Research

Jian Zhao, Maoyuan Sun, Francine Chen, Patrick Chiu Understanding Missing Links in Bipartite Networks with MissBiN. IEEE Transactions on Visualization and Computer Graphics, 2020 (In Press).
TVCG

Abstract: The analysis of bipartite networks is critical in a variety of application domains, such as exploring entity co-occurrences in intelligence analysis and investigating gene expression in bio-informatics. One important task is missing link prediction, which infers the existence of unseen links based on currently observed ones. In this paper, we propose a visual analysis system, MissBiN, to involve analysts in the loop for making sense of link prediction results. MissBiN equips a novel method for link prediction in a bipartite network by leveraging the information of bi-cliques in the network. It also provides an interactive visualization for understanding the algorithm outputs. The design of MissBiN is based on three high-level analysis questions (what, why, and how) regarding missing links, which are distilled from the literature and expert interviews. We conducted quantitative experiments to assess the performance of the proposed link prediction algorithm, and interviewed two experts from different domains to demonstrate the effectiveness of MissBiN as a whole. We also provide a comprehensive usage scenario to illustrate the usefulness of the tool in an application of intelligence analysis.

Jian Zhao, Mingming Fan, Mi Feng. ChartSeer: Interactive Steering Exploratory Visual Analysis with Machine Intelligence. IEEE Transactions on Visualization and Computer Graphics, 2020 (In Press).
TVCG

Abstract: During exploratory visual analysis (EVA), analysts need to continually determine which subsequent activities to perform, such as which data variables to explore or how to present data variables visually. Due to the vast combinations of data variables and visual encodings that are possible, it is often challenging to make such decisions. Further, while performing local explorations, analysts often fail to attend to the holistic picture that is emerging from their analysis, leading them to improperly steer their EVA. These issues become even more impactful in the real world analysis scenarios where EVA occurs in multiple asynchronous sessions that could be completed by one or more analysts. To address these challenges, this work proposes ChartSeer, a system that uses machine intelligence to enable analysts to visually monitor the current state of an EVA and effectively identify future activities to perform. ChartSeer utilizes deep learning techniques to characterize analyst-created data charts to generate visual summaries and recommend appropriate charts for further exploration based on user interactions. A case study was first conducted to demonstrate the usage of ChartSeer in practice, followed by a controlled study to compare ChartSeer’s performance with a baseline during EVA tasks. The results demonstrated that ChartSeer enables analysts to adequately understand current EVA status and advance their analysis by creating charts with increased coverage and visual encoding diversity.

Takanori Fujiwara, Jian Zhao, Francine Chen, Kwan‑Liu Ma. A Visual Analytics Framework for Contrastive Network Analysis Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 2020 (In Press).
VAST'20

Abstract: A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other. For example, when comparing protein interaction networks derived from normal and cancer tissues, one essential task is to discover protein-protein interactions unique to cancer tissues. However, this task is challenging when the networks contain complex structural (and semantic) relations. To address this problem, we design ContraNA, a visual analytics framework leveraging both the power of machine learning for uncovering unique characteristics in networks and also the effectiveness of visualization for understanding such uniqueness. The basis of ContraNA is cNRL, which integrates two machine learning schemes, network representation learning (NRL) and contrastive learning (CL), to generate a low-dimensional embedding that reveals the uniqueness of one network when compared to another. ContraNA provides an interactive visualization interface to help analyze the uniqueness by relating embedding results and network structures as well as explaining the learned features by cNRL. We demonstrate the usefulness of ContraNA with two case studies using real-world datasets. We also evaluate ContraNA through a controlled user study with 12 participants on network comparison tasks. The results show that participants were able to both effectively identify unique characteristics from complex networks and interpret the results obtained from cNRL.

Jian Zhao, Fanny Chevalier, Christopher Collins. Designing Tree Visualization Techniques for Discourse Analysis. LingVis: Visual Analytics for Linguistics, M. Butt, A. Hautli-Janisz, and V. Lyding (Editors), Chapter 3, Center for the Study of Language and Information, 2020.

Abstract: A discourse parser is a natural language processing system which can represent the organization of a document based on a rhetorical structure tree - one of the key data structures enabling applications such as text summarization question answering and dialogue generation. Computational linguists currently rely on manually exploring and comparing the discourse structures to get intuitions for improving parsing algorithms. In this paper, we revisit our earlier work on DAViewer, an interactive visualization system for assisting computational linguists to explore, compare, evaluate, and annotate the results of discourse parsers. We present an investigation of the rationales guiding design decisions for discourse analysis and compare three alternative representations of discourse parse trees. We report the results of an expert review of these design alternatives for the task of comparing discourse parsing algorithms.

Zejiang Shen, Jian Zhao, Melissa Dell, Yaoliang Yu, Weining Li. OLALA: Object-Level Active Learning Based Layout Annotation. arXiv:2010.01762, 2020.
arXiv

Abstract: In layout object detection problems, the ground-truth datasets are constructed by annotating object instances individually. Yet active learning for object detection is typically conducted at the image level, not at the object level. Because objects appear with different frequencies across images, image-level active learning may be subject to over-exposure to common objects. This reduces the efficiency of human labeling. This work introduces an Object-Level Active Learning based Layout Annotation framework, OLALA, which includes an object scoring method and a prediction correction algorithm. The object scoring method estimates the object prediction informativeness considering both the object category and the location. It selects only the most ambiguous object prediction regions within an image for annotators to label, optimizing the use of the annotation budget. For the unselected model predictions, we propose a correction algorithm to rectify two types of potential errors with minor supervision from ground-truths. The human annotated and model predicted objects are then merged as new image annotations for training the object detection models. In simulated labeling experiments, we show that OLALA helps to create the dataset more efficiently and report strong accuracy improvements of the trained models compared to image-level active learning baselines.

Siwei Fu, Jian Zhao, Linping Yuan, Zhicheng Liu, Kwan-Liu Ma, Huamin Qu. ICE: Identify and Compare Event Sequence Sets through Multi-Scale Matrix and Unit Visualizations. arXiv:2006.12718, 2020.
arXiv

Abstract: Comparative analysis of event sequence data is essential in many application domains, such as website design and medical care. However, analysts often face two challenges: they may not always know which sets of event sequences in the data are useful to compare, and the comparison needs to be achieved at different granularity, due to the volume and complexity of the data. This paper presents, ICE, an interactive visualization that allows analysts to explore an event sequence dataset, and identify promising sets of event sequences to compare at both the pattern and sequence levels. More specifically, ICE incorporates a multi-level matrix-based visualization for browsing the entire dataset based on the prefixes and suffixes of sequences. To support comparison at multiple levels, ICE employs the unit visualization technique, and we further explore the design space of unit visualizations for event sequence comparison tasks. Finally, we demonstrate the effectiveness of ICE with three real-world datasets from different domains.

