Abstract Painters arrange white space in contrast with chromatic space composed of strokes. This research measures white space, color complexity and stroke density in Chinese ink paintings and examines how these attributes influence the paintings’ perceived complexity. Empirical evidence from 21 well-known modern Chinese artists’ ink paintings shows that white space decreases paintings’ complexity, while chromatic space and stroke density increase complexity. We also reveal that a large rate of white space guides the viewers’ attention on chromatic space and enhances the impacts of color complexity and stroke density on perceived complexity. An eye-tracker measures viewers’ elaboration duration on each painting, which provides consistent evidence to validate our conclusion based on subjective reported visual complexity. Our research provides insights into the rhetorical role of white space in sensory information processing.

Summary Correlation analysis has long been a question of great interest in measuring the relationship among different variables and has been applied in many fields, such as dimension reduction, classification, and so on. However, current methods of correlation analysis take into account the linear relationship between multiple variables and only few works on nonlinear interaction of two variables have been considered. In this article, we first present a nonlinear analysis method of multiple (two or more) variables based on mutual information for tensor analysis (MITA). In addition, we extend the mutual‐information matrix analysis directly to MITA and show the multivariable mutual information formula based on Venn diagram. Experiments on multiview dimension reduction, including attacking internet traffic prediction, advertisement classification, and biometric structure prediction illustrate the effectiveness of the proposed method, especially in the case of low‐dimensional subspace.

Hierarchical structures are very common in the real world for recording all kinds of relational data generated in our daily life and business procedures. A very popular visualization method for displaying such data structures is called "Tree". So far, there are a variety of Tree visualization methods that have been proposed and most of them can only visualize one hierarchical dataset at a time. Hence, it raises the difficulty of comparison between two or more hierarchical datasets.In this paper, we proposed Pansy Tree which used a tree metaphor to visualize merged hierarchies. We design a unique icon named pansy to represent each merged node in the structure. Each Pansy is encoded by three colors mapping data items from three different datasets in the same hierarchical position (or tree node). The petals and sepal on Pansy are designed for showing each attribute’s values and hierarchical information. We also redefine the links in force layout encoded by width and animation to better convey hierarchical information. We further apply Pansy Tree into CNCEE datasets and demonstrate two use cases to verify its effectiveness.The main contribution of this work is to merge three datasets into one tree that makes it much easier to explore and compare the structures, data items and data attributes with visual tools.

Dynamic networks are networks in which nodes and edges come and go at different times. The dynamic nature of this type of networks has imposed a challenge in network visualisation. Many techniques have been proposed to visualise dynamic networks in an effort of not only capturing the dynamic features of networks over the time but also laying out the network in a way that is fast and makes the visualisation aesthetically pleasing. However, empirical research has shown that the current techniques have limitations in one way or another. In the paper, we present an interactive visualisation called DGViewer which gives users control and flexibility allowing them to decide what, when and how to view visualisations to improve the analysis and comprehension of dynamic networks. A preliminary usability study indicates that users are positive with this user-centred visualisation for better user experience and task performance.

Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades. In this work, we conduct a non-medical/clinical approach, generating graphs from five features concluded from the COVID-19 outbreak data and local community data in NSW (New South Wales), Australia, and offering insights from a visual analysis perspective. The results show that household income, population density and ethnicity affect the infection in different areas. Features such as human behaviours need to be imported for further COVID-19 research in the data science sector. This work is an initial step into this area and allows more insights to be brought into future COVID-19 research through a visual analysis perfective.

This paper introduces the use of pictogram design concept in ancient China for the development of a set of today's "graphic icons" or "infographics" in modern social network visualization systems. These graphic icons should be close to sensory symbols that derive their expressive power from their ability to use the perceptual processing power of the brain without learning. Therefore, with the use of such a set of "sensory symbols" we aim to achieve the identification of corresponding physical objects (or their attributes) to be performed close to the pre-attentive time.

