Conference stringclasses 6 values | Year int64 1.99k 2.03k | Title stringlengths 8 187 | DOI stringlengths 16 32 | Abstract stringlengths 128 7.15k ⌀ | Accessible bool 2 classes | Early bool 2 classes | AuthorNames-Deduped listlengths 1 24 | Award listlengths 0 2 | Resources listlengths 0 5 | ResourceLinks listlengths 0 10 |
|---|---|---|---|---|---|---|---|---|---|---|
CHI | 2,018 | CrowdLayout: Crowdsourced Design and Evaluation of Biological Network Visualizations | 10.1145/3173574.3173806 | Biologists often perform experiments whose results generate large quantities of data, such as interactions between molecules in a cell, that are best represented as networks (graphs). To visualize these networks and communicate them in publications, biologists must manually position the nodes and edges of each network to reflect their real-world physical structure. This process does not scale well, and graph layout algorithms lack the biological underpinnings to offer a viable alternative. In this paper, we present CrowdLayout, a crowdsourcing system that leverages human intelligence and creativity to design layouts of biological network visualizations. CrowdLayout provides design guidelines, abstractions, and editing tools to help novice workers perform like experts. We evaluated CrowdLayout in two experiments with paid crowd workers and real biological network data, finding that crowds could both create and evaluate meaningful, high-quality layouts. We also discuss implications for crowdsourced design and network visualizations in other domains. | false | false | [
"Divit P. Singh",
"Lee Lisle",
"T. M. Murali 0001",
"Kurt Luther"
] | [] | [] | [] |
CHI | 2,018 | Data Illustrator: Augmenting Vector Design Tools with Lazy Data Binding for Expressive Visualization Authoring | 10.1145/3173574.3173697 | Building graphical user interfaces for visualization authoring is challenging as one must reconcile the tension between flexible graphics manipulation and procedural visualization generation based on a graphical grammar or declarative languages. To better support designers' workflows and practices, we propose Data Illustrator, a novel visualization framework. In our approach, all visualizations are initially vector graphics; data binding is applied when necessary and only constrains interactive manipulation to that data bound property. The framework augments graphic design tools with new concepts and operators, and describes the structure and generation of a variety of visualizations. Based on the framework, we design and implement a visualization authoring system. The system extends interaction techniques in modern vector design tools for direct manipulation of visualization configurations and parameters. We demonstrate the expressive power of our approach through a variety of examples. A qualitative study shows that designers can use our framework to compose visualizations. | false | false | [
"Zhicheng Liu 0001",
"John Thompson 0002",
"Alan Wilson 0004",
"Mira Dontcheva",
"James Delorey",
"Sam Grigg",
"Bernard Kerr",
"John T. Stasko"
] | [
"BP"
] | [] | [] |
CHI | 2,018 | DataInk: Direct and Creative Data-Oriented Drawing | 10.1145/3173574.3173797 | Creating whimsical, personal data visualizations remains a challenge due to a lack of tools that enable for creative visual expression while providing support to bind graphical content to data. Many data analysis and visualization creation tools target the quick generation of visual representations, but lack the functionality necessary for graphics design. Toolkits and charting libraries offer more expressive power, but require expert programming skills to achieve custom designs. In contrast, sketching affords fluid experimentation with visual shapes and layouts in a free-form manner, but requires one to manually draw every single data point. We aim to bridge the gap between these extremes. We propose DataInk, a system supports the creation of expressive data visualizations with rigorous direct manipulation via direct pen and touch input. Leveraging our commonly held skills, coupled with a novel graphical user interface, DataInk enables direct, fluid, and flexible authoring of creative data visualizations. | false | false | [
"Haijun Xia",
"Nathalie Henry Riche",
"Fanny Chevalier",
"Bruno Rodrigues De Araújo",
"Daniel Wigdor"
] | [
"HM"
] | [] | [] |
CHI | 2,018 | Design Patterns for Data Comics | 10.1145/3173574.3173612 | Data comics for data-driven storytelling are inspired by the visual language of comics and aim to communicate insights in data through visualizations. While comics are widely known, few examples of data comics exist and there has not been any structured analysis nor guidance for their creation. We introduce data-comic design-patterns, each describing a set of panels with a specific narrative purpose, that allow for rapid storyboarding of data comics while showcasing their expressive potential. Our patterns are derived from i) analyzing common patterns in infographics, datavideos, and existing data comics, ii) our experiences creating data comics for different scenarios. Our patterns demonstrate how data comics allow an author to combine the best of both worlds: spatial layout and overview from infographics as well as linearity and narration from videos and presentations. | false | false | [
