Can supervise: YES
Fan, X, Li, C, Yuan, X, Dong, X & Liang, J 2019, 'An interactive visual analytics approach for network anomaly detection through smart labeling', Journal of Visualization, vol. 22, no. 5, pp. 955-971.View/Download from: Publisher's site
© 2019, The Visualization Society of Japan. Abstract: Network anomaly detection is an important means for safeguarding network security. On account of the difficulties encountered in traditional automatic detection methods such as lack of labeled data, expensive retraining costs for new data and non-explanation, we propose a novel smart labeling method, which combines active learning and visual interaction, to detect network anomalies through the iterative labeling process of the users. The algorithms and the visual interfaces are tightly integrated. The network behavior patterns are first learned by using the self-organizing incremental neural network. Then, the model uses a Fuzzy c-means-based algorithm to do classification on the basis of user feedback. After that, the visual interfaces are updated to present the improved results of the model, which can help users to choose meaningful candidates, judge anomalies and understand the model results. The experiments show that compared to labeling without our visualizations, our method can achieve a high accuracy rate of anomaly detection with fewer labeled samples. Graphic abstract: [Figure not available: see fulltext.].
Chen, S, Chen, S, Wang, Z, Liang, J, Wu, Y & Yuan, X 2018, 'D-MAP+: Interactive visual analysis and exploration of ego-centric and event-centric information diffusion patterns in social media', ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 1.View/Download from: Publisher's site
© 2018 Association for Computing Machinery. Information diffusion analysis is important in social media. In this work, we present a coherent ego-centric and event-centric model to investigate diffusion patterns and user behaviors. Applying the model, we propose Diffusion Map+ (D-Maps+), a novel visualization method to support exploration and analysis of user behaviors and diffusion patterns through a map metaphor. For ego-centric analysis, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected. Event-centric analysis focuses on multiple central users discussing a specific event, with all the people participating and reposting messages about it. Social media users are mapped to a hexagonal grid based on their behavior similarities and in the chronological order of repostings. With the additional interactions and linkings, D-Map+ is capable of providing visual profiling of influential users, describing their social behaviors and analyzing the evolution of significant events in social media. A comprehensive visual analysis system is developed to support interactive exploration with D-Map+. We evaluate our work with real-world social media data and find interesting patterns among users and events. We also perform evaluations including user studies and expert feedback to certify the capabilities of our method.
Chen, S, Wang, Z, Yuan, X & Liang, J 2018, 'Uncertainty-aware Visual Analytics for Exploring Human Behaviors from Heterogeneous Spatial Temporal Data', Journal of Visual Languages and Computing, vol. 48, pp. 187-198.
Chen, Y, Dong, Y, Sun, Y & Liang, J 2018, 'A Multi-comparable visual analytic approach for complex hierarchical data', Journal of Visual Languages and Computing, vol. 47, pp. 19-30.View/Download from: Publisher's site
© 2018 Elsevier Ltd Maximum residue limit (MRL) standard which specifies the highest level of every pesticide residue in different agricultural products plays a critical role in food safety. However, such standards which related to the characteristics of pesticides and the classification of agricultural products which organized into a hierarchical structure are complex and vary widely across different regions or countries. So it is a big challenge to compare multi-regional MRL standard data comprehensively. In this paper, we present a multi-comparable visual analytic approach for complex hierarchical data and a visual analytics system (McVA) to support multiple comparison and evaluation of MRL standard. With a cooperative multi-view visual design, our proposed approach links the hierarchies of MRL datasets and provides the capacity for comparison at different levels and dimensions. We also introduce a metric model for evaluating the completeness and strictness of MRL standards quantitatively. The case study of real problems and the positive feedback from domain experts demonstrate the effectiveness of this approach.
Lu, M, Lai, C, Ye, T, Liang, J & Yuan, X 2017, 'Visual Analysis of Multiple Route Choices Based on General GPS Trajectories', IEEE Transactions on Big Data, vol. 3, no. 2, pp. 234-247.View/Download from: Publisher's site
There are often multiple routes between regions. Drivers choose different routes with different considerations. Such considerations, have always been a point of interest in the transportation area. Studies of route choice behaviour are usually based on small range experiments with a group of volunteers. However, the experiment data is quite limited in its spatial and temporal scale as well as the practical reliability. In this work, we explore the possibility of studying route choice behaviour based on general trajectory dataset, which is more realistic in a wider scale. We develop a visual analytic system to help users handle the large-scale trajectory data, compare different route choices, and explore the underlying reasons. Specifically, the system consists of: 1. the interactive trajectory filtering which supports graphical trajectory query; 2. the spatial visualization which gives an overview of all feasible routes extracted from filtered trajectories; 3. the factor visual analytics which provides the exploration and hypothesis construction of different factors' impact on route choice behaviour, and the verification with an integrated route choice model. Applying to real taxi GPS dataset, we report the system's performance and demonstrate its effectiveness with three cases.