Scatter-plot visualisation techniques are useful for analysing the correlations of variables on the axes as well as revealing patterns or abnormality in multidimensional data sets. However, Scatter-plot techniques have a drawback that they are not effective in showing a high number of dimensions where each plot in two-dimensional space can show a pair-wise of two variables on the X- and Y-axis. Star-plots are suitable for showing small data set with a low number of dimensions thank to the compactness in the visualisation. This paper proposes a hybrid technique that integrates Start-plots with Scatter-plots in the visualisation to enable the greater capability of scatter-plots in showing more information on each individual Star-plot. Our visualisation provides both overall views of mapping variables on multiple Scatter-plots whilst we also utilise Star-plots for showing the selected attributes on individual items for better comparison among and within variables. We also demonstrate the effectiveness of this hybrid method through case studies on various data sets.

Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data. A recent technique, called Linkable Scatterplots, provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction, linking and brushing. This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time, Multiple-Scatterplots who number of plots can be specified and shown, and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix. Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization, particularly in comparison with the Simultaneous-Scatterplots.​ While the time taken to complete tasks was longer in the Multiple-Scatterplots technique, compared with the simpler Sequential-Scatterplots, Multiple-Scatterplots is inherently more accurate. Moreover, the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study. Overall, results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.

Today there are abounding collected data in cases of various diseases in medical sciences. Physicians can access new findings about diseases and procedures in dealing with them by probing these data. Clinical data is a collection of large and complex datasets that commonly appear in multidimensional data formats. It has been recognized as a big challenge in modern data analysis tasks. Therefore, there is an urgent need to find new and effective techniques to deal with such huge datasets. This paper presents an application of a new visual data mining platform for visual analysis of the stroke data for predicting the levels of risk to those people who have the similar characteristics of the stroke patients. The visualization platform uses a hierarchical clustering algorithm to aggregate the data and map coherent groups of data-points to the same visual elements - curved 'super-polylines' that significantly reduces the visual complexity of the visualization. On the other hand, to enable users to interactively manipulate data items (super-polylines) in the parallel coordinates geometry through the mouse rollover and clicking, we created many 'virtual nodes' along the multi-axis of the visualization based on the hierarchical structure of the value range of selected data attributes. The experimental result shows that we can easily verify research hypothesis and reach to the conclusion of research questions through human-data & human-algorithm interactions by using this visual platform with a fully transparency manner of data processing.

Visual analytics plays a key role in bringing insights to audiences who are interested and dedicated in data exploration. In the area of relational data, many advanced visualization tools and frameworks are proposed in order to dealing with such data features. However, the majority of those have not greatly considered the whole process from data-model mining to query utilizing on dimensions and data values, which might cause interruption to exploration activities. This paper presents a new interactive exploration framework for relational data analysis through automatic interconnection of data models, data dimensions and data values. The basic idea is to construct a relative and switchable chain of those context representations by integrating our previous techniques on node-link, parallel coordinate and scatterplot graphics. This approach enables users to flexibly make relative queries on desired contexts at any stage of exploration for deep data understanding. The result from a typical case study for the framework demonstration indicates that our approach is able to handle the addressed challenge.

We propose a novel calculation method of personality based on the Chinese physiognomy. The proposed solution combines the ancient and the modem physiognomy to summarize the corresponding relation between the personality and facial feature and model the baseline to shape the face feature. We compute histogram of image by searching for the threshold values to create a binary image in an adaptive way. The two-pass connected component method indicates the feature region. We encode the binary image to remove the noise point, so that the new connected image can provide a better result. The method was tested on ORL face database.