Takanori Fujiwara, Jian Zhao, Francine Chen, Yaoliang Yu, Kwan‑Liu Ma. Interpretable Contrastive Learning for Networks. arXiv:2005.12419, 2020.
arXiv

Abstract: Contrastive learning (CL) is an emerging analysis approach that aims to discover unique patterns in one dataset relative to another. By applying this approach to network analysis, we can reveal unique characteristics in one network by contrasting with another. For example, with networks of protein interactions obtained from normal and cancer tissues, we can discover unique types of interactions in cancer tissues. However, existing CL methods cannot be directly applied to networks. To address this issue, we introduce a novel approach called contrastive network representation learning (cNRL). This approach embeds network nodes into a low-dimensional space that reveals the uniqueness of one network compared to another. Within this approach, we also design a method, named i-cNRL, that offers interpretability in the learned results, allowing for understanding which specific patterns are found in one network but not the other. We demonstrate the capability of i-cNRL with multiple network models and real-world datasets. Furthermore, we provide quantitative and qualitative comparisons across i-cNRL and other potential cNRL algorithm designs.

Brad Glasbergen, Michael Abebe, Khuzaima Daudjee, Daniel Vogel, Jian Zhao. Sentinel: Understanding Data Systems. Proceedings of the ACM SIGMOD Conference (Demo), pp. 2729-2732, 2020.  Best Demo
SIGMOD'20

Abstract: The complexity of modern data systems and applications greatly increases the challenge in understanding system behaviour and diagnosing performance problems. When these problems arise, system administrators are left with the difficult task of remedying them by relying on large debug log files, vast numbers of metrics, and system-specific tooling. We demonstrate the Sentinel system, which enables administrators to analyze systems and applications by building models of system execution and comparing them to derive key differences in behaviour. The resulting analyses are then presented as system reports to administrators and developers in an intuitive fashion. Users of Sentinel can locate, identify and take steps to resolve the reported performance issues. As Sentinel’s models are constructed online by intercepting debug logging library calls, Sentinel’s functionality incurs little overhead and works with all systems that use standard debug logging libraries.

Mingming Fan, Ke Wu^†, Jian Zhao^†, Yue Li, Winter Wei, Khai Truong. VisTA: Integrating Machine Intelligence with Visualization to Support the Investigation of Think-Aloud Sessions. IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2019), 26(1), pp. 343-352, 2020.
TVCG InfoVis'19

Abstract: Think-aloud protocols are widely used by user experience (UX) practitioners in usability testing to uncover issues in user interface design. It is often arduous to analyze large amounts of recorded think-aloud sessions and few UX practitioners have an opportunity to get a second perspective during their analysis due to time and resource constraints. Inspired by the recent research that shows subtle verbalization and speech patterns tend to occur when users encounter usability problems, we take the first step to design and evaluate an intelligent visual analytics tool that leverages such patterns to identify usability problem encounters and present them to UX practitioners to assist their analysis. We first conducted and recorded think-aloud sessions, and then extracted textual and acoustic features from the recordings and trained machine learning (ML) models to detect problem encounters. Next, we iteratively designed and developed a visual analytics tool, VisTA, which enables dynamic investigation of think-aloud sessions with a timeline visualization of ML predictions and input features. We conducted a between-subjects laboratory study to compare three conditions, i.e., VisTA, VisTASimple (no visualization of the ML’s input features), and Baseline (no ML information at all), with 30 UX professionals. The findings show that UX professionals identified more problem encounters when using VisTA than Baseline by leveraging the problem visualization as an overview, anticipations, and anchors as well as the feature visualization as a means to understand what ML considers and omits. Our findings also provide insights into how they treated ML, dealt with (dis)agreement with ML, and reviewed the videos (i.e., play, pause, and rewind).

Maoyuan Sun, Jian Zhao, Hao Wu, Kurt Luther, Chris North, Naren Ramakrishnan. The Effect of Edge Bundling and Seriation on Sensemaking of Biclusters in Bipartite Graphs. IEEE Transactions on Visualization and Computer Graphics, 25(10), pp. 2983-2998, 2019.
TVCG

Abstract: Exploring coordinated relationships (e.g., shared relationships between two sets of entities) is an important analytics task in a variety of real-world applications, such as discovering similarly behaved genes in bioinformatics, detecting malware collusions in cyber security, and identifying products bundles in marketing analysis. Coordinated relationships can be formalized as biclusters. In order to support visual exploration of biclusters, bipartite graphs based visualizations have been proposed, and edge bundling is used to show biclusters. However, it suffers from edge crossings due to possible overlaps of biclusters, and lacks in-depth understanding of its impact on user exploring biclusters in bipartite graphs. To address these, we propose a novel bicluster-based seriation technique that can reduce edge crossings in bipartite graphs drawing and conducted a user experiment to study the effect of edge bundling and this proposed technique on visualizing biclusters in bipartite graphs. We found that they both had impact on reducing entity visits for users exploring biclusters, and edge bundles helped them find more justified answers. Moreover, we identified four key trade-offs that inform the design of future bicluster visualizations. The study results suggest that edge bundling is critical for exploring biclusters in bipartite graphs, which helps to reduce low-level perceptual problems and support high-level inferences.

Zhicong Lu, Mingming Fan, Yun Wang, Jian Zhao, Michelle Annett, Daniel Wigdor. InkPlanner: Supporting Prewriting via Intelligent Visual Diagramming. IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2018), 25(1), pp. 277-287, 2019.
TVCG VAST'18

Abstract: Prewriting is the process of generating and organizing ideas before drafting a document. Although often overlooked by novice writers and writing tool developers, prewriting is a critical process that improves the quality of a final document. To better understand current prewriting practices, we first conducted interviews with writing learners and experts. Based on the learners’ needs and experts’ recommendations, we then designed and developed InkPlanner, a novel pen and touch visualization tool that allows writers to utilize visual diagramming for ideation during prewriting. InkPlanner further allows writers to sort their ideas into a logical and sequential narrative by using a novel widget— NarrativeLine. Using a NarrativeLine, InkPlanner can automatically generate a document outline to guide later drafting exercises. Inkplanner is powered by machine-generated semantic and structural suggestions that are curated from various texts. To qualitatively review the tool and understand how writers use InkPlanner for prewriting, two writing experts were interviewed and a user study was conducted with university students. The results demonstrated that InkPlanner encouraged writers to generate more diverse ideas and also enabled them to think more strategically about how to organize their ideas for later drafting.