Liu, Y, Huang, M, Liang, J & Huang, W 2017, 'A physiognomy based method for facial feature extraction and recognition', Journal of Visual Languages and Computing, vol. 43, pp. 103-109.View/Download from: Publisher's site
This paper proposes a novel calculation method of personality based on Chinese physiognomy. The proposed solution combines ancient and modern physiognomy to understand the relationship between personality and facial features and to model a baseline to shape facial features. We compute a histogram of image by searching for threshold values to create a binary image in an adaptive way. The two-pass connected component method indicates the feature's region. We encode the binary image to remove the noise point, so that the new connected image can provide a better result. According to our analysis of contours, we can locate facial features and classify them by means of a calculation method. The number of clusters is decided by a model and the facial feature contours are classified by using the k-means method. The validity of our method was tested on a face database and demonstrated by a comparative experiment.
Chen, S, Yuan, X, Wang, Z, Guo, C, Liang, J, Wang, Z, Zhang, XL & Zhang, J 2016, 'Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data', IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 270-279.View/Download from: Publisher's site
Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.
Chen, Y, Zhen, Y, Hu, H, Liang, J & Ma, K 2016, 'Visualization Technique for Multi-Attribute in Hierarchical Structure', Journal of Software, vol. 27, no. 5, pp. 1091-1102.View/Download from: Publisher's site
Nowadays, there is increasing need to analyze the complex data with both hierarchical and multi-attributes in many fields such as food safety, stock market, and network security. The visual analytics appeared in recent years provides a good solution to analyze this kind of data. So far, many visualization methods for multi-dimensional data and hierarchical data, the typical data objects in the field of information visualization, have been presented to solve data analyzing problems effectively. However, the existing solutions can't meet requirements of visual analysis for the complex data with both multi-dimensional and hierarchical attributes. This paper presents a technology named Multi-Coordinate in Treemap (MCT), which combines rectangle treemap and multi-dimensional coordinates techniques. MCT uses treemap created with Squarified and Strip layout algorithm to represent hierarchical structure, uses four edges of treemap's rectangular node as the attribute axis, and through mapping property values to attribute axis, connecting attribute points and fitting curve, to achieve visualization of multi-attribute in hierarchical structure. This work applies MCT technology to visualize pesticide residue detection data and implements the visualization for detecting excessive pesticide residue in fruits and vegetables distributed in each provinces of China. This technology provides an efficient analysis tool for field experts. MCT can also be applied in other fields which require visual analysis of complex data with both hierarchical and multi-attribute.
Liu, Y, Li, L, Li, X, Wang, Y, Ren, X & Liang, J 2016, 'Antibacterial modification of microcrystalline cellulose by grafting copolymerization', BioResources, vol. 11, no. 1, pp. 519-529.View/Download from: Publisher's site
Microcrystalline cellulose (MCC) has the advantage of a high specific surface area as compared to that of conventional cellulose fibers. In this study the monomer methacrylamide (MAM) was used to treat MCC by grafting copolymerization. SEM, FTIR, and solid 13C NMR were used to characterize the morphology and composition of MAM-g-MCC. After the chlorination of MAM-g-MCC with 10% sodium hypochlorite solution, the grafted MCC exhibited antibacterial activity as a result of the formation of N-Cl bonds. The thermal stability, antibacterial ability, and storage stability of chlorinated MAM-g-MCC were also studied. The results showed that the chlorinated MAM-g-MCC had excellent storage stability and could inactivate all S. aureus and E. coli O157:H7 within 10 min.