Summary In this paper, we extended our density model to BigData analysis and visualization. BigData, which contains images, videos, texts, audio files and other forms of data collected from multiple datasets, is difficult to process and visualize using traditional database management and visualization tools. The challenges are in representing multiple datasets and illustrating and visualizing data patterns to meet business, government and organization needs. We have established the 5Ws density model which uses the 5Ws dimensions for BigData analysis and visualization. The 5Ws dimensions are what the data contain, why the data were transferred, where the data came from, when the data occurred, who received the data and how the data were transferred. According to the network log dataset, an example of BigData, each data incident can be classified into these 5Ws dimensions. The network log dataset ISCX2012 is tested throughout our model. This new model not only classifies network attributes and patterns but also establishes density patterns that provide more analytical features for BigData analysis and visualization. The experimental result shows that this new model with clustered visualization can be efficiently used for BigData analysis and network intrusion detection. Concurrency and Computation: Practice and Experience, 2014.© 2014 Wiley Periodicals, Inc.

Clustered Graph Visualization, such as DA-TU [2], has been successfully applied in the field of large scale relational data visualization for data analytics. However, it was very inefficient in interactions by using WIMP based HCI in DA-TU, in which a Control Panel was required to swap among eight different modes of operation. In this paper, we attempt to use a new multi-touch enabled NUI method for supporting interactions in DA-TU. We aim to increase the efficiency of HCI process in DA-TU. To achieve this, we first transplanted DA-TU from WIMP based PC to NUI based iPad. We then designed a set of new multi-finger gesture vocabularies to support DA-TU navigations in tablet environment. Adopting such set of rich HCI vocabularies achieves better efficiency and user experience in data exploration. We have conducted usability studies that demonstrated our hypothesis.

Feature analysis is always beneficial to the detection of anonymous criminals in digital forensics, including people and activities, where vast amount of features extracted from databases are involved. Not all features extracted are continuous or different, some of them are discrete or have the same value with others. We discovered that using visual analytics to select features for forensic investigations is not only improve the analysis time of selection, but can also deeply and obviously display the slight changes of features and criminals and also the relationship between features and criminals in order to find the target with significant difference with others, and also predict the more active features to be used in the future. Experiments show that visual feature analysis can help to catch the desire results quickly and clearly.

Geoscience observation data refers to the datasets consisting of time series of multiple parameters generated from the sensors at fixed locations. Although a few works have attempted to visualize features of these data, none of them views these data as a specific type and attempts to show the overview in all the space, time and attribute aspects. It is important for domain experts to select interested subsets from huge amounts of observation data according to the high level patterns shown in the overview. We present a novel approach to visualizing geoscience observation data in a compact radial view. Our solution consists of three visual elements. A map showing the spatial aspect is in the center of the visualization, while temporal and attribute aspects are seamlessly combined with the spatial information. Our approach is equipped with interactive mechanisms for highlighting the selected features, adjusting the display range, as well as interactively generating a fisheye view. We demonstrate the effectiveness and usability of our approach with a usability experiment. Eye tracking records and user feedbacks obtained in the experiment prove the effectiveness of our approach.

Clustered graph drawing is widely considered as a good method to overcome the scalability problem when visualizing large (or huge) graphs. Force-directed algorithm is a popular approach for laying graphs yet small to medium size datasets due to its slow convergence time. This paper proposes a new method which combines clustering and a force-directed algorithm, to reduce the computational complexity and time. It works by dividing a Long Convergence: LC into two Short Convergences: SC1, SC2, where SC1+SC2 <; LC. We also apply our work on weighted graphs. Our experiments show that the new method improves the aesthetics in graph visualization by providing clearer views for connectivity and edge weights.

This paper extends a previous work on node link tree visualization and interaction by providing visual clues on hidden structures. We adopt the effectiveness of DOI Tree, a multi-focal tree layout algorithm, for exploring large hierarchical structures. The advantages of visualization are its most familiar mapping for users, its capability on providing multiple focused nodes, and its dynamic rescaling of substructures to fit the available space. By providing various methods of topological previews of substructure including simple icon view, tree view and tree map view, we provide better understanding the topology of hidden branches.

