Quang Vinh Nguyen

EncCon: an approach to constructing interactive visualization of large hierarchical data

This paper describes a new technique called EncCon for visualizing and navigating large hierarchical information. This technique consists of two components: visualization and navigation. Visualization uses a fast enclosure + connection method to calculate the geometrical layout for the display of large hierarchies in a two-dimensional space. Our technique uses a rectangular division algorithm for recursively positioning the graph. This visualization aims to maximize the utilization of display space while retaining a good geometrical layout as well as a clear (explicit) presentation of the hierarchical structure of graphs. This paper also presents an experimental evaluation of EncCons layout algorithm. Besides the layout algorithm, EncCon uses a new focus + context viewing technique for the navigation of large hierarchies. We use the zooming + layering concept to achieve the focus + context viewing, rather than the traditional enlarge + embedded concept, which is used by most of the available focus + context techniques. Technically, it employs semi-transparency to achieve the display of two layers of information in z-coordination at the same visualization. Both context view and detail view are drawn at two separate layers. These layers are then displayed in an overlapped manner at the same physical screen space.

A space-optimized tree visualization

We describe a new method for the visualization of tree structured relational data. It can be used especially for the display of very large hierarchies in a 2-dimensional space. We discuss the advantages and limitations of current techniques of tree visualization. Our strategy is to optimize the drawing of trees in a geometrical plane and maximize the utilization of display space by allowing more nodes and links to be displayed at a limit screen resolution. We use the concept of enclosure to partition the entire display space into a collection of local regions that are assigned to all nodes in tree T for the display of their sub-trees and themselves. To enable the exploration of large hierarchies, we use a modified semantic zooming technique to view the detail of a particular part of the hierarchy at a time based on users interest. Layout animation is also provided to preserve the mental map while the user is exploring the hierarchy by changing zoomed views.

Space-optimized tree: a connection+enclosure approach for the visualization of large hierarchies

This paper describes a new approach, space-optimized tree, for the visualization and navigation of tree-structured relational data. This technique can be used especially for the display of very large hierarchies in a two-dimensional space. We discuss the advantages and limitations of current techniques of tree visualization. Our strategy is to optimize the drawing of trees in a geometrical plane and maximize the utilization of display space by allowing more nodes and links to be displayed at a limited screen resolution. Space-optimized tree is a connection+enclosure visualization approach that recursively positions children of a subtree into polygon areas and still uses a node–link diagram to present the entire hierarchical structure. To be able to handle the navigation of large hierarchies, we use a new hybrid viewing technique that combines two viewing methods, the modified semantic zooming and a focus+context technique. While the semantic zooming technique can enlarge a particular viewing area by filtering out the rest of tree structure from the visualization, the focus+context technique allows the user to interactively focus, view and browse the entire visual structure with a reasonable high-density display.

A Visualization Approach for Frauds Detection in Financial Market

The traditional solutions to the stock market security are not sufficient in identifying attackers and further attack plans from the analysis of existing events.Therefore, it is difficult for analysts to prevent future unexpected events or frauds by only monitoring the realtime trading information. The event-driven fraud detection in financial market could not help analysts to find attack plans and the further intention of attackers. This paper proposed a new framework of visual analytics for stock market security. The proposed solution consists of two stages: 1) Visual Surveillance of Market Performance, and 2) Behavior-Driven Visual Analysis of Trading Networks.In the first stage, we use a 3D treemaps to monitor the real-time stock market performance and to identify a particular stock that produced an unusual trading pattern. We then move to the next stage: social network visualization to conduct behavior-driven visual analysis of suspected pattern. Through the visual analysis of social (or trading)network, analysts may finally identify the attackers (the sources of the fraud), and further attack plans.

A Fast Algorithm for Balanced Graph Clustering

Scalability problem is a long-lasting challenge for both information visualization and graph drawing communities. Available graph visualization techniques could perform well for small or medium size graphs but they are rarely able to handle very large and complex graphs. One of effective approach to solve this problem is to employ graph abstraction; that is to hierarchically partitioning the complete graph into a clustered graph. A graph visualization technique is then applied to display the abstract view of this clustered graph with partially displayed detail of one or a few sub-graphs where the user is currently focusing on. This reduces the complexity of display and makes it easier for users to interpret, perceive and navigate the large scale information. In this paper, we propose a graph clustering method which can quickly discover the community structure embedded in large graphs and partition the graph into densely connected sub-graphs. The proposed algorithm can not only run fast, but also achieve a consistent partitioning result in which a graph is divided into a set of clusters of the similar size in terms of their visual complexity and the number of nodes and edges. In addition, we also provide a mechanism to partition very dense graphs in which the number of edges is much larger than the number of nodes.

