publications

2023

1. 

   TVCG

   Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events

   Kam Kwai Wong, Xingbo Wang, Yong Wang, Jianben He, Rong Zhang, and 1 more author

   IEEE Transactions on Visualization and Computer Graphics, 2023

   Abstract arXiv DOI

   Delivering customer services through video communications has brought new opportunities to analyze customer satisfaction for quality management. However, due to the lack of reliable self-reported responses, service providers are troubled by the inadequate estimation of customer services and the tedious investigation into multimodal video recordings. We introduce Anchorage, a visual analytics system to evaluate customer satisfaction by summarizing multimodal behavioral features in customer service videos and revealing abnormal operations in the service process. We leverage the semantically meaningful operations to introduce structured event understanding into videos which help service providers quickly navigate to events of their interest. Anchorage supports a comprehensive evaluation of customer satisfaction from the service and operation levels and efficient analysis of customer behavioral dynamics via multifaceted visualization views. We extensively evaluate Anchorage through a case study and a carefully-designed user study. The results demonstrate its effectiveness and usability in assessing customer satisfaction using customer service videos. We found that introducing event contexts in assessing customer satisfaction can enhance its performance without compromising annotation precision. Our approach can be adapted in situations where unlabelled and unstructured videos are collected along with sequential records.

2. 

   IEEE VR

   Towards an Understanding of Distributed Asymmetric Collaborative Visualization on Problem-solving

   Wai Tong, Meng Xia, Kam Kwai Wong, Doug A Bowman, Ting-Chuen Pong, and 2 more authors

   IEEE Conference on Virtual Reality and 3D User Interfaces, 2023

   Abstract arXiv DOI Git

   This paper provided empirical knowledge of the user experience for using collaborative visualization in a distributed asymmetrical setting through controlled user studies. With the ability to access various computing devices, such as Virtual Reality (VR) head-mounted displays, scenarios emerge when collaborators have to or prefer to use different computing environments in different places. However, we still lack an understanding of using VR in an asymmetric setting for collaborative visualization. To get an initial understanding and better inform the designs for asymmetric systems, we first conducted a formative study with 12 pairs of participants. All participants collaborated in asymmetric (PC-VR) and symmetric settings (PC-PC and VR-VR). We then improved our asymmetric design based on the key findings and observations from the first study. Another ten pairs of participants collaborated with enhanced PC-VR and PC-PC conditions in a follow-up study. We found that a well-designed asymmetric collaboration system could be as effective as a symmetric system. Surprisingly, participants using PC perceived less mental demand and effort in the asymmetric setting (PC-VR) compared to the symmetric setting (PC-PC). We provided fine-grained discussions about the trade-offs between different collaboration settings.

3. 

   TVCG

   XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis

   Yingchaojie Feng, Xingbo Wang, Bo Pan, Kam Kwai Wong, Yi Ren, and 5 more authors

   IEEE Transactions on Visualization and Computer Graphics, 2023

   Abstract arXiv DOI

   Natural language interfaces (NLIs) enable users to flexibly specify analytical intentions in data visualization. However, diagnosing the visualization results without understanding the underlying generation process is challenging. Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries. We present XNLI, an explainable NLI system for visual data analysis. The system introduces a Provenance Generator to reveal the detailed process of visual transformations, a suite of interactive widgets to support error adjustments, and a Hint Generator to provide query revision hints based on the analysis of user queries and interactions. Two usage scenarios of XNLI and a user study verify the effectiveness and usability of the system. Results suggest that XNLI can significantly enhance task accuracy without interrupting the NLI-based analysis process.

2022

1. 

   IEEE VIS

   CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data

   Wei Zhang, Jason K. Wong, Xumeng Wang, Youcheng Gong, Rongchen Zhu, and 6 more authors

   IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS), 2022

   Abstract arXiv DOI Presentation

   In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.

2. 

   IEEE VIS

   DPVisCreator: Incorporating pattern constraints to privacy-preserving visualizations via differential privacy

   Jiehui Zhou, Xumeng Wang, Jason K. Wong, Huanliang Wang, Zhongwei Wang, and 6 more authors

   IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS), 2022

   Abstract arXiv DOI

   Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users’ preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model PriVis. A prototype system, DPVisCreator, is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.

2021

1. 

   TVCG

   Explaining with Examples Lessons Learned from Crowdsourced Introductory Description of Information Visualizations

   Leni Yang, Cindy Xiong, Jason K. Wong, Aoyu Wu, and Huamin Qu

   IEEE Transactions on Visualization and Computer Graphics, 2021

   Abstract arXiv DOI

   Data visualizations have been increasingly used in oral presentations to communicate data patterns to the general public. Clear verbal introductions of visualizations to explain how to interpret the visually encoded information are essential to convey the takeaways and avoid misunderstandings. We contribute a series of studies to investigate how to effectively introduce visualizations to the audience with varying degrees of visualization literacy. We begin with understanding how people are introducing visualizations. We crowdsource 110 introductions of visualizations and categorize them based on their content and structures. From these crowdsourced introductions, we identify different introduction strategies and generate a set of introductions for evaluation. We conduct experiments to systematically compare the effectiveness of different introduction strategies across four visualizations with 1,080 participants. We find that introductions explaining visual encodings with concrete examples are the most effective. Our study provides both qualitative and quantitative insights into how to construct effective verbal introductions of visualizations in presentations, inspiring further research in data storytelling.

2. 

   ChinaVis

   iPoet: Interactive Painting Poetry Creation with Visual Multimodal Analysis

   Yingchaojie Feng, Jiazhou Chen, Keyu Huang, Jason K. Wong, Hui Ye, and 4 more authors

   Journal of Visualization, 2021

   Abstract DOI

   Chinese painting poetry is an extraordinary aesthetic phenomenon in world art history. It is not only part of the paintings but also helps us to better understand the spiritual conception that the artists express. In this paper, we present an interactive visual system to enable ordinary users to compose customized painting poetry for ancient Chinese paintings, which contain three properties: (1) We employ object detection and image captioning to describe the scenery depicted in the painting. (2) We extend the modern color theory to analyze the underlying emotions of each painting. (3) We propose an interactive poetry generation method that takes the content description and the emotional expression to add the diversity of the poetry creation. Several visual components are carefully designed to visualize and contextualize the features in the painting. They effectively guide users to steer the creation of personalized painting poems. We conduct efficient case studies and user interviews to demonstrate the effectiveness of our system.

2020

1. 

   IEEE VIS

   TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Groups

   Yating Lin*, Kamkwai Wong*, Yong Wang, Rong Zhang, Bo Dong, and 2 more authors

   IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS), 2020

   Abstract arXiv DOI Presentation

   Tax evasion is a serious economic problem for many countries, as it can undermine the government’ s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they failed to support the analysis and exploration of the uprising related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A taxpayer network is constructed and fused with the trade network to detect suspicious RPTTE groups. Rich visualizations are designed to facilitate the exploration and investigation of suspicious transactions between related taxpayers with profit and topological data analysis. Specifically, we propose a calendar heatmap with a carefully-designed encoding scheme to intuitively show the evidence of transferring revenue through related party transactions. We demonstrate the usefulness and effectiveness of TaxThemis through two case studies on real-world tax-related data, and interviews with domain experts.