Illustrative deformation for data exploration

Much of the visualization research has focused on improving the rendering quality and speed, and enhancing the perceptibility of features in the data. Recently, significant emphasis has been placed on focus+context (F+C) techniques (e.g., fisheye views and magnification lens) for data exploration in addition to viewing transformation and hierarchical navigation. However, most of the existing data exploration techniques rely on the manipulation of viewing attributes of the rendering system or optical attributes of the data objects, with users being passive viewers. In this paper, we propose a more active approach to data exploration, which attempts to mimic how we would explore data if we were able to hold it and interact with it in our hands. This involves allowing the users to physically or actively manipulate the geometry of a data object. While this approach has been traditionally used in applications, such as surgical simulation, where the original geometry of the data objects is well understood by the users, there are several challenges when this approach is generalized for applications, such as flow and information visualization, where there is no common perception as to the normal or natural geometry of a data object. We introduce a taxonomy and a set of transformations especially for illustrative deformation of general data exploration. We present combined geometric or optical illustration operators for focus+context visualization, and examine the best means for preventing the deformed context from being misperceived. We demonstrated the feasibility of this generalization with examples of flow, information and video visualization.     

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。     

Interactive visualization and analysis of hierarchical neuralprojections for data mining

Dimensionality reducing mappings, often also denoted as multidimensional scaling, are the basis for multivariate data projection and visual analysis in data mining. Topology and distance preserving mapping techniques-e.g., Kohonen's self-organizing feature map (SOM) or Sammon's nonlinear mapping (NLM)-are available to achieve multivariate data projections for the following interactive visual analysis process. For large data bases, however, NLM computation becomes intractable. Also, if additional data points or data sets are to be included in the projection, a complete recomputation of the mapping is required. In general, a neural network could learn the mapping and serve for arbitrary additional data projection. However, the computational costs would also be high, and convergence is not easily achieved. In this work, a convenient hierarchical neural projection approach is introduced, where first an unsupervised neural network-e.g., a SOM-quantizes the data base, followed by fast NLM mapping of the quantized data. In the second stage of the hierarchy, an enhancement of the NLM by a recall algorithm is applied. The training and application of a second neural network, which is learning the mapping by function approximation, is quantitatively compared with this new approach. Efficient interactive visualization and analysis techniques, exploiting the achieved hierarchical neural projection for data mining, are presented.     

A new intrusion detection system using support vector machines and hierarchical clustering

Whenever an intrusion occurs, the security and value of a computer system is compromised. Network-based attacks make it difficult for legitimate users to access various network services by purposely occupying or sabotaging network resources and services. This can be done by sending large amounts of network traffic, exploiting well-known faults in networking services, and by overloading network hosts. Intrusion Detection attempts to detect computer attacks by examining various data records observed in processes on the network and it is split into two groups, anomaly detection systems and misuse detection systems. Anomaly detection is an attempt to search for malicious behavior that deviates from established normal patterns. Misuse detection is used to identify intrusions that match known attack scenarios. Our interest here is in anomaly detection and our proposed method is a scalable solution for detecting network-based anomalies. We use Support Vector Machines (SVM) for classification. The SVM is one of the most successful classification algorithms in the data mining area, but its long training time limits its use. This paper presents a study for enhancing the training time of SVM, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the Dynamically Growing Self-Organizing Tree (DGSOT) algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering algorithms (e.g., hierarchical agglomerative clustering). Clustering analysis helps find the boundary points, which are the most qualified data points to train SVM, between two classes. We present a new approach of combination of SVM and DGSOT, which starts with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We compare our approach with the Rocchio Bundling technique and random selection in terms of accuracy loss and training time gain using a single benchmark real data set. We show that our proposed variations contribute significantly in improving the training process of SVM with high generalization accuracy and outperform the Rocchio Bundling technique.     

Flow Mapping and Multivariate Visualization of Large Spatial Interaction Data

Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many variables are associated with each flow, such as the number of migrants for different age groups, income levels, and occupations. It is a challenging task to visualize such data and discover network structures, multivariate relations, and their geographic patterns simultaneously. This paper addresses these challenges by developing an integrated interactive visualization framework that consists three coupled components: (1) a spatially constrained graph partitioning method that can construct a hierarchy of geographical regions (communities), where there are more flows or connections within regions than across regions; (2) a multivariate clustering and visualization method to detect and present multivariate patterns in the aggregated region-to-region flows; and (3) a highly interactive flow mapping component to map both flow and multivariate patterns in the geographic space, at different hierarchical levels. The proposed approach can process relatively large data sets and effectively discover and visualize major flow structures and multivariate relations at the same time. User interactions are supported to facilitate the understanding of both an overview and detailed patterns.     

Structure-based brushes: a mechanism for navigating hierarchicallyorganized data and information spaces

Interactive selection is a critical component in exploratory visualization, allowing users to isolate subsets of the displayed information for highlighting, deleting, analysis, or focused investigation. Brushing, a popular method for implementing the selection process, has traditionally been performed in either screen space or data space. In this paper, we introduce an alternate, and potentially powerful, mode of selection that we term structure-based brushing, for selection in data sets with natural or imposed structure. Our initial implementation has focused on hierarchically structured data, specifically very large multivariate data sets structured via hierarchical clustering and partitioning algorithms. The structure-based brush allows users to navigate hierarchies by specifying focal extents and level-of-detail on a visual representation of the structure. Proximity-based coloring, which maps similar colors to data that are closely related within the structure, helps convey both structural relationships and anomalies. We describe the design and implementation of our structure-based brushing tool. We also validate its usefulness using two distinct hierarchical visualization techniques, namely hierarchical parallel coordinates and tree-maps. Finally, we discuss relationships between different classes of brushes and identify methods by which structure-based brushing could be extended to alternate data structures     

Hierarchical GTM: constructing localized nonlinear projectionmanifolds in a principled way

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets and, therefore, a hierarchical visualization system is desirable. In this paper, we extend an existing locally linear hierarchical visualization system PhiVis in several directions: 1) We allow for nonlinear projection manifolds. The basic building block is the Generative Topographic Mapping (GTM). 2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. 3) Using tools from differential geometry, we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the ancestor visualization plots which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 18-dimensional data sets     

QoS enhancements for global replication management in peer to peer networks

Replica Management is a key issue to reduce the bandwidth consumption, to improve data availability and to maintain data consistency in large distributed systems. Global Replica Management (GRM) means to maintain the data consistency across the entire network. It is preferable particularly for multi-group distributed systems. On the other hand, GRM is not favorable for many applications because a very large number of message passes is needed for replica management processes. In this paper, in order to reduce the number of message passes needed to achieve the efficient GRM strategy, an interconnection structure called the Distributed Spanning Tree (DST) has been employed. The application of DST converts the peer network into logical layered structures and thereby provides a hierarchical mechanism for replication management. It is proved that this hierarchical approach improves the data availability and consistency across the entire network. In addition to these, it is also proved that the proposed approach reduces the data latency and the required number of message passes for any specific application in the network.     

Social Network Clustering and Visualization using Hierarchical Edge Bundles

The hierarchical edge bundle (HEB) method generates useful visualizations of dense graphs, such as social networks, but requires a predefined clustering hierarchy, and does not easily benefit from existing straight‐line visualization improvements. This paper proposes a new clustering approach that extracts the community structure of a network and organizes it into a hierarchy that is flatter than existing community‐based clustering approaches and maps better to HEB visualization. Our method not only discovers communities and generates clusters with better modularization qualities, but also creates a balanced hierarchy that allows HEB visualization of unstructured social networks without predefined hierarchies. Results on several data sets demonstrate that this approach clarifies real‐world communication, collaboration and competition network structure and reveals information missed in previous visualizations. We further implemented our techniques into a social network visualization application on facebook.com and let users explore the visualization and community clustering of their own social networks.     

Hierarchical and adaptive visualization on nested grids

Modern numerical methods are capable to resolve fine structures in solutions of partial differential equations. Thereby they produce large amounts of data. The user wants to explore them interactively by applying visualization tools in order to understand the simulated physical process. Here we present a multiresolution approach for a large class of hierarchical and nested grids. It is based on a hierarchical traversal of mesh elements combined with an adaptive selection of the hierarchical depth. The adaptation depends on an error indicator which is closely related to the visual impression of the smoothness of isosurfaces or isolines, which are typically used to visualize data. Significant examples illustrate the applicability and efficiency on different types of meshes.     

Eventual Clusterer: A Modular Approach to Designing Hierarchical Consensus Protocols in MANETs

This paper proposes a modular approach to the design of hierarchical consensus protocols for the mobile ad hoc network with a static and known set of hosts. A two-layer hierarchy is imposed on the network by grouping mobile hosts into clusters, each with a clusterhead. The messages from and to the hosts in the same cluster are merged/unmerged by the clusterhead so as to reduce the message cost and improve the scalability. The proposed modular approach separates the concerns of clustering hosts from achieving consensus. A clustering function, called eventual clusterer (denoted as diamC), is designed for constructing and maintaining the two-layer hierarchy. Similar to unreliable failure detectors, diamC greatly facilitates the design of hierarchical protocols by providing the fault-tolerant clustering function transparently. We propose an implementation of diamC based on the failure detector diamS. Using diamC, we design a new hierarchical consensus protocol. As shown by the performance evaluation results, the proposed consensus protocol can save both message cost and time cost. Our proposed modular design is therefore effective and can lead to efficient solutions to achieving consensus in mobile ad hoc networks.     

一种层次结构中多维属性的可视化方法

在很多领域的统计分析中,通常需要分析既具有层次结构又具有多维属性的复杂数据,如食品安全数据、股票数据、网络安全数据等.针对现有多维数据和层次结构的可视化方法不能满足对同时具有层次和多维两种属性数据的可视分析要求,提出了一种树图中的多维坐标MCT(multi-coordinate in treemap)技术.该技术采用基于Squarified和Strip布局算法的树图表示层次结构,用树图中节点矩形的边作为属性轴,通过属性映射、属性点连接、曲线拟合实现层次结构中多维属性的可视化.将该技术应用于全国农药残留侦测数据,实现了对全国各地区、各超市、各农产品中农药残留检出和超标情况的可视化,为领域人员提供了有效的分析工具.MCT技术也可用于其他领域的层次多属性数据的可视化.     

层次泛函网络整体学习算法

文中设计了一类单输人单输出泛函网络与双输人单输出泛函网络作为构造层次泛函网络基本模型,提出了一种层次泛函网络模型,给出了层次泛函网络构造方法和整体学习算法,而层次泛函网络的参数利用解方程组来进行逐层学习．以非线性代数方程组为例,指出人们熟知的一些数学解题方法可以用层次泛函网络来表达,探讨了基于层次泛函网络求解非线性代数方程组学习算法实现的一些技术问题．相对传统方法,层次泛函网络更适合于具有层次结构的应用领域．计算机仿真结果表明,这种层次学习方法具有较快的收敛速度和良好的逼近性能．     

Automatic Animation for Time‐Varying Data Visualization

This paper presents a digital storytelling approach that generates automatic animations for time‐varying data visualization. Our approach simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. Specifically, we analyze information related to a given event and abstract it as an event graph, which represents data features as nodes and event relationships as links. This graph embeds a tree‐like hierarchical structure which encodes data features at different scales. Next, narrative structures are built by exploring starting nodes and suitable search strategies in this graph. Different stages of narrative structures are considered in our automatic rendering parameter decision process to generate animations as digital stories. We integrate this animation generation approach into an interactive exploration process of time‐varying data, so that more comprehensive information can be provided in a timely fashion. We demonstrate with a storm surge application that our approach allows semantic visualization of time‐varying data and easy animation generation for users without special knowledge about the underlying visualization techniques.     

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。     

An interrogative visualization environment for large-scale engineering simulations

An interrogative visualization environment is described for the interactive display and querying of large datasets. The environment combines a web-based intelligent agent facility with a visualization engine. The intelligent agent facility (IAF) incorporates a rule-based expert system for natural-language understanding, voice and text input facilities, a hierarchical clickable command list, an interface for multimodal devices such as menu-based wireless handheld devices and gesture recognition devices, and human-like avatars acting as virtual assistants. The IAF interacts with, and controls, the visualization engine through a TCP/IP network socket interface. The environment enables multiple users using a variety of interaction modes and devices to effectively browse through large amounts of data, focus on and query interesting features, and more easily comprehend and make use of the data. Application of the environment to the visualization of engineering simulations is described.     

An object-oriented database management system for computer-aided design of tall buildings

An effective database and database management system is the key to the success of an integrated approach to software engineering applications in general, and Computer-Aided Design (CAD) for structural applications in particular. Due to the inherent nature of CAD data such as dynamic modeling, a wide range of data types, large data volume, etc., the traditional database models, such as hierarchical, network and relational models, are unable to handle the aforementioned applications satisfactorily. An object-oriented data modeling is known to be the most effective approach. However, many of the commercial object-oriented databases are designed for information management, and they are inadequate for CAD application due to the different features of the object-hierarchy and varying data management objectives during the design cycles. This paper presents a hierarchical index-based object-oriented database management model for CAD applications. To deal with the object hierarchy encountered in CAD for the design of tall buildings, the proposed database consists of several salient features: a hierarchical object model, its related storage structure, a data dictionary, a class factory and an index system. The proposed database management model has been implemented into an integrated CAD system for design application of tall buildings.     

Ranked centroid projection: a data visualization approach with self-organizing maps

The self-organizing map (SOM) is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text mining. The proposed approach first transforms the document space into a multidimensional vector space by means of document encoding. Afterwards, a growing hierarchical SOM (GHSOM) is trained and used as a baseline structure to automatically produce maps with various levels of detail. Following the GHSOM training, the new projection method, namely the ranked centroid projection (RCP), is applied to project the input vectors to a hierarchy of 2-D output maps. The RCP is used as a data analysis tool as well as a direct interface to the data. In a set of simulations, the proposed approach is applied to an illustrative data set and two real-world scientific document collections to demonstrate its applicability.     

A mathematical morphology based method for hierarchical clustering analysis of spatial points on street networks

Spatial clustering analysis is an important issue that has been widely studied to extract the meaningful subgroups of geo-referenced data. Although many approaches have been developed in the literature, efficiently modeling the network constraint that objects (e.g. urban facility) are observed on or alongside a street network remains a challenging task for spatial clustering. Based on the techniques of mathematical morphology, this paper presents a new spatial clustering approach NMMSC designed for mining the grouping patterns of network-constrained point objects. NMMSC is essentially a hierarchical clustering approach, and it generally consists of two main steps: first, the original vector data is converted to raster data by utilizing basic linear unit of network as the pixel in network space; second, based on the specified 1-dimensional raster structure, an extended mathematical morphology operator (i.e. dilation) is iteratively performed to identify spatial point agglomerations with hierarchical structure snapped on a network. Compared to existing methods of network-constrained hierarchical clustering, our method is more efficient for cluster similarity computation with linear time complexity. The effectiveness and efficiency of our approach are verified through the experiments with real and synthetic data sets.