Visual Information Communication

This paper proposes a new method called Concentric Coordinate for visualizing multidimensional datasets. To reduce the overlapping and edge crossings among curves, axes are arranged as concentric circles rather than parallel lines that are commonly used in the traditional approach. Edges which represent data items are drawn as segments of curves rather than poly-lines drawn in the classical parallel coordinate approach. Some heuristics are applied in our new method in order to improve the readability of views. The paper demonstrates the advantages of new method. In comparison with the parallel coordinate method, our concentric circle approach can reduce more than 15 % of the edge overlaps and crossings by visualizing the same dataset. In our new approach, we further enhance the readability of views by increasing the crossing angle. Finally, a visual interactive network scans detection system called CCScanViewer is developed based on our new visualization method to represent traffic activities in network flows, and the experiments show that the new approach is effective in detecting unusual patterns of network scans, port scans, the hidden scans, DDoS attacks etc.

A focus+context visualization technique using semi-transparency

This paper describes a new 2D focus+context visualization technique for viewing and navigating large hierarchical information. This technique uses a semi-transparency approach to display both the global and the detail views of the hierarchy with an overlapped manner in the same display space. The twin views always keep one in highlighted and another in deemphasized, and this state can be shifted interactively when a user focuses on other view. The main advantage of this technique is that there is no geometrical partitioning required and both the context view and the focus view can use the full display space. To achieve the smooth transactions between the views, animation is accommodated to preserver users mental map during the navigation. Although this visualization technique can theoretically be applied to many layout algorithms, we only apply this technique, in our implementation, for viewing and navigating hierarchies that are drawn by ENCCON (Nguyen and Huang, 2004) layout algorithm.

Visualizing large trees with divide & conquer partition

While prior works on enclosure approach, guarantees the space utilization of a single geometrical area, mostly rectangle, this paper proposes a flexible enclosure tree layout method for partitioning various polygonal shapes that break through the limitation of rectangular constraint. Similar to Treemap techniques, it uses enclosure to divide display space into smaller areas for its sub-hierarchies. The algorithm can partition a polygonal shape or even an arbitrary shape into smaller polygons, rotated rectangles or vertical-horizontal rectangles. The proposed method and implementation algorithms provide an effective interactive visualization tool for partitioning large hierarchical structures within a confined display area with different shapes for real-time applications. We demonstrated the effective of the new method with a case study, an automated evaluation and a usability study.

A new visualization approach for supporting knowledge management and collaboration in e-learning

This work introduces an information visualization component that appears as an additional window in an online collaborative e-learning environment, and can be used for viewing, browsing, editing and manipulating knowledge-based learning information through the LiveNet (http://138.25.13.210:8000/ln4-1) -a knowledge management and enterprise collaboration tool. This visual component employs a new visualization technique, called ENCCON (Nguyen and Huang, 2004), which uses a connection + enclosure layout method and a semi-transparency navigation mechanism to handle large scale of hierarchical learning information and it aims to provide a better assistance to the users of LiveNet for visual manipulation and navigation of knowledge-based data objects. The use of visualization techniques in LiveNet provides users with not only a 2D graphical visual interface for direct data manipulation, but also a set of clear views, the focus+context views, of the interrelations among the data objects which enhances the understanding of relational information that the users want to see. Although the proposed visualization technique ENCCON can be applied in many domains, we only concern its application in the domain of collaborative e-learning through the LiveNet.

Unlocking the Complexity of Port Data With Visualization

Interactive visual analysis can support displaying complex multidimensional data. We developed a novel system, which can incorporate domain knowledge and visual guidelines, to present complex logistical data. This paper presents the details of the development process of that system. Diagrammatic visualization approach was adopted for presenting multidimensional port data, in relation to events. The paper discusses case studies with land-side data and wharf-side data obtained from Port Botany in Sydney, Australia. We conducted a usability study with 20 students that compared performance with our visualization against a traditional bar chart. Task completion was significantly shorter with the diagrammatic visualization, and the visualization was rated higher in overall user preference.