerator (GRAAL) design-exploration framework is an open system that offers a coherent development methodology for hardware/software cosimulation and codesign of embedded 3D graphics accelerators. GRAAL incorporates tools to help visually debug graphics algorithms implemented in hardware and to estimate performance in terms of throughput, power consumption, and area.     

基于场景语义的3D交互体系结构

借鉴真实世界的认知心理学原理,将虚拟场景的可视表达和语义信息结合起来共同服务于用户的交互过程,多种3D交互技术被融合在一个统一的交互框架内,使复杂虚拟环境中的3D用户界面更容易被用户理解和使用.通过增强场景图的语义处理能力,建立支持高层语义的3D用户界面体系结构,3D交互系统不仅在几何层上而且还能在语义层上支持交互任务的执行.最后介绍了一个应用实例.     

一种数据仓库的多维数据模型

数据模型是数据仓库研究的核心问题之一.很多研究表明,传统数据模型(如实体联系模型和关系模型)不能有效地表示数据仓库的数据结构和语义,也难以有效地支持联机分析处理(on-line analysis processing,简称OLAP).最近,人们提出了几种多维数据模型.但是,这些多维数据模型在表示数据仓库的复杂数据结构和语义以及OLAP操作方面仍显不足.该文以偏序和映射为基础,提出了一种新的多维数据模型.该数据模型能够充分表达数据仓库的复杂数据结构和语义,并提供一个以OLAP操作为核心的操作代数,支持层次结构间的复杂聚集操作序列,能够有效地支持OLAP应用.该数据模型支持聚集函数约束的概念,提供了表示层次结构间聚集函数约束的机制.     

Reconstruction of 3D Objects from Orthographic Projections

An algorithm for automatic reconstruction of 3D objects from their orthographic projections is presented in this paper. It makes some improvements to, and complements the, Wesley-Markowsky algorithm, which is a typical hierarchical reconstruction algorithm limited to polyhedral objects, and extracts the idea of pattern recognition expressed in the Aldefeld algorithm. It is shown in theory by analysis and in practice by implementation that the proposed algorithm successfully rejected pathological cases and found all solutions with the same set of orthographic views. Compared with the existing algorithms presented in references, this algorithm covers some more complex cases of objects incorporating cylinders.     

多准则框架下的蛋白质三维结构相似性检索

蛋白质三维结构与功能的关系问题是当前生命科学领域的一个重大科学问题,蛋白质三维结构相似性比较则是探索蛋白质结构与功能关系的一种重要手段．文中就蛋白质结构在空间分布的特殊性提出了一种多准则框架下的蛋白质三维结构相似性检索模型．在该模型下,通过三种蛋白质空间旋转与平移不变特征的获取,实现了蛋白质骨架空间走向函数的一致性、蛋白质骨架距离直方图的一致性以及蛋白质距离矩阵数据分布一致性的多准则相似性检索策略．作者对实际的27804个蛋白质样本数据库进行实验,结果表明该文所提出的检索模式以及相似性准则的设计是实现大规模蛋白质三维结构相似性检索的一种有效方法．     

Multidimensional illumination functions for visualization of complex 3D environments

This paper presents a new view-independent, energy equilibrium method for determining the light distributed in a complex 3D environment consisting of surfaces with general reflectance properties. The method does not depend on discretization of directions or discretization of surfaces to differential elements. Hence, it is a significant improvement over the earlier complete view-independent method which is computationally intractable for complex environments or the hybrid methods which include an extended view-dependent ray tracing second pass. The new method is based on an efficient data structure of order O(N²) called the spherical cover. The spherical cover elegantly captures the complex multidimensional directional nature of light distributed over surfaces. Subdivision techniques based on range estimation of various parameters using interval-arithmetic-like methods are next described for efficiently computing the spherical cover for a given 3D environment. Using the spherical cover, light is progressively propagated through the environment until energy equilibrium is reached. Complexity analysis of the propagation step is carried out to show that the method is computationally tractable. The paper also includes a comprehensive review of earlier rendering techniques viewed from the point of view of capturing the multidimensional nature of light distribution over surfaces.     