John Wenskovitch, Jian Zhao^*, Scott Carter, Matthew Cooper, Chris North. Albireo: An Interactive Tool for Visually Summarizing Computational Notebook Structure. Proceedings of the IEEE Symposium on Visualization in Data Science, pp. 1-10, 2019.
VDS'19

Abstract: Computational notebooks have become a major medium for data exploration and insight communication in data science. Although expressive, dynamic, and flexible, in practice they are loose collections of scripts, charts, and tables that rarely tell a story or clearly represent the analysis process. This leads to a number of usability issues, particularly in the comprehension and exploration of notebooks. In this work, we design, implement, and evaluate Albireo, a visualization approach to summarize the structure of notebooks, with the goal of supporting more effective exploration and communication by displaying the dependencies and relationships between the cells of a notebook using a dynamic graph structure. We evaluate the system via a case study and expert interviews, with our results indicating that such a visualization is useful for an analyst’s self-reflection during exploratory programming, and also effective for communication of narratives and collaboration between analysts.

Cheonbok Park, Inyoup Na, Yongjang Jo, Sungbok Shin, Yoo Jaehyo, Bum Chul Kwon, Jian Zhao, Hyungjong Noh, Yeonsoo Lee, Jaegul Choo. SANVis: Visual Analytics for Understanding Self-Attention Networks. Proceedings of the IEEE VIS Conference, pp. 146-150, 2019.
VIS'19

Abstract: Attention networks, a deep neural network architecture inspired by humans’ attention mechanism, have seen significant success in im- age captioning, machine translation, and many other applications. Recently, they have been further evolved into an advanced approach called multi-head self-attention networks, which can encode a set of input vectors, e.g., word vectors in a sentence, into another set of vectors. Such encoding aims at simultaneously capturing diverse syntactic and semantic features within a set, each of which corresponds to a particular attention head, forming altogether multi-head attention. Meanwhile, the increased model complexity prevents users from easily understanding and manipulating the inner workings of models. To tackle the challenges, we present a visual analytics sys- tem called SANVis, which helps users understand the behaviors and the characteristics of multi-head self-attention networks. Using a state-of-the-art self-attention model called Transformer, we demon- strate usage scenarios of SANVis in machine translation tasks. Our system is available at http://short.sanvis.org.

Jian Zhao, Maoyuan Sun, Francine Chen, Patrick Chiu. MissBiN: Visual Analysis of Missing Links in Bipartite Networks. Proceedings of the IEEE VIS Conference, pp. 71-75, 2019.
VIS'19

Abstract: The analysis of bipartite networks is critical in a variety of application domains, such as exploring entity co-occurrences in intelligence analysis and investigating gene expression in bio-informatics. One important task is missing link prediction, which infers the existence of unseen links based on currently observed ones. In this paper, we propose MissBiN that involves analysts in the loop for making sense of link prediction results. MissBiN combines a novel method for link prediction and an interactive visualization for examining and understanding the algorithm outputs. Further, we conducted quantitative experiments to assess the performance of the proposed link prediction algorithm and a case study to evaluate the overall effectiveness of MissBiN.

Maoyuan Sun, David Koop, Jian Zhao, Chris North, Naren Ramakrishnan Interactive Bicluster Aggregation in Bipartite Graphs. Proceedings of the IEEE VIS Conference, pp. 246-250, 2019.
VIS'19

Abstract: Exploring coordinated relationships is important for sensemaking of data in various fields, such as intelligence analysis. To support such investigations, visual analysis tools use biclustering to mine relationships in bipartite graphs and visualize the resulting biclusters with standard graph visualization techniques. Due to overlaps among biclusters, such visualizations can be cluttered (e.g., with many edge crossings), when there are a large number of biclusters. Prior work attempted to resolve this problem by automatically ordering nodes in a bipartite graph. However, visual clutter is still a serious problem, since the number of displayed biclusters remains unchanged. We propose bicluster aggregation as an alternative approach, and have developed two methods of interactively merging biclusters. These interactive bicluster aggregations help organize similar biclusters and reduce the number of displayed biclusters. Initial expert feedback indicates potential usefulness of these techniques in practice.

Mona Loorak, Wei Zhou, Ha Trinh, Jian Zhao, Wei Li. Hand-Over-Face Input Sensing for Interaction with Smartphones through the Built-in Camera. Proceedings of the ACM International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 32:1-32:12, 2019.  Best Paper
MobileHCI'19

Abstract: This paper proposes using face as a touch surface and employing hand-over-face (HOF) gestures as a novel input modality for interaction with smartphones, especially when touch input is limited. We contribute InterFace, a general system framework that enables the HOF input modality using advanced computer vision techniques. As an examplar of the usage of this framework, we demonstrate the feasibility and usefulness of HOF with an Android application for improving single-user and group selfie-taking experience through providing appearance customization in real-time. In a within-subjects study comparing HOF against touch input for single-user interaction, we found that HOF input led to significant improvements in accuracy and perceived workload, and was preferred by the participants. Qualitative results of an observational study also demonstrated the potential of HOF input modality to improve the user experience in multi-user interactions. Based on the lessons learned from our studies, we propose a set of potential applications of HOF to support smartphone interaction. We envision that the affordances provided by the this modality can expand the mobile interaction vocabulary and facilitate scenarios where touch input is limited or even not possible.

Hao-Fei Cheng, Bowen Yu, Siwei Fu, Jian Zhao, Brent Hecht, Joseph Konstan, Loren Terveen, Svetlana Yarosh, Haiyi Zhu. Teaching UI Design at Global Scales: A Case Study of the Design of Collaborative Capstone Projects for MOOCs. Proceedings of the ACM Conference on Learning at Scale, pp. 11:1-11:11, 2019.
L@S'19

Abstract: Group projects are an essential component of teaching user interface (UI) design. We identified six challenges in transferring traditional group projects into the context of Massive Open Online Courses: managing dropout, avoiding free-riding, appropriate scaffolding, cultural and time zone differences, and establishing common ground. We present a case study of the design of a group project for a UI Design MOOC, in which we implemented technical tools and social structures to cope with the above challenges. Based on survey analysis, interviews, and team chat data from the students over a six-month period, we found that our socio-technical design addressed many of the obstacles that MOOC learners encountered during remote collaboration. We translate our findings into design implications for better group learning experiences at scale.