"Benjamin Bach",
"Zezhong Wang 0001",
"Matteo Farinella",
"Dave Murray-Rust",
"Nathalie Henry Riche"
] | [] | [] | [] |
CHI | 2,018 | Dream Lens: Exploration and Visualization of Large-Scale Generative Design Datasets | 10.1145/3173574.3173943 | This paper presents Dream Lens, an interactive visual analysis tool for exploring and visualizing large-scale generative design datasets. Unlike traditional computer aided design, where users create a single model, with generative design, users specify high-level goals and constraints, and the system automatically generates hundreds or thousands of candidates all meeting the design criteria. Once a large collection of design variations is created, the designer is left with the task of finding the design, or set of designs, which best meets their requirements. This is a complicated task which could require analyzing the structural characteristics and visual aesthetics of the designs. Two studies are conducted which demonstrate the usability and usefulness of the Dream Lens system, and a generatively designed dataset of 16,800 designs for a sample design problem is described and publicly released to encourage advancement in this area. | false | false | [
"Justin Matejka",
"Michael Glueck",
"Erin Bradner",
"Ali Hashemi 0001",
"Tovi Grossman",
"George W. Fitzmaurice"
] | [] | [] | [] |
CHI | 2,018 | Experiential Augmentation: Uncovering The Meaning of Qualitative Visualizations when Applied to Augmented Objects | 10.1145/3173574.3174064 | As we move toward commercial usage of ubiquitous computing and augmented reality, it is important to think about how computing should communicate with us when it is distributed in our environment. This paper proposes that qualitative indexical visualizations based on learned understanding of physical phenomena (Experiential Augmentation) can enhance our interaction design language and aid digital interfaces in communicating in a real-world context. We present a study that gathers data on how participants interpret such visualizations, and propose a model with which to analyze their responses. Finally, we also give a set of design recommendations for those interested in creating similar augmentations. | false | false | [
"Dixon Lo",
"Dan Lockton",
"Stacie Rohrbach"
] | [] | [] | [] |
CHI | 2,018 | Exploration and Explanation in Computational Notebooks | 10.1145/3173574.3173606 | Computational notebooks combine code, visualizations, and text in a single document. Researchers, data analysts, and even journalists are rapidly adopting this new medium. We present three studies of how they are using notebooks to document and share exploratory data analyses. In the first, we analyzed over 1 million computational notebooks on GitHub, finding that one in four had no explanatory text but consisted entirely of visualizations or code. In a second study, we examined over 200 academic computational notebooks, finding that although the vast majority described methods, only a minority discussed reasoning or results. In a third study, we interviewed 15 academic data analysts, finding that most considered computational notebooks personal, exploratory, and messy. Importantly, they typically used other media to share analyses. These studies demonstrate a tension between exploration and explanation in constructing and sharing computational notebooks. We conclude with opportunities to encourage explanation in computational media without hindering exploration. | false | false | [
"Adam Rule",
"Aurélien Tabard",
"James D. Hollan"
] | [
"HM"
] | [] | [] |
CHI | 2,018 | Flexible and Mindful Self-Tracking: Design Implications from Paper Bullet Journals | 10.1145/3173574.3173602 | Digital self-tracking technologies offer many potential benefits over self-tracking with paper notebooks. However, they are often too rigid to support people's practical and emotional needs in everyday settings. To inform the design of more flexible self-tracking tools, we examine bullet journaling: an analogue and customisable approach for logging and reflecting on everyday life. Analysing a corpus of paper bullet journal photos and related conversations on Instagram, we found that individuals extended and adapted bullet journaling systems to their changing practical and emotional needs through: (1) creating and combining personally meaningful visualisations of different types of trackers, such as habit, mood, and symptom trackers; (2) engaging in mindful reflective thinking through design practices and self-reflective strategies; and (3) posting photos of paper journals online to become part of a self-tracking culture of sharing and learning. We outline two interrelated design directions for flexible and mindful self-tracking: digitally extending analogue self-tracking and supporting digital self-tracking as a mindful design practice. | false | false | [
"Amid Ayobi",
"Tobias Sonne",
"Paul Marshall",
"Anna L. Cox"
] | [] | [] | [] |