Liu, Y, Xiao, C, Li, X, Li, L, Ren, X, Liang, J & Huang, TS 2016, 'Antibacterial efficacy of functionalized silk fabrics by radical copolymerization with quaternary ammonium salts', Journal of Applied Polymer Science, vol. 133, no. 21.View/Download from: Publisher's site
© 2016 Wiley Periodicals, Inc. Quaternary ammonium salts Quats-C8, Quats-C12, and Quats-C18 with different alkyl chain lengths have been successfully synthesized, and used for modifying silk fabrics. The optimum reaction conditions of initiator concentration, curing temperature, curing time, and monomer concentration have been studied. The modified fabrics of silk-g-C8, silk-g-C12, and silk-g-C18 were characterized by FTIR spectra. Antibacterial test showed that the modified silk fabrics possessed potent antibacterial activity against both Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli. The carbon number in the alkyl chain of monomers Quats-C8, Quats-C12, and Quats-C18 can affect the antibacterial efficacy. With longer alkyl chain, the antibacterial efficacy increased. The quaternary ammonium salts-modified silk fabrics have small change on the tensile strength and wrinkle recovery angle, and have shown potential practical application.
Lu, M, Liang, J, Wang, Z & Yuan, X 2016, 'Exploring OD patterns of interested region based on taxi trajectories', Journal of Visualization, vol. 19, no. 4, pp. 811-821.View/Download from: Publisher's site
Traffics of different regions in a city have different Origin-Destination (OD) patterns, which potentially reveal the surrounding traffic context and social functions. In this work, we present a visual analysis system to explore OD patterns of interested regions based on taxi trajectories. The system integrates interactive trajectory filtering with visual OD patterns exploration. Trajectories related to interested region are selected by a suite of graphical filtering tools, from which OD clusters are detected automatically. OD traffic patterns can be explored at two levels: overview of OD and detailed exploration on dynamic OD patterns, including information of dynamic traffic volume and travel time. By testing on real taxi trajectory data sets, we demonstrate the effectiveness of our system.
Chen, Y, Zhang, X, Feng, Y, Liang, J & Chen, H 2015, 'Sunburst with ordered nodes based on hierarchical clustering: a visual analyzing method for associated hierarchical pesticide residue data', Journal of Visualization, vol. 18, no. 2, pp. 237-254.View/Download from: Publisher's site
According to the characteristics of pesticide residue data and analyzing requirements in food safety fields, we presented a visual analyzing method for associated hierarchical data, called sunburst with ordered nodes based on hierarchical clustering (SONHC). SONHC arranged the leaf nodes in sunburst in order using hierarchical clustering algorithm, put the associated dataset as a node in center of the sunburst, and connected it with the associated leaf nodes in sunburst using colored lines. So, it can present not only two hierarchical structures but also the relationships between them. Based on SONHC and some interaction techniques (clicking, contraction and expansion, etc) we developed an associated visual analyzing system (AVAS) for pesticide residues detection results data, which can help users to inspect the hierarchical structure of pesticide and agricultural products and to explore the associations between pesticides and agricultural products, and associations between different pesticides. The results of user experience test showed that SONHC algorithm overperforms than SA and SR algorithm in ULE and ULE's variance. AVAS system is effective in helping users to analyze the pesticide residues data. Furthermore, SONHC algorithm can also be adopted to analyze associated hierarchical data in other fields, such as finance, insurance and e-commerce.
Huang, M, Liang, J, Nguyen, Q & Simoff, S 2014, 'Angular Treemaps' in Banissi, E, Marchese, FT, Forsell, C & Johansson, J (eds), Information Visualisation Techniques, Usability and Evaluation, Cambridge Scholars Publishing, UK, pp. 83-107.
Space-filling visualization techniques have proved their capability in
visualizing large hierarchically structured data. However, most existing
techniques restrict their partitioning process in vertical and horizontal
directions only, which causes problems with identifying hierarchical
structures. This chapter presents a new space-filling method named
Angular Treemaps that relaxes the constraint of the rectangular
subdivision. The approach of Angular Treemaps uses the 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 substructure
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 is improved by using this technique in locating and
identifying categorized analysis tasks.
Huang, ML, YUE, Z, Nguyen, QV, Liang, J & Luo, Z 2019, 'Stroke Data Analysis through a HVN Visual Data Mining Platform', Proc. of 23rd International Conference on Information Visualization, International Conference on Information Visualization, IEEE CSP, Adelaide, Australia.View/Download from: Publisher's site
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.
Chen, S, Chen, S, Lin, L, Yuan, X, Liang, J & Zhang, X 2017, 'E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media', Proceedings of IEEE Symposium on Visual Analytics Science and Technology (VAST'17), IEEE Conference on Visual Analytics Science and Technology, IEEE, Phoenix, Arizona, USA, pp. 2720-2729.