Chidansh Bhatt, Jian Zhao, Hideto Oda, Francine Chen, Matthew Lee. OPaPi: Optimized Parts Pick-up Routing for Efficient Manufacturing. Proceedings of the ACM SIGMOD Workshop on Human-In-the-Loop Data Analytics, 5:1-5:8, 2019.
HILDA'19

Abstract: Manufacturing environments require changes in work procedures and settings based on changes in product demand affecting the types of products for production. Resource re-organization and time needed for worker adaptation to such frequent changes can be expensive. For example, for each change, managers in a factory may be required to manually create a list of inventory items to be picked up by workers. Uncertainty in predicting the appropriate pick-up time due to differences in worker-determined routes may make it difficult for managers to generate a fixed schedule for delivery to the assembly line. To address these problems, we propose OPaPi, a human-centric system that improves the efficiency of manufacturing by optimizing parts pick-up routes and scheduling. OPaPi leverages frequent pattern mining and the traveling salesman problem solver to suggest rack placement for more efficient routes. The system further employs interactive visualization to incorporate an expert’s domain knowledge and different manufacturing constraints for real-time adaptive decision making.

Shenyu Xu^†, Chris Bryan^†, Kelvin Li^†, Jian Zhao, Kwan-Liu Ma. Chart Constellations: Effective Chart Summarization for Collaborative and Multi-User Analyses. Computer Graphics Forum (Proceedings of EuroVis 2018), 37(3), pp. 75-86, 2018.
CGF EuroVis'18

Abstract: Nowadays, many data problems in the real-world are complex and thus require multiple analysts working together to uncover embedded insights by creating chart-driven data stories. But how, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present a new system called Chart Constellations to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta-visualization approach. This approach supports deriving summary insights about the investigations and supports the exploration of new, un-investigated regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that our system promotes the discovery of both broad and high-level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.

Wen Zhong, Wei Xu, Kevin Yager, Gregory Doerk, Jian Zhao, Yunke Tian, Sungsoo Ha, Cong Xie, Yuan Zhong, Klaus Mueller, Kerstin Kleese Van Dam. MultiSciView: Multivariate Scientific X-ray Image Visual Exploration with Cross-Data Space Views. Visual Informatics (Proceedings of PacificVAST 2018), 2(1), pp. 14-25, 2018.
VI PacificVAST'18

Abstract: X-ray images obtained from synchrotron beamlines are large-scale, high-resolution and high-dynamic-range grayscale data encoding multiple complex properties of the measured materials. They are typically associated with a variety of metadata which increases their inherent complexity. There is a wealth of information embedded in these data but so far scientists lack modern exploration tools to unlock these hidden treasures. To bridge this gap, we propose MultiSciView, a multivariate scientific x-ray image visualization and exploration system for beamline-generated x-ray scattering data. Our system is composed of three complementary and coordinated interactive visualizations to enable a coordinated exploration across the images and their associated attribute and feature spaces. The first visualization features a multi-level scatterplot visualization dedicated for image exploration in attribute, image, and pixel scales. The second visualization is a histogram-based attribute cross filter by which users can extract desired subset patterns from data. The third one is an attribute projection visualization designed for capturing global attribute correlations. We demonstrate our framework by ways of a case study involving a real-world material scattering dataset. We show that our system can efficiently explore large-scale x-ray images, accurately identify preferred image patterns, anomalous images and erroneous experimental settings, and effectively advance the comprehension of material nanostructure properties..

Chidansh Bhatt, Matthew Cooper, Jian Zhao. SeqSense: Video Recommendation Using Topic Sequence Mining. Proceedings of the International Conference on Multimedia Modeling, pp. 252-263, 2018.
MMM'18

Abstract: This paper examines content-based recommendation in domains exhibiting sequential topical structure. An example is educational video, including Massive Open Online Courses (MOOCs) in which knowledge builds within and across courses. Conventional content-based or collaborative filtering recommendation methods do not exploit courses' sequential nature. We describe a system for video recommendation that combines topic-based video representation with sequential pattern mining of inter-topic relationships. Unsupervised topic modeling provides a scalable and domain-independent representation. We mine inter-topic relationships from manually constructed syllabi that instructors provide to guide students through their courses. This approach also allows the inclusion of multi-video sequences among the recommendation results. Integrating the resulting sequential information with content-level similarity provides relevant as well as diversified recommendations. Quantitative evaluation indicates that the proposed system, SeqSense, recommends fewer redundant videos than baseline methods, and instead emphasizes results consistent with mined topic transitions.

Jian Zhao, Chidansh Bhatt, Matthew Cooper, David Shamma. Flexible Learning with Semantic Visual Exploration and Sequence-Based Recommendation of MOOC Videos. Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 329:1--329:13, 2018.
CHI'18

Abstract: Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. This paper presents MOOCex, a technique that can offer a more flexible learning experience for MOOCs. MOOCex can recommend lecture videos across different courses with multiple perspectives, and considers both the video content and also sequential inter-topic relationships mined from course syllabi. MOOCex is also equipped with interactive visualization allowing learners to explore the semantic space of recommendations within their current learning context. The results of comparisons to traditional methods, including content-based recommendation and ranked list representation, indicate the effectiveness of MOOCex. Further, feedback from MOOC learners and instructors suggests that MOOCex enhances both MOOC-based learning and teaching.

Siwei Fu, Jian Zhao^*, Hao-Fei Cheng, Haiyi Zhu, Jennifer Marlow. T-Cal: Understanding Team Conversation Data with Calendar-based Visualization. Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 500:1-500:13, 2018.
CHI'18

Abstract: Understanding team communication and collaboration patterns is critical for improving work efficiency in organizations. This paper presents an interactive visualization system, T-Cal, that supports the analysis of conversation data from modern team messaging platforms (e.g., Slack). T-Cal employs a user-familiar visual interface, a calendar, to enable seamless multi-scale browsing of data from different perspectives. T-Cal also incorporates a number of analytical techniques for disentangling interleaving conversations, extracting keywords, and estimating sentiment. The design of T-Cal is based on an iterative user-centered design process including field studies, requirements gathering, initial prototypes demonstration, and evaluation with domain users. The resulting two case studies indicate the effectiveness and usefulness of T-Cal in real-world applications, including student group chats during a MOOC and daily conversations within an industry research lab.

Jian Zhao, Francine Chen, Patrick Chiu. A Generic Visualization Framework for Understanding Missing Links in Bipartite Networks. Proceedings of the ACM SIGGRAPH Asia Conference (Poster), 2018.
SA'18

Abstract: The analysis of bipartite networks is critical in many application domains, such as studying gene expression in bio-informatics. One important task is missing link prediction, which infers the existence of new links based on currently observed ones. However, in practice, analysts need to utilize their domain knowledge based on the algorithm outputs in order to make sense of the results. We propose a novel visual analysis framework, MissBi, which allows for examining and understanding missing links in bipartite networks. Some initial feedback from a management school professor has demonstrated the effectiveness of the tool.