CHI | 2,018 | Frames and Slants in Titles of Visualizations on Controversial Topics | 10.1145/3173574.3174012 | Slanted framing in news article titles induce bias and influence recall. While recent studies found that viewers focus extensively on titles when reading visualizations, the impact of titles in visualization remains underexplored. We study frames in visualization titles, and how the slanted framing of titles and the viewer's pre-existing attitude impact recall, perception of bias, and change of attitude. When asked to compose visualization titles, people used five existing news frames, an open-ended frame, and a statistics frame. We found that the slant of the title influenced the perceived main message of a visualization, with viewers deriving opposing messages from the same visualization. The results did not show any significant effect on attitude change. We highlight the danger of subtle statistics frames and viewers' unwarranted conviction of the neutrality of visualizations. Finally, we present a design implication for the generation of visualization titles and one for the viewing of titles. | false | false | [
"Ha Kyung Kong",
"Zhicheng Liu 0001",
"Karrie Karahalios"
] | [] | [] | [] |
CHI | 2,018 | HomeFinder Revisited: Finding Ideal Homes with Reachability-Centric Multi-Criteria Decision Making | 10.1145/3173574.3173821 | Finding an ideal home is a difficult and laborious process. One of the most crucial factors in this process is the reachability between the home location and the concerned points of interest, such as places of work and recreational facilities. However, such importance is unrecognized in the extant real estate systems. By characterizing user requirements and analytical tasks in the context of finding ideal homes, we designed ReACH, a novel visual analytics system that assists people in finding, evaluating, and choosing a home based on multiple criteria, including reachability. In addition, we developed an improved data-driven model for approximating reachability with massive taxi trajectories. This model enables users to interactively integrate their knowledge and preferences to make judicious and informed decisions. We show the improvements in our model by comparing the theoretical complexities with the prior study and demonstrate the usability and effectiveness of the proposed system with task-based evaluation. | false | false | [
"Di Weng",
"Heming Zhu",
"Jie Bao 0003",
"Yu Zheng 0004",
"Yingcai Wu"
] | [] | [] | [] |
CHI | 2,018 | InfoNice: Easy Creation of Information Graphics | 10.1145/3173574.3173909 | Information graphics are widely used to convey messages and present insights in data effectively. However, creating expressive data-driven infographics remains a great challenge for general users without design expertise. We present InfoNice, a visualization design tool that enables users to easily create data-driven infographics. InfoNice allows users to convert unembellished charts into infographics with multiple visual elements through mark customization. We implement InfoNice into Microsoft Power BI to demonstrate the integration of InfoNice into data analysis workflow seamlessly, bridging the gap between data exploration and presentation. We evaluate the usability and usefulness of InfoNice through example infographics, an in-lab user study, and real-world user feedback. Our results show that InfoNice enables users to create a variety of infographics easily for common scenarios. | false | false | [
"Yun Wang 0012",
"Haidong Zhang",
"He Huang",
"Xi Chen",
"Qiufeng Yin",
"Zhitao Hou",
"Dongmei Zhang 0001",
"Qiong Luo 0001",
"Huamin Qu"
] | [] | [] | [] |
CHI | 2,018 | Investigating the Effect of the Multiple Comparisons Problem in Visual Analysis | 10.1145/3173574.3174053 | The goal of a visualization system is to facilitate dataset-driven insight discovery. But what if the insights are spurious? Features or patterns in visualizations can be perceived as relevant insights, even though they may arise from noise. We often compare visualizations to a mental image of what we are interested in: a particular trend, distribution or an unusual pattern. As more visualizations are examined and more comparisons are made, the probability of discovering spurious insights increases. This problem is well-known in Statistics as the multiple comparisons problem (MCP) but overlooked in visual analysis. We present a way to evaluate MCP in visualization tools by measuring the accuracy of user reported insights on synthetic datasets with known ground truth labels. In our experiment, over 60% of user insights were false. We show how a confirmatory analysis approach that accounts for all visual comparisons, insights and non-insights, can achieve similar results as one that requires a validation dataset. | false | false | [
"Emanuel Zgraggen",
"Zheguang Zhao",
"Robert C. Zeleznik",
"Tim Kraska"
] | [] | [] | [] |