Significant events are often discussed and spread through social
media, involving many people. Reposting activities and opinions
expressed in social media offer good opportunities to understand
the evolution of events. However, the dynamics of reposting activities
and the diversity of user comments pose challenges to understand
event-related social media data. We propose E-Map, a visual
analytics approach that uses map-like visualization tools to help
multi-faceted analysis of social media data on a significant event
and in-depth understanding of the development of the event. E-Map
transforms extracted keywords, messages, and reposting behaviors
into map features such as cities, towns, and rivers to build a structured
and semantic space for users to explore. It also visualizes
complex posting and reposting behaviors as simple trajectories and
connections that can be easily followed. By supporting multi-level
spatial temporal exploration, E-Map helps to reveal the patterns
of event development and key players in an event, disclosing the
ways they shape and affect the development of the event. Two
cases analysing real-world events confirm the capacities of E-Map
in facilitating the analysis of event evolution with social media data
Lu, M, Liang, J, Zhang, Y, Li, G, Chen, S, Li, Z & Yuan, X 2017, 'Interaction+: Interaction Enhancement for Web-based Visualizations', Proceedings of IEEE Pacific Visualization Symposium (PacificVis 2017), IEEE Pacific Visualization Symposium, IEEE, Seoul, Korea, pp. 61-70.View/Download from: Publisher's site
In this work, we present Interaction+, a tool that enhances the interactive
capability of existing web-based visualizations. Different
from the toolkits for authoring interactions during the visualization
construction, Interaction+ takes existing visualizations as input, analyzes
the visual objects, and provides users with a suite of interactions
to facilitate the visual exploration, including selection, aggregation,
arrangement, comparison, filtering, and annotation. Without
accessing the underlying data or process how the visualization
is constructed, Interaction+ is application-independent and can be
Yi, C, Yu, D, Sun, Y & Liang, J 2017, 'McVA: A Multi-comparison Visual Analysis System for Maximum Residue Limit Standard in Food Safety', Proceedings of ChinaVis 2017, ChinaVis 2017, QingDao, China.
Chen, S, Chen, S, Wang, Z, Liang, J, Yuan, X, Cao, N & Wu, Y 2016, 'D-Map: Visual Analysis of Ego-centric information Diffusion patterns in social media', Visual Analytics Science and Technology (VAST), 2016 IEEE Conference on, IEEE Symposium on Visual Analytics Science and Technology, IEEE, Baltimore, MD, USA.View/Download from: Publisher's site
Popular social media platforms could rapidly propagate vital information over social networks among a significant number of people. In this work we present D-Map (Diffusion Map), a novel visualization method to support exploration and analysis of social behaviors during such information diffusion and propagation on typical social media through a map metaphor. In D-Map, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected and mapped to a hexagonal grid based on their behavior similarities and in chronological order of the repostings. With additional interaction and linking, D-Map is capable of providing visual portraits of the influential users and describing their social behaviors. A comprehensive visual analysis system is developed to support interactive exploration with D-Map. We evaluate our work with real world social media data and find interesting patterns among users. Key players, important information diffusion paths, and interactions among social communities can be identified.
Chen, S, Wang, Z, Liang, J & Yuan, X 2016, 'Uncertainty-aware Visual Analytics for Exploring Human Behaviors from Heterogeneous Spatial Temporal Data', Proceedings of the Third Conference of China Visualization and Visual Analytics (ChinaVis'16), the Third Conference of China Visualization and Visual Analytics (ChinaVis'16), Changsha, China.
When analyzing human behaviors, we need to construct the human
behaviors from multiple sources of data, e.g. trajectory data, transaction
data, identity data, etc. The problem we're facing is the data
conflicts, different resolution, missing and conflicting data, which
together lead to the uncertainty in the spatial temporal data. Such
uncertainty in data leads to difficulties even failure in the visual
analytics task for analyzing people behavior, pattern and outliers.
However, traditional automatic methods can not solve the problems
in such complex scenario, where the uncertain and conflicting patterns
are not well-defined. To solve the problems, we proposed a
semi-automatic approach, for users to solve the conflicts and identify
the uncertainties. To be general, We summarized five types of
uncertainties and solutions to conduct the tasks of behavior analysis.
Combined with the uncertainty-aware methods, we proposed a
visual analytics system to analyze human behaviors, detect patterns
and find outliers. Case studies from the IEEE VAST Challenge
2014 dataset confirms the effectiveness of our approach.