Matthew Cooper, Jian Zhao, Chidansh Bhatt, David Shamma. Using Recommendation to Explore Educational Video. Proceedings of the ACM International Conference on Multimedia Retrieval (Demo), 2018.
ICMR'18

Abstract: Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. Increasingly, professionals consume this content to augment or update specific skills rather than complete degree or certification programs. To better address the needs of this emergent user population, we describe a visual recommender system called MOOCex. The system recommends lecture videos across multiple courses and content platforms to provide a choice of perspectives on topics. The recommendation engine considers both video content and sequential inter-topic relationships mined from course syllabi. Furthermore, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context.

Jian Zhao, Maoyuan Sun, Francine Chen, Patrick Chiu. BiDots: Visual Exploration of Weighted Biclusters. IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2017), 24(1), pp. 195-204, 2018.
TVCG VAST'17

Abstract: Discovering and analyzing biclusters, i.e., two sets of related entities with close relationships, is a critical task in many real-world applications, such as exploring entity co-occurrences in intelligence analysis, and studying gene expression in bio-informatics. While the output of biclustering techniques can offer some initial low-level insights, visual approaches are required on top of that due to the algorithmic output complexity.This paper proposes a visualization technique, called BiDots, that allows analysts to interactively explore biclusters over multiple domains. BiDots overcomes several limitations of existing bicluster visualizations by encoding biclusters in a more compact and cluster-driven manner. A set of handy interactions is incorporated to support flexible analysis of biclustering results. More importantly, BiDots addresses the cases of weighted biclusters, which has been underexploited in the literature. The design of BiDots is grounded by a set of analytical tasks derived from previous work. We demonstrate its usefulness and effectiveness for exploring computed biclusters with an investigative document analysis task, in which suspicious people and activities are identified from a text corpus.

Jian Zhao, Michael Glueck, Petra Isenberg, Fanny Chevalier, Azam Khan. Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs. IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2017), 24(1), pp. 340-350, 2018.  Honorable Mention
TVCG VAST'17

Abstract: During asynchronous collaborative analysis, handoff of partial findings is challenging because externalizations produced by analysts may not adequately communicate their investigative process. To address this challenge, we developed techniques to automatically capture and help encode tacit aspects of the investigative process based on an analyst’s interactions, and streamline explicit authoring of handoff annotations. We designed our techniques to mediate awareness of analysis coverage, support explicit communication of progress and uncertainty with annotation, and implicit communication through playback of investigation histories. To evaluate our techniques, we developed an interactive visual analysis system, KTGraph, that supports an asynchronous investigative document analysis task. We conducted a two-phase user study to characterize a set of handoff strategies and to compare investigative performance with and without our techniques. The results suggest that our techniques promote the use of more effective handoff strategies, help increase an awareness of prior investigative process and insights, as well as improve final investigative outcomes.

Siwei Fu, Hao Dong, Weiwei Cui, Jian Zhao, Huamin Qu. How Do Ancestral Traits Shape Family Trees over Generations? IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2017), 24(1), pp. 205-214, 2018.
TVCG VAST'17

Abstract: Whether and how does the structure of family trees differ by ancestral traits over generations? This is a fundamental question regarding the structural heterogeneity of family trees for the multi-generational transmission research. However, previous work mostly focuses on parent-child scenarios due to the lack of proper tools to handle the complexity of extending the research to multi-generational processes. Through an iterative design study with social scientists and historians, we develop TreeEvo that assists users to generate and test empirical hypotheses for multi-generational research. TreeEvo summarizes and organizes family trees by structural features in a dynamic manner based on a traditional Sankey diagram. A pixel-based technique is further proposed to compactly encode trees with complex structures in each Sankey Node. Detailed information of trees is accessible through a space-efficient visualization with semantic zooming. Moreover, TreeEvo embeds Multinomial Logit Model (MLM) to examine statistical associations between tree structure and ancestral traits. We demonstrate the effectiveness and usefulness of TreeEvo through an in-depth case-study with domain experts using a real-world dataset (containing 54,128 family trees of 126,196 individuals).

Mingqian Zhao, Yijia Su, Jian Zhao, Shaoyu Chen, Huamin Qu. Mobile Situated Analytics of Ego-centric Network Data. Proceedings of the ACM SIGGRAPH Asia Symposium on Visualization, pp. 14:1-14:8, 2017.
SA'17

Abstract: Situated Analytics has become popular and important with the resurge of Augmented Reality techniques and the prevalence of mobile platforms. However, existing Situated Analytics could only assist in simple visual analytical tasks such as data retrieval, and most visualization systems capable of aiding complex Visual Analytics are only designed for desktops. Thus, there remain lots of open questions about how to adapt desktop visualization systems to mobile platforms. In this paper, we conduct a study to discuss challenges and trade-offs during the process of adapting an existing desktop system to a mobile platform. With a specific example of interest, egoSlider {Wu et al. 2016}, a four-view dynamic ego-centric network visualization system is tailored to adapt the iPhone platform. We study how different view management techniques and interactions influence the effectiveness of presenting multi-scale visualizations including Scatterplot and Storyline visualizations. Simultaneously, a novel Main view+Thumbnails interface layout is devised to support smooth linking between multiple views on mobile platforms. We assess the effectiveness of our system through expert interviews with four experts in data visualization.

Jian Zhao, Michael Glueck, Simon Breslav, Fanny Chevalier, Azam Khan. Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data based on User-Authored Annotations. IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2016), 23(1), pp. 261-270, 2017.
TVCG VAST'17

Abstract: User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate thoughts between analysts. We present Annotation Graphs, a dynamic graph visualization that allows for high-level meta-analysis of data based on user-authored data annotations. Annotation graphs are implemented within C8, a system that enables visual exploratory analysis of a dataset and annotation authoring. Various layouts of the annotation graph are supported for viewing the annotation semantics from different perspectives. The space of annotation semantics includes data selections, comments, and tags, as well as their relationships. We propose a mixed-initiative approach to layout the annotation graph by integrating an analyst’s manual manipulations with an automatic layout based on the inferred similarity of the annotation semantics. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.