We introduce a new Space-Filling Multidimensional Data Visualization (SFMDVis) that can be used to facilitate the viewing, interaction and analysis of the multidimensional data with a fully utilized display space. The existing multidimensional visualizations typically create visual clutter and over-plotting that make it difficult for interaction with data items directly. Our new space filling technique uses horizontal lines to represent multidimensional data items. Each line is logically divided into segments based on color mapping in order to denote the data item with its value. The proposed visualization is space efficient and also avoids the visual clutter and over-plotting problems as we have often observed in other visualizations. In addition, we allow user to interact directly with data on the display which is more intuitive and efficient than other means.

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.

This paper presents a new interactive scatter plot visualization for multi-dimensional data analysis. We apply RST to reduce the visual complexity through dimensionality reduction. We use an innovative point-to-region mouse click concept to enable direct interactions with scatter points that are theoretically impossible. To show the decision trend we use a virtual Z dimension to display a set of linear flows showing approximation of the decision trend. We have conducted a case study to demonstrate the effectiveness and usefulness of our new technique for identifying the impact sources of wine quality through the visual analytics of a wine dataset consisting of 12 attributes with 4898 samples.

Space-filling visualization techniques have proved their capability in visualizing large hierarchical structured data. However, most existing techniques restrict their partitioning process in vertical and horizontal direction only, which cause problem with identifying hierarchical structures. According to Gestalt research, limiting tree map visualisation to rectangles blocks the utilisation of human capability on object recognition, due to the same fixed size (90 degrees) of all the angles of the shapes in the tree visualisation. However, this assertion was only supported by theory and not rooted in empirical perception data. We conducted a series of controlled experiments to investigate the effect of shape variation of data elements and container in visual data analysis process. We first studied how shape variation affects user's perception in the visual data analysis process. We compared combined treemap with traditional rectangular treemaps, slice & dice treemaps and squarifed treemaps. Finally, we demonstrated the effect of the new approach which combines rectangular and non-rectangular treemaps and validate the method based on the empirical results.

Visualization and interaction of multidimensional data always requires optimized solutions to integrate the display, exploration and analytical reasoning of data into one visual pipeline for human-centered data analysis and interpretation. Parallel coordinate, as one of the popular multidimensional data visualization techniques, is suffered from the visual clutter problem. Though changing the ordering of axis is a straightforward way to address it, optimizing the order of axis is a NP-complete problem. In this paper, we propose a new axes re-ordering method in parallel coordinates visualization: a similarity-based method, which is based on the combination of Nonlinear Correlation Coefficient (NCC) and Singular Value Decomposition (SVD) algorithms. By using this approach, the first remarkable axe can be selected based on mathematics theory and all axis are re-ordered in line with the degree of similarities among them. Meanwhile, we would also propose a measurement of contribution rate of each dimension to reveal the property hidden in the dataset. At last, case studies demonstrate the rationale and effectiveness of our approaches: NCC reordering method can enlarge the mean crossing angles and reduce the amount of polylines between the neighboring axes. It can reduce the computational complexity greatly in comparison with other re-ordering methods.

Modern visual analytics tools provide mechanism for users to gain unknown knowledge through effective visual interactions for user to quickly understand the progress of algorithms and adjust the input parameters on intermediate visualizations that towards the production of most satisfied outcome. This requires the quick production of a sequence of graph visualizations. However, the traditional force-directed graph drawing algorithms are very slow to reach an equilibrium configuration of forces. They usually spend tens of seconds producing the layout of a graph converge. Thus, they do not satisfy the requirement of rapid drawing of graphs. This paper proposes a fast convergence method for drawing force-directed graphs. We essentially pre-calculate the geometrical position of all vertices before applying a force-directed layout algorithm to reach the energy minimization of the graph layout. The experimental results have shown that this approach could not only reduce the convergence time but also the number of edge crossings that approves the quality of layout significantly.

Space-filling visualization techniques have proved their capability in visualizing large hierarchical structured data. However, most existing techniques restrict their partitioning process in vertical and horizontal direction only, which cause problem with identifying hierarchical structures. This paper presents a new space-filling method named Angular Treemaps that relax the constraint of the rectangular subdivision. The approach of Angular Treemaps utilizes divide and conquer paradigm to visualize and emphasize large hierarchical structures within a compact and limited display area with better interpretability. Angular Treemaps generate various layouts to highlight hierarchical sub-structure based on user's preferences or system recommendations. It offers flexibility to be adopted into a wider range of applications, regarding different enclosing shapes. Preliminary usability results suggest user's performance by using this technique is improved in locating and identifying categorized analysis tasks.