Shu, Q, Guo, H, Liang, J, Che, L, Liu, J & Yuan, X 2016, 'EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Data', Pacific Visualization Symposium (PacificVis), 2016 IEEE, IEEE Symposium on Pacific Visualization (PacificVis), IEEE, Taipei, Taiwan.View/Download from: Publisher's site
This paper presents a novel visual analysis tool, EnsembleGraph, which aims at helping scientists understand spatiotemporal similarities across runs in time-varying ensemble simulation data. We abstract the input data into a graph, where each node represents a region with similar behaviors across runs and nodes in adjacent time frames are linked if their regions overlap spatially. The visualization of this graph, combined with multiple-linked views showing details, enables users to explore, select, and compare the extracted regions that have similar behaviors. The driving application of this paper is the study of regional emission influences over tropospheric ozone, based on the ensemble simulations conducted with different anthropogenic emission absences using MOZART-4. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations.
Ye, T, Hao, Y, Wang, Z, Lai, C, Chen, S, Li, Z, Liang, J & Yuan, X 2016, 'Behavior Analysis through Collaborative Visual Exploration on Trajectory Data', ChinaVis2016, ChinaVis2016, Changsha, China.
Liu, Y, Huang, M, Liang, J & Huang, W 2016, 'Facial Feature Extraction and Recognition for Traditional Chinese Physiognomy', Proceedings - 20th International Conference Information Visualisation, International Conference on Information Visualisation, IEEE Computer Society's Conference Publishing Services (CPS), Lisbon, Portugal, pp. 408-412.View/Download from: Publisher's site
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.
Che, L, Liang, J, Yuan, X, Shen, J, Xu, J & Li, Y 2015, 'Laplacian-based Dynamic Graph Visualization', Visualization Symposium (PacificVis), 2015 IEEE Pacific, IEEE Pacific Visualization Symposium (was APVIS), IEEE, Hangzhou, China.View/Download from: Publisher's site
Visualizing dynamic graphs are challenging due to the difficulty to preserving a coherent mental map of the changing graphs. In this paper, we propose a novel layout algorithm which is capable of maintaining the overall structure of a sequence graphs. Through Laplacian constrained distance embedding, our method works online and maintains the aesthetic of individual graphs and the shape similarity between adjacent graphs in the sequence. By preserving the shape of the same graph components across different time steps, our method can effectively help users track and gain insights into the graph changes. Two datasets are tested to demonstrate the effectiveness of our algorithm.
Lu, M, Lai, C, Ye, T, Liang, J & Yuan, X 2015, 'Visual analysis of route choice behaviour based on GPS trajectories', Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on, IEEE Symposium on Visual Analytics Science and Technology, IEEE, Chicago, IL, USA.View/Download from: Publisher's site
There are often multiple routes between regions. Many factors potentially affect driver's route choice, such as expected time cost, length etc. In this work, we present a visual analysis system to explore driver's route choice behaviour based on taxi GPS trajectory data. With interactive trajectory filtering, the system constructs feasible routes between regions of interest. Using a rank-based visualization, the attributes of multiple routes are explored and compared. Based on a statistical model, the system supports to verify trajectory-related factors' impact on route choice behaviour. The effectiveness of the system is demonstrated by applying to real trajectory dataset.
Lu, M, Lai, C, Ye, T, Liang, J & Yuan, X 2015, 'Visual Analysis of Route Choice Behaviour based on GPS Trajectories', 21th ACM SIGKDD Workshop on Urban Computing (UrbComp 2015), Sydney, Australia.
Lu, M, Wang, Z, Liang, J & Yuan, X 2015, 'OD-Wheel: Visual Design to Explore OD Patterns of a Central Region', Visualization Symposium (PacificVis), 2015 IEEE Pacific, IEEE Pacific Visualization Symposium (was APVIS), IEEE, Hangzhou, China, pp. 87-91.View/Download from: Publisher's site
Wang, Z, Yuan, X, Ye, T, Hao, Y, Chen, S, Liang, J, Li, Q, Wang, H & Wu, Y 2015, 'Visual data quality analysis for taxi GPS data', 2015 IEEE Conference on Visual Analytics Science and Technology, VAST 2015 - Proceedings, pp. 223-224.View/Download from: Publisher's site
© 2015 IEEE. We present a novel visual analysis method to systematically discover data quality problems in raw taxi GPS data. It combines semi-supervised active learning and interactive visual exploration. It helps analysts interactively discover unknown data quality problems, and automatically extract known problems. We report analysis results on Beijing taxi GPS data.