Siwei Fu, Jian Zhao, Weiwei Cui, Huamin Qu. Visual Analysis of MOOC Forums with iForum. IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2016), 23(1), pp. 201-210, 2017.
TVCG VAST'16

Abstract: Discussion forums of Massive Open Online Courses (MOOC) provide great opportunities for students to interact with instructional staff as well as other students. Exploration of MOOC forum data can offer valuable insights for these staff to enhance the course and prepare the next release. However, it is challenging due to the large, complicated, and heterogeneous nature of relevant datasets, which contain multiple dynamically interacting objects such as users, posts, and threads, each one including multiple attributes. In this paper, we present a design study for developing an interactive visual analytics system, called iForum, that allows for effectively discovering and understanding temporal patterns in MOOC forums. The design study was conducted with three domain experts in an iterative manner over one year, including a MOOC instructor and two official teaching assistants. iForum offers a set of novel visualization designs for presenting the three interleaving aspects of MOOC forums (i.e., posts, users, and threads) at three different scales. To demonstrate the effectiveness and usefulness of iForum, we describe a case study involving field experts, in which they use iForum to investigate real MOOC forum data for a course on JAVA programming.

Jian Zhao, Michael Glueck, Fanny Chevalier, Yanhong Wu, Azam Khan. Egocentric Analysis of Dynamic Networks with EgoLines. Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 5003-5014, 2016.  Honorable Mention
CHI'16

Abstract: The egocentric analysis of dynamic networks focuses on discovering the temporal patterns of a subnetwork around a specific central actor (i.e., an ego-network). These types of analyses are useful in many application domains, such as social science and business intelligence, providing insights about how the central actor interacts with the outside world. We present EgoLines, an interactive visualization to sup- port the egocentric analysis of dynamic networks. Using a "subway map" metaphor, a user can trace an individual actor over the evolution of the ego-network. The design of EgoLines is grounded in a set of key analytical questions pertinent to egocentric analysis, derived from our interviews with three domain experts and general network analysis tasks. We demonstrate the effectiveness of EgoLines in egocentric analysis tasks through a controlled experiment and a case study with a domain expert.

Jian Zhao, Ricardo Jota, Daniel Wigdor, Ravin Balakrishnan. Augmenting Mobile Phone Interaction with Face-Engaged Gestures. arXiv:1610.00214, 2016.
arXiv

Abstract: The movement of a user's face, easily detected by a smartphone's front camera, is an underexploited input modality for mobile interactions. We introduce three sets of face-engaged interaction techniques for augmenting the traditional mobile inputs, which leverages the combination of the head movements with touch gestures and device motions, all sensed via the phone's built-in sensors. We systemically present the space of design considerations for mobile interactions using one or more of the three input modalities (i.e., touch, motion, and head). The additional affordances of the proposed techniques expand the mobile interaction vocabulary, and can facilitate unique usage scenarios such as one-hand or touch-free interaction. An initial evaluation was conducted and users had positive reactions to the new techniques, indicating the promise of an intuitive and convenient user experience.

Yanhong Wu, Naveen Pitipornvivat, Jian Zhao, Sixiao Yang, Guowei Huang, Huamin Qu. egoSlider: Visual Analysis of Egocentric Network Evolution. IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2015), 22(1), pp. 260-269, 2016.
TVCG VAST'15

Abstract: Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.

Jian Zhao, R. William Soukoreff, Ravin Balakrishnan. Exploring and Modeling Unimanual Object Manipulation on Multi-Touch Displays. International Journal of Human-Computer Studies, 78(0), pp. 68-80, 2015.
IJHCS

Abstract: Touch-sensitive devices are becoming increasingly wide-spread, and consequently gestural interfaces have become familiar to the public. Despite the fact that many gestures require frequently dragging, pinching, spreading, and rotating the finger-tips, there currently does not exist a human performance model describing this interaction. In this paper, a novel user performance model is derived for virtual object manipulation on touch-sensitive displays, which involves simultaneous translation, rotation, and scaling of the object. Two controlled experiments with dual-finger unimanual manipulations were conducted to validate the new model. The results indicate that the model fits the experimental data well, and performs the best among several alternative models. Moreover, based on the analysis of the empirical data, the simultaneity nature of manipulation in the task is explored and several design implications are provided.

Jian Zhao, Zhicheng Liu, Mira Dontcheva, Aaron Hertzmann, Alan Wilson. MatrixWave: Visual Comparison of Event Sequence Data. Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 259-268, 2015.  Honorable Mention
CHI'15

Abstract: Event sequence data analysis is common in many domains, including web and software development, transportation, and medical care. Few have investigated visualization techniques for comparison analysis of multiple event sequence datasets. Grounded in the real-world characteristics of web clickstream data, we explore visualization techniques for comparison of two clickstream datasets collected on different days or from users with different demographics. Through iterative design with web analysts, we designed MatrixWave, a matrix-based representation that allows analysts to get an overview of differences in traffic patterns and interactively explore paths through the website. We use color to encode differences and size to offer context over traffic volume. User feedback on MatrixWave is positive. Participants in a laboratory study were more accurate with MatrixWave than the conventional Sankey diagram.

Fan Du, Nan Cao, Jian Zhao, Yu-Ru Lin. Trajectory Bundling for Animated Transitions. Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 289-298, 2015.
CHI'15

Abstract: Animated transition has been a popular design choice when switching between different views or layouts, in which moving trajectories are created as cues for tracking objects between their location shifting. Tracking moving objects, however, becomes difficult when objects' moving paths overlap or tracking targets increase. In our work, we propose a new design to facilitate tracking moving objects in animated transitions. Instead of simply moving an object along a straight line, we create "bundled" moving trajectories for a group of objects that are close to one another and share similar moving directions. To study the effect of bundled trajectories, we untangle variations due to different aspects of tracking complexity in a comprehensive controlled user study. The results ascertain the effectiveness of using bundled trajectories, especially when the number of tracking targets grow and the object movement involves high degree of occlusion. We discuss the implication of our new design and study.

Jian Zhao, Nan Cao, Zhen Wen, Yale Song, Yu-Ru Lin, Christopher Collins. #FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media. IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2014), 20(12), pp. 1773-1782, 2014.  Honorable Mention
TVCG VAST'14

Abstract: We present FluxFlow, an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media. Everyday, millions of messages are created, commented, and shared by people on social media websites, such as Twitter and Facebook. This provides valuable data for researchers and practitioners in many application domains, such as marketing, to inform decision-making. Distilling valuable social signals from the huge crowd's messages, however, is challenging, due to the heterogeneous and dynamic crowd behaviors. The challenge is rooted in data analysts' capability of discerning the anomalous information behaviors, such as the spreading of rumors or misinformation, from the rest that are more conventional patterns, such as popular topics and newsworthy events, in a timely fashion. FluxFlow incorporates advanced machine learning algorithms to detect anomalies, and offers a set of novel visualization designs for presenting the detected threads for deeper analysis. We evaluated FluxFlow with real datasets containing the Twitter feeds captured during significant events such as Hurricane Sandy. Through quantitative measurements of the algorithmic performance and qualitative interviews with domain experts, the results show that the back-end anomaly detection model is effective in identifying anomalous retweeting threads, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data and comprehend the underlying analytical model.