Visualization of uncertainty in datasets is a new field of research, which aims to represent incomplete data for analysis in real scenarios. In many cases, datasets, especially multi-dimensional datasets, often contain either errors or uncertain values. To address this challenge, we may treat these uncertainties as scalar values like probability. For visual representation in parallel coordinates, we draw a small "circle" to temporarily define a dummy vertex for an uncertain value of a data item, at the crossing point between polylines and the axis of certain dimension. Furthermore, these temporary positions of uncertainty could be permuted to achieve visual effectiveness. This feature provides a great opportunity by optimizing the order of uncertain values to tackle another important challenge in information visualization: clutter reduction. Visual clutter always obscures the visualizing structure even in small datasets. In this paper, we apply Sugiyama's layered directed graph drawing algorithm into parallel coordinates visualization to minimize the number of edge crossing among polylines, which has significantly improved the readability of visual structure. Experiments in case studies have shown the effectiveness of our new methods for clutter reduction in parallel coordinates visualization. These experiments also imply that besides visual clutter, the number of uncertain values and the type of multi-dimensional data are important attributes that affect visualization performance in this field.

Aesthetics are often used to judge how good a graph drawing is in terms of specific drawing rules. A direct measurement of overall quality is missing. In this paper, we propose to evaluate quality based on aggregation of individual aesthetics. This measure can be used by visualization designers to quickly compare the quality of drawings at hand at the design stage and make decisions accordingly. We present a user study that validates this measure. The implications of the proposed measure for future research are discussed.

This paper describes TabuVis, a light weight visual analytics system that provides a flexible, customizable and effective visualization for multidimensional data. A key aspect of visual analytics of data with multiple attributes is the quality and appropriateness of the analytical platforms whose presentation can be adjusted via domain experts. A commercial-free and comprehensive prototype was developed that used scatter-plot approach to support the visual analytics process. Our system consists of multiple components enabling the complete analysis process, including data processing, automatic marks, interactive visualization and control. We demonstrate the effectiveness of TabuVis with two case studies using medical and Oscars data sets.

Networking attacks embedded in spam emails are increasingly becoming numerous and sophisticated in nature. Hence this has given a growing need for spam email analysis to identify these attacks. The use of these intrusion detection systems has given rise to other two issues, 1) the presentation and understanding of large amounts of spam emails, 2) the user-assisted input and quantified adjustment during the analysis process. In this paper we introduce a new analytical model that uses two coefficient vectors: 'density' and 'weight'for the analysis of spam email viruses and attacks. We then use a visual clustering method to classify and display the spam emails. The visualization allows users to interactively select and scale down the scope of views for better understanding of different types of the spam email attacks. The experiment shows that this new model with the clustering visualization can be effectively used for network security analysis.

We present a navigatable multi-dimensional visualization of data named coaxial interactive visualization which is based on the coaxial axes. In this visualization, the dimensions of data in n-dimensional space are mapped to n concentric circular axes drawn in a planar space with polar coordinates. A data item in n-dimensional space is represented as a poly-curve with n vertices across all circular axes. Via the spatial distortion in polar system, the view of multi-dimensional data is interactively adjusted to satisfy viewer's interest. The viewer, thus, can effectively navigate the underlying visual pattern to reach the great detail for uncovering trends and patterns in the visualization. One novelties of this work is performing focus+context viewing in polar coordinate system.