Ye, T, Hao, Y, Wang, Z, Lai, C, Chen, S, Li, Z, Liang, J & Yuan, X 2015, 'Behavior analysis through collaborative visual exploration on trajectory data', Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on, IEEE Symposium on Visual Analytics Science and Technology, IEEE, Chicago, IL, USA.View/Download from: Publisher's site
In VAST Challenge 2015, we proposed a collaborative visual exploration
system for behavior analysis over trajectory records. We
discuss technical details in this report, in order to deliberate how the
system supports multiple users to collaboratively analyze the same
data, assist in sharing their findings, and constructing an overall
picture of their insights.
Liang, J, Hua, J, Huang, M, Nguyen, Q & Simoff, SJ 2012, 'Rectangle orientation in area judgment task for treemap design', The 24th Australian Computer-Human Interaction Conference, OzCHI '12, Australian Computer Human Interaction Conference, ACM Press, Melbourne, VIC, Australia, pp. 349-352.View/Download from: Publisher's site
Prior works on treemaps have mainly focused on developing the new layouts. The existing treemaps generated from various algorithms require careful examination on design parameter. However, current research does not provide usability studies of treemap guidelines on effectiveness of design parameters. Hence, selecting the most effective parameter for certain type of task is primarily based on intuition preference of visualization designer. For example, in the existing research, there is insufficient guidance on orientation for treemap design yet. Therefore, the impact of orientation remains unclear in visual analysis tasks performance. The contribution of this paper is to assess the effect of orientation in visual data analysis process so that we will further investigate treemap design guidance
Liang, J, Nguyen, Q, Simoff, SJ & Huang, M 2013, 'Visualizing large trees with divide & conquer partition', Proceedings of the 6th International Symposium on Visual Information Communication and Interaction, International Symposium on Visual Information Communication and Interaction, ACM, Tianjin, China, pp. 79-87.View/Download from: Publisher's site
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.
Liang, J, Huang, M & Nguyen, Q 2012, 'Perceptual User Study for Combined Treemap', Proceedings of 11th International Conference on Machine Learning and Applications (volumn 1), ICMLA 2012, International Conference on Machine Learning and Applications, IEEE, Boca Raton, FL, USA, pp. 300-305.View/Download from: Publisher's site
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 users 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 nonrectangular treemaps and validate the method based on the empirical results.
Liang, J, Nguyen, Q, Simoff, SJ & Huang, M 2012, 'Angular Treemaps - A new Technique for Visualizing and Emphasizing Hierarchical Structured Data', 2012 16th International Conference on Information Visualisation (IV), International Conference on Information Visualisation, IEEE, Montpellier, France, pp. 74-80.View/Download from: Publisher's site
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 spacefilling 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 userâs preferences or system recommendations. It offers flexibility to be adopted into a wider range of applications, regarding different enclosing shapes. Preliminary usability results suggest userâs performance by using this technique is improved in locating and identifying categorized analysis tasks.
Lu, L, Huang, M, Chen, Y, Liang, J & Nguyen, Q 2012, 'Clutter Reduction in Multi-dimensional Visualization of Incomplete Data Using Sugiyama Algorithm', 2012 16th International Conference on Information Visualisation (IV), International Conference on Information Visualisation, IEEE, Montpellier, France, pp. 93-99.View/Download from: Publisher's site
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.
Liang, J & Huang, M 2010, 'Highlighting in Information Visualization: A Survey', 2010 14th International Conference on Information Visualisation (IV), International Conference on Information Visualisation, IEEE Computer Society, London, UK, pp. 79-85.View/Download from: Publisher's site
Highlighting was the basic viewing control mechanism in computer graphics and visualization to guide usersâ 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.
Huang, M, Liang, J & Nguyen, Q 2009, 'A Visualization Approach for Frauds Detection in Financial Market', 13th International Conference on Information Visualisation, IV 2009, International Conference on Information Visualisation, IEEE Computer Society, Barcelona, Spain, pp. 197-202.View/Download from: Publisher's site
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 todayï½s 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.
Huang, M, Nguyen, Q & Liang, J 2008, 'A Usability Study on the Use of Multi-Context Visualization', Computer Graphics, Imaging and Visualization - Modern Techniques and Applications, International Conference Computer Graphics, Imaging and Visualization, IEEE Computer Society, Penang, Malaysia, pp. 311-316.View/Download from: Publisher's site