CHI | 2,018 | Looks Can Be Deceiving: Using Gaze Visualisation to Predict and Mislead Opponents in Strategic Gameplay | 10.1145/3173574.3173835 | In competitive co-located gameplay, players use their opponents' gaze to make predictions about their plans while simultaneously managing their own gaze to avoid giving away their plans. This socially competitive dimension is lacking in most online games, where players are out of sight of each other. We conducted a lab study using a strategic online game; finding that (1) players are better at discerning their opponent's plans when shown a live visualisation of the opponent's gaze, and (2) players who are aware that their gaze is tracked will manipulate their gaze to keep their intentions hidden. We describe the strategies that players employed, to various degrees of success, to deceive their opponent through their gaze behaviour. This gaze-based deception adds an effortful and challenging aspect to the competition. Lastly, we discuss the various implications of our findings and its applicability for future game design. | false | false | [
"Joshua Newn",
"Fraser Allison",
"Eduardo Velloso",
"Frank Vetere"
] | [] | [] | [] |
CHI | 2,018 | Methods for Intentional Encoding of High Capacity Human-Designable Visual Markers | 10.1145/3173574.3173887 | Previous techniques for human-designable visual markers have focused on small encoding spaces, and assume artists do not need to encode specific bit representations. We present a general framework for human-designable visual markers for artists to encode specific bit representations in large spaces. A three-part study, conducted over three weeks, methodically evaluates the usability of different encoding methods when artists encode specific bit representations. The methods span different shape characteristics suitable for artist encoding (convexity, hollowness, number, size, and distance from centroid) and visualization tools are proposed to aid in this process. We further demonstrate that any of the methods presented may be practically used to encode a URL with the aid of a universally available database like TinyURL (rather than a task-specific database), making human-designable visual markers practical for applications such as advertisements. | false | false | [
"Joshua D. A. Jung",
"Daniel Vogel 0001"
] | [] | [] | [] |
CHI | 2,018 | More Text Please! Understanding and Supporting the Use of Visualization for Clinical Text Overview | 10.1145/3173574.3173996 | Clinical practice is heavily reliant on the use of unstructured text to document patient stories due to its expressive and flexible nature. However, a physician's capacity to recover information from text for clinical overview is severely affected when records get longer and time pressure increases. Data visualization strategies have been explored to aid in information retrieval by replacing text with graphical summaries, though often at the cost of omitting important text features. This causes physician mistrust and limits real-world adoption. This work presents our investigation into the role and use of text in clinical practice, and reports on efforts to assess the best of both worlds---text and visualization---to facilitate clinical overview. We report on insights garnered from a field study, and the lessons learned from an iterative design process and evaluation of a text-visualization prototype, MedStory, with 14 medical professionals. The results led to a number of grounded design recommendations to guide visualization design to support clinical text overview. | false | false | [
"Nicole Sultanum",
"Michael Brudno",
"Daniel Wigdor",
"Fanny Chevalier"
] | [
"HM"
] | [] | [] |
CHI | 2,018 | Physical Keyboards in Virtual Reality: Analysis of Typing Performance and Effects of Avatar Hands | 10.1145/3173574.3173919 | Entering text is one of the most common tasks when interacting with computing systems. Virtual Reality (VR) presents a challenge as neither the user's hands nor the physical input devices are directly visible. Hence, conventional desktop peripherals are very slow, imprecise, and cumbersome. We developed a apparatus that tracks the user's hands, and a physical keyboard, and visualize them in VR. In a text input study with 32 participants, we investigated the achievable text entry speed and the effect of hand representations and transparency on typing performance, workload, and presence. With our apparatus, experienced typists benefited from seeing their hands, and reach almost outside-VR performance. Inexperienced typists profited from semi-transparent hands, which enabled them to type just 5.6 WPM slower than with a regular desktop setup. We conclude that optimizing the visualization of hands in VR is important, especially for inexperienced typists, to enable a high typing performance. | false | false | [
"Pascal Knierim",
"Valentin Schwind",
"Anna Maria Feit",
"Florian Nieuwenhuizen",
"Niels Henze"
] | [] | [] | [] |
CHI | 2,018 | Pictures Worth a Thousand Words: Reflections on Visualizing Personal Blood Glucose Forecasts for Individuals with Type 2 Diabetes | 10.1145/3173574.3174112 | Type 2 Diabetes Mellitus (T2DM) is a common chronic condition that requires management of one's lifestyle, including nutrition. Critically, patients often lack a clear understanding of how everyday meals impact their blood glucose. New predictive analytics approaches can provide personalized mealtime blood glucose forecasts. While communicating forecasts can be challenging, effective strategies for doing so remain little explored. In this study, we conducted focus groups with 13 participants to identify approaches to visualizing personalized blood