When visualizing graphs into node-link diagrams, angular resolution is often used as one of the aesthetic criteria measuring the diagram quality in terms of human comprehension. However, angular resolution has not been empirically validated for its relevance to humans. In addition, although many force-directed algorithms have been proposed for automatic graph drawing, performance evaluation of these algorithms has not been conclusive due to the lack of proper methods. To shorten these gaps, this paper 1) validates the aesthetic based on human experimental data and identifies the best of angular resolution measures used in the literature; 2) introduces a force-directed algorithm, forceAR, for improving angular resolution; 3) proposes a new framework for more reliable and thorough evaluation of force-directed algorithms. Finally as a case study, our forceAR algorithm is evaluated using this framework.

Highlighting was the basic viewing control mechanism in computer graphics and visualization to guide users' attention in reading diagrams, images, graphs and digital texts. As the rapid growth of theory and practice in information visualization, highlighting has extended its role that acts as not only a viewing control, but also an interaction control and a graphic recommendation mechanism in knowledge visualization and visual analytics. In this work, we attempt to give a formal summarization and classification of the existing highlighting methods and techniques that can be applied in Information Visualization, Visual Analytics and Knowledge Visualization. We propose a new three-layer model of highlighting. We discuss the responsibilities of each layer in the different stage of the visual information processing.

Wavelet Analysis method is considered as one of the most efficient methods for detecting DDoS attacks. However, during the peak data communication hours with a large amount of data transactions, this method is required to collect too many samples that will greatly increase the computational complexity. Therefore, the real-time response time as well as the accuracy of attack detection becomes very low. To address the above problem, we propose a new DDoS detection method called Modified Wavelet Analysis method which is based on the existing Isomap algorithm and wavelet analysis. In the paper, we present our new model and algorithm for detecting DDoS attacks and demonstrate the reasons of why we enlarge the Hurst's value of the self-similarity in our new approach. Finally we present an experimental evaluation to demonstrate that the proposed method is more efficient than the other traditional methods based on wavelet analysis.

Recent research has indicated that human graph reading performance can be affected by the size of crossing angle. Crossing angle is closely related to another aesthetic criterion: number of edge crossings. Although crossing number has been previously identified as the most important aesthetic, its relative impact on performance of human graph reading is unknown, compared to crossing angle. In this paper, we present an exploratory user study investigating the relative importance between crossing number and crossing angle. This study also aims to further examine the effects of crossing number and crossing angle not only on task performance measured as response time and accuracy, but also on cognitive load and visualization efficiency. The experimental results reinforce the previous findings of the effects of the two aesthetics on graph comprehension. The study demonstrates that on average these two closely related aesthetics together explain 33% of variance in the four usability measures: time, accuracy, mental effort and visualization efficiency, with about 38% of the explained variance being attributed to the crossing angle.

With an astonishing amount of data generated for processing on a daily basic, it is essential to provide an effective methodology for understanding, reasoning and supporting decision making of large information spaces. This paper presents a new concept that provides an intelligent and interactive visualization in supporting large scale analysis. This aims to provide a much greater flexibility and control for the users to interactively customize the visualizations according to their preferences. A simple prototype is also presented to demonstrate the concept on hierarchical structures.

We present XKMis, a system for keyword search in XML documents. Unlike previous work, our method is not based on the lowest common ancestor (LCA) or its variant, rather we divide the nodes into meaningful and self-containing information segments, called minimal information segments (MISs), and return MIS-subtrees which consist of MISs that are logically connected by the keywords. The MIS-subtrees are closer to what the user wants. The MIS-subtrees enable us to use the region code of XML trees to develop an algorithm for the search which is more efficient especially for large XML trees. We report our experiment results, which verify the better effectiveness and efficiency of our system.

Bar chart is a very common and simple graph that is mainly used to visualize simple x, y plots of data for numerical comparisons by partitioning the categorical data values into bars and typically limited to operate on highly aggregated dataset. In todaypsilas growing complexity of business data with multi dimensional attributes using bar chart itself is not sufficient to deal with the representation of such business dataset and it also not utilizes the screen space efficiently.Nevertheless, bar chart is still useful because of its shape create strong visual attention to users at first glance than other visualization techniques. In this article, we present a treemap bar chart + tablelens interaction technique that combines the treemap and bar chart visualizations with a tablelens based zooming technique that allows users to view the detail of a particular bar when the density of bars increases. In our approach, the capability of the original bar chart and treemaps for representing complex business data is enhanced and the utilization of display space is also optimized.