Visualizing Multidimensional Data with Order Statistics

Multidimensional data sets are common in many domains, and dimensionality reduction methods that determine a lower dimensional embedding are widely used for visualizing such data sets. This paper presents a novel method to project data onto a lower dimensional space by taking into account the order statistics of the individual data points, which are quantified by their depth or centrality in the overall set. Thus, in addition to conveying relative distances in the data, the proposed method also preserves the order statistics, which are often lost or misrepresented by existing visualization methods. The proposed method entails a modification of the optimization objective of conventional multidimensional scaling (MDS) by introducing a term that penalizes discrepancies between centrality structures in the original space and the embedding. We also introduce two strategies for visualizing lower dimensional embeddings of multidimensional data that takes advantage of the coherent representation of centrality provided by the proposed projection method. We demonstrate the effectiveness of our visualization with comparisons on different kinds of multidimensional data, including categorical and multimodal, from a variety of domains such as botany and health care.     

Visualizing Multidimensional Data with Glyph SPLOMs

Scatterplot matrices or SPLOMs provide a feasible method of visualizing and representing multi‐dimensional data especially for a small number of dimensions. For very high dimensional data, we introduce a novel technique to summarize a SPLOM, as a clustered matrix of glyphs, or a Glyph SPLOM. Each glyph visually encodes a general measure of dependency strength, distance correlation, and a logical dependency class based on the occupancy of the scatterplot quadrants. We present the Glyph SPLOM as a general alternative to the traditional correlation based heatmap and the scatterplot matrix in two examples: demography data from the World Health Organization (WHO), and gene expression data from developmental biology. By using both, dependency class and strength, the Glyph SPLOM illustrates high dimensional data in more detail than a heatmap but with more summarization than a SPLOM. More importantly, the summarization capabilities of Glyph SPLOM allow for the assertion of “necessity” causal relationships in the data and the reconstruction of interaction networks in various dynamic systems.     

A Framework for Developing Enterprise Data Warehouses

Learn to follow a five-stage framework designed to accommodate the common challenges encountered by project teams when building enterprise data warehousing solutions. This framework is an architecture-driven approach that subsequently involves selection of add-on data warehouse tools and meeting ongoing administration requirements.     

数据仓库设计过程研究

从数据仓库与操作型数据库的区别出发，借鉴传统数据库设计的阶段划分思想给出了一种数据仓库设计方法，并明确了各步骤间的关系及其输入／输出和完成的工作。     

Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes

We describe a flexible model for representing images of objects of a certain class, known a priori, such as faces, and introduce a new algorithm for matching it to a novel image and thereby perform image analysis. The flexible model, known as a multidimensional morphable model, is learned from example images of objects of a class. In this paper we introduce an effective stochastic gradient descent algorithm that automatically matches a model to a novel image. Several experiments demonstrate the robustness and the broad range of applicability of morphable models. Our approach can provide novel solutions to several vision tasks, including the computation of image correspondence, object verification and image compression.     

一种支持空间拓扑分析的3维数据模型

3维空间数据模型是构建3维空间数据库和3维GIS的基础，有着极为重要的研究意义。为了能更好地表示空间3维对象，提出了一种基于3D-realms的数据模型，它是对基于2D-realms的空间数据模型的3维扩展，并详细给出了该模型的基本定义、语义描述和基于该模型的3维空间对象操作，运用分层的方法可将该模型分为4层：3维几何原语层、3D-realms层、3维基本空间构建要素层和3维基本空间对象层（包括点、线、面和体）。其中，由于每个较低层都是构建较高层中元素的基础，因此可为上层提供支持。实验表明，基于3D-realms的3维空间数据模型不仅能保证拓扑关系的正确性，而且能较好地表示3维空间对象，并能有效地支持3维空间拓扑分析操作。