Jian Zhao, R. William Soukoreff, Xiangshi Ren, Ravin Balakrishnan. A Model of Scrolling on Touch-Sensitive Displays. International Journal of Human-Computer Studies, 72(12), pp. 805-821, 2014.
IJHCS

Abstract: Scrolling interaction is a common and frequent activity allowing users to browse content that is initially off-screen. With the increasing popularity of touch-sensitive devices, gesture-based scrolling interactions (e.g., finger panning and flicking) have become an important element in our daily interaction vocabulary. However, there are currently no comprehensive user performance models for scrolling tasks on touch displays. This paper presents an empirical study of user performance in scrolling tasks on touch displays. In addition to three geometrical movement parameters --- scrolling distance, display window size, and target width, we also investigate two other factors that could affect the performance, i.e., scrolling modes --- panning and flicking, and feedback techniques --- with and without distance feedback. We derive a quantitative model based on four formal assumptions that abstract the real-world scrolling tasks, which are drawn from the analysis and observations of user scrolling actions. The results of a control experiment reveal that our model generalizes well for direct-touch scrolling tasks, accommodating different movement parameters, scrolling modes and feedback techniques. Also, the supporting blocks of the model, the four basic assumptions and three important mathematical components, are validated by the experimental data. In-depth comparisons with existing models of similar tasks indicate that our model performs the best under different measurement criteria. Our work provides a theoretical foundation for modeling sophisticated scrolling actions, as well as offers insights into designing scrolling techniques for next-generation touch input devices.

Jian Zhao, Liang Gou, Fei Wang, Michelle Zhou. PEARL: An Interactive Visual Analytic Tool for Understanding Personal Emotion Style Derived from Social Media. Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, pp. 203-212, 2014.
VAST'14

Abstract: Hundreds of millions of people leave digital footprints on social media (e.g., Twitter and Facebook). Such data not only disclose a person's demographics and opinions, but also reveal one's emotional style. Emotional style captures a person's patterns of emotions over time, including his overall emotional volatility and resilience. Understanding one's emotional style can provide great benefits for both individuals and businesses alike, including the support of self-reflection and delivery of individualized customer care. We present PEARL a timeline-based visual analytic tool that allows users to interactively discover and examine a person's emotional style derived from this person's social media text. Compared to other visual text analytic systems, our work offers three unique contributions. First, it supports multi-dimensional emotion analysis from social media text to automatically detect a person's expressed emotions at different time points and summarize those emotions to reveal the person's emotional style. Second, it effectively visualizes complex, multi-dimensional emotion analysis results to create a visual emotional profile of an individual, which helps users browse and interpret one's emotional style. Third, it supports rich visual interactions that allow users to interactively explore and validate emotion analysis results. We have evaluated our work extensively through a series of studies. The results demonstrate the effectiveness of our tool both in emotion analysis from social media and in support of interactive visualization of the emotion analysis results.

Ji Wang, Jian Zhao, Sheng Guo, Chris North, Naren Ramakrishnan. ReCloud: Semantics-based Word Cloud Visualization of User Reviews. Proceedings of the Graphics Interface Conference, pp. 151-158, 2014.
GI'14

Abstract: User reviews, like those found on Yelp and Amazon, have become an important reference for decision making in daily life, for example, in dining, shopping and entertainment. However, large amounts of available reviews make the reading process tedious. Existing word cloud visualizations attempt to provide an overview. However their randomized layouts do not reveal content relationships to users. In this paper, we present ReCloud, a word cloud visualization of user reviews that arranges semantically related words as spatially proximal. We use a natural language processing technique called grammatical dependency parsing to create a semantic graph of review contents. Then, we apply a force-directed layout to the semantic graph, which generates a clustered layout of words by minimizing an energy model. Thus, ReCloud can provide users with more insight about the semantics and context of the review content. We also conducted an experiment to compare the efficiency of our method with two alternative review reading techniques: random layout word cloud and normal text-based reviews. The results showed that the proposed technique improves user performance and experience of understanding a large number of reviews.

Jian Zhao, Christopher Collins, Fanny Chevalier, Ravin Balakrishnan. Interactive Exploration of Implicit and Explicit Relations in Faceted Datasets. IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2013), 19(12), pp. 2080-2089, 2013.
TVCG VAST'13

Abstract: Many datasets, such as scientific literature collections, contain multiple heterogeneous facets which derive implicit relations, as well as explicit relational references between data items. The exploration of this data is challenging not only because of large data scales but also the complexity of resource structures and semantics. In this paper, we present PivotSlice, an interactive visualization technique which provides efficient faceted browsing as well as flexible capabilities to discover data relationships. With the metaphor of direct manipulation, PivotSlice allows the user to visually and logically construct a series of dynamic queries over the data, based on a multi-focus and multi-scale tabular view that subdivides the entire dataset into several meaningful parts with customized semantics. PivotSlice further facilitates the visual exploration and sensemaking process through features including live search and integration of online data, graphical interaction histories and smoothly animated visual state transitions. We evaluated PivotSlice through a qualitative lab study with university researchers and report the findings from our observations and interviews. We also demonstrate the effectiveness of PivotSlice using a scenario of exploring a repository of information visualization literature.

Jian Zhao, Daniel Wigdor, Ravin Balakrishnan. TrailMap: Facilitating Information Seeking in a Multi-Scale Digital Map via Implicit Bookmarking. Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 3009-3018, 2013.
CHI'13

Abstract: Web applications designed for map exploration in local neighborhoods have become increasingly popular and important in everyday life. During the information-seeking process, users often revisit previously viewed locations, repeat earlier searches, or need to memorize or manually mark areas of interest. To facilitate rapid returns to earlier views during map exploration, we propose a novel algorithm to automatically generate map bookmarks based on a user's interaction. TrailMap, a web application based on this algorithm, is developed, providing a fluid and effective neighborhood exploration experience. A one-week study is conducted to evaluate TrailMap in users' everyday web browsing activities. Results showed that TrailMap's implicit bookmarking mechanism is efficient for map exploration and the interactive and visual nature of the tool is intuitive to users.

Ji Wang, Jian Zhao, Sheng Guo, Chris North. Clustered Layout Word Cloud for User Generated Review. Yelp Dataset Challenge (Grand Prize Winner), 2013.
Yelp

Abstract: User reviews, like those found on Yelp and Amazon, have become an important reference for decision making in daily life, for example, in dining, shopping and entertainment. However, large amounts of available reviews make the reading process tedious. Existing word cloud visualizations attempt to provide an overview. However their randomized layouts do not reveal content relationships to users. In this paper, we present ReCloud, a word cloud visualization of user reviews that arranges semantically related words as spatially proximal. We use a natural language processing technique called grammatical dependency parsing to create a semantic graph of review contents. Then, we apply a force-directed layout to the semantic graph, which generates a clustered layout of words by minimizing an energy model. Thus, ReCloud can provide users with more insight about the semantics and context of the review content. We also conducted an experiment to compare the efficiency of our method with two alternative review reading techniques: random layout word cloud and normal text-based reviews. The results showed that the proposed technique improves user performance and experience of understanding a large number of reviews.

Jian Zhao, Fanny Chevalier, Christopher Collins, Ravin Balakrishnan. Facilitating Discourse Analysis with Interactive Visualization. IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2012), 18(12), pp. 2639-2648, 2012.
TVCG InfoVis'12

Abstract: A discourse parser is a natural language processing system which can represent the organization of a document based on a rhetorical structure tree---one of the key data structures enabling applications such as text summarization, question answering and dialogue generation. Computational linguistics researchers currently rely on manually exploring and comparing the discourse structures to get intuitions for improving parsing algorithms. In this paper, we present DAViewer, an interactive visualization system for assisting computational linguistics researchers to explore, compare, evaluate and annotate the results of discourse parsers. An iterative user-centered design process with domain experts was conducted in the development of DAViewer. We report the results of an informal formative study of the system to better understand how the proposed visualization and interaction techniques are used in the real research environment.

Jian Zhao, Steven Drucker, Danyel Fisher, Donald Brinkman. TimeSlice: Interactive Faceted Browsing of Timeline Data. Proceedings of the International Working Conference on Advanced Visual Interfaces, pp. 433-436, 2012.
AVI'12

Abstract: Temporal events with multiple sets of metadata attributes, i.e., facets, are ubiquitous across different domains. The capabilities of efficiently viewing and comparing events data from various perspectives are critical for revealing relationships, making hypotheses, and discovering patterns. In this paper, we present TimeSlice, an interactive faceted visualization of temporal events, which allows users to easily compare and explore timelines with different attributes on a set of facets. By directly manipulating the filtering tree, a dynamic visual representation of queries and filters in the facet space, users can simultaneously browse the focused timelines and their contexts at different levels of detail, which supports efficient navigation of multi-dimensional events data. Also presented is an initial evaluation of TimeSlice with two datasets - famous deceased people and US daily flight delays.

Jian Zhao. A Particle Filter Based Approach of Visualizing Time-varying Volume. Proceedings of the IEEE Symposium on Large-Scale Data Analysis and Visualization (Poster), 2012.
LDAV'12

Abstract: Extracting and presenting essential information of time-varying volumetric data is critical in many fields of sciences. This paper introduces a novel approach of identifying important aspects of the dataset under the particle filter framework in computer vision. With the view of time-varying volumes as dynamic voxels moving along time, an algorithm for computing the 3D voxel transition curves is derived. Based on the curves which characterize the local data temporal behavior, this paper also introduces several post-processing techniques to visualize important features such as curve clusters by k-means and curve variations computed from curve gradients.

Jian Zhao, Fanny Chevalier, Emmanuel Pietriga, Ravin Balakrishnan. Exploratory Analysis of Time-Series with ChronoLenses. IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2011), 17(12), pp. 2422-2431, 2011.
TVCG InfoVis'11

Abstract: Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling the simultaneous display of multiple variables, as well as the multi-scale display of time-series of very high resolution or that span long time periods. There has been comparatively little research on how to support the more elaborate tasks associated with the exploratory visual analysis of timeseries, e.g., visualizing derived values, identifying correlations, or discovering anomalies beyond obvious outliers. Such tasks typically require deriving new time-series from the original data, trying different functions and parameters in an iterative manner. We introduce a novel visualization technique called ChronoLenses, aimed at supporting users in such exploratory tasks. ChronoLenses perform on-the-fly transformation of the data points in their focus area, tightly integrating visual analysis with user actions, and enabling the progressive construction of advanced visual analysis pipelines.

R. William Soukoreff, Jian Zhao, Xiangshi Ren. The Entropy of a Rapid Aimed Movement: Fitts' Index of Difficulty versus Shannon's Entropy. Proceedings of 13th IFIP TC 13 International Conference on Human Computer Interaction, Vol Part 4, pp. 222-239, 2011.
INTERACT'11

Abstract: A thought experiment is proposed that reveals a difference between Fitts' index of difficulty and Shannon's entropy, in the quantification of the information content of a series of rapid aimed movements. This implies that the contemporary Shannon formulation of the index of difficulty is similar to, but not identical to, entropy. Preliminary work is reported toward developing a model that resolves the problem. Starting from first principles (information theory), a formulation for the entropy of a Fitts' law style rapid aimed movement is derived, that is similar in form to the traditional formulation. Empirical data from Fitts' 1954 paper are analysed, demonstrating that the new model fits empirical data as well as the current standard approach. The novel formulation is promising because it accurately describes human movement data, while also being derived from first principles (using information theory), thus providing insight into the underlying cause of Fitts' law.

Jian Zhao, Fanny Chevalier, Ravin Balakrishnan. KronoMiner: Using Multi-Foci Navigation for the Visual Exploration of Time-Series Data. Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 1737-1746, 2011.
CHI'11

Abstract: The need for pattern discovery in long time-series data led researchers to develop interactive visualization tools and analytical algorithms for gaining insight into the data. Most of the literature on time-series data visualization either focus on a small number of tasks or a specific domain. We propose KronoMiner, a tool that embeds new interaction and visualization techniques as well as analytical capabilities for the visual exploration of time-series data. The interface design has been iteratively refined based on feedback from expert users. Qualitative evaluation with an expert user not involved in the design process indicates that our prototype is promising for further research.

Jian Zhao, R. William Soukoreff, Ravin Balakrishnan. A Model of Multi-touch Manipulation GRAND'11: Proceedings of the 2nd Annual Grand Conference (Poster), 2011.
GRAND'11

Abstract: As touch-sensitive devices become increasingly popular, fundamentally understanding the human performances of multi-touch gestures is critical. However, there is currently no mathematical model for interpreting such gestures. In this paper, a novel model of multi-touch interaction is derived by combining the Mahalanobis distance metric and Fitts' law. The model describes the time required to complete an object manipulation task that includes translocation, rotation, and scaling. Empirical data is reported that validates the new model (R2>0.9). Linear relationship between the difficulty and time elapsed is revealed indicating that the model can provide guidelines for interface designers for empirically comparing gestures and devices.