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.

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.

This paper presents a technique for navigating large clustered graphs with triple-layer display. Although our navigation and interaction technique can be applied independently to any graph layout algorithms, in our implementation, our navigation algorithm is cooperated with a clustered graph layout algorithm. In short, we provide users with the display of three levels of contextual information for navigation. They are the display of full-context, the display of the current-context view and the display of the focused view. Our technique not only allows users to navigate hierarchically up and down through the entire clustered graph, but also provides a way to navigate multiple clusters concurrently. We also use animation to preserve the users' mental maps during the interaction.

This paper presents a framework supporting collaborative efforts between the designer and developer of software-intensive systems. The framework realizes a two-layered meta-tool concept: a powerful specification language in the form of a grammar for the designer at the upper layer; and a generative mechanism for generating domain-specific design languages at the lower layer. The paper introduces the spatial graph grammar formalism as the specification language and the generation mechanism. We also describe successful and potential application domains of the two-layered framework

This paper presents a new multi-level focus + context technique for visualizing and manipulating hierarchies which contain large number of leaves. Our technique uses an ordinary 2D hyperbolic tree browser to display the major structure of hierarchies while a 1D distortion-based fisheye menu is also used to assist the display of those sub-hierarchies containing large number of leaf nodes. This significantly solves the problem of inefficient use of the display space when a user navigates, using a hyperbolic tree browser, through a portion of the hierarchy containing a large number of leaves.

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.

This paper reports an empirical study that explores the problem of finding a highly-efficient, user-friendly interface design method on small display devices. We compared three models using our PDA interface simulator: presentation optimization method, semantic conversion method, and zooming method. A controlled experiment has been carried out to identify the pros and cons of each method. The results show that of the three interface methods, the zooming method is slightly better than the semantic conversion method, while they both outperform the optimizing presentation method.

We present a fast focus+context viewing technique for the navigation of classical hierarchical layouts. In these layout techniques, only one dimension is considerably important to the visualization. Based on this property, we consider only one dimension of distortion of views for browsing the hierarchical structures. We firstly introduce briefly some information visualization techniques. We then discuss the features, advantages and limitations of layout and navigation of current classical hierarchical layouts. We finally introduce our new focus+context technique that is simple and fast for the navigation and exploration of the classical hierarchical layouts.

The current generation of web browsers, such as Netscape and Microsoft Explorer, excites people because of the structure of hyper-documents that provides users with an effective and convenient way to move in cyberspace. This is done through clicking on a series of hyperlinks embedded in hyper-documents. However, this arrangement does not give users a visual "map" to guide them in their web journey. It does not provide sense of "space" while the user is exploring the (cyber) space; instead it only gives a series of lists. This is mainly because of the difficulty of constructing such a huge, complex, and dynamic map with an (virtually) unlimited number of nodes and edges. Because the whole web is partially unknown, it is difficult for mapmaker to draw the map. Further, the computation of the overall geometrical structure of such a huge map (with millions or perhaps billions of nodes) is impossible as most visualization algorithms have super-linear time complexity and only scale to a few hundred nodes. Most existing visualization techniques and current research interests emphasize "site mapping". That is, they try to find an effective way of constructing a structured geometrical map for one web site (a local map). This can only guide the user through a very limited region of cyberspace, and does not help users in their overall journey through the cyberspace. In this paper, we look at the whole journey through the cyberspace as one graph; a huge and partially unknown graph. We use our new on-line exploratory visualization technique [7] to maintain and display a subset of this huge graph incrementally. The Visual Web Browser consists of three major components: a fast accessible linkage server, a filtering mechanism, and an on-line exploratory visualization. The framework of these components provides the user with a dynamic graphical map for guiding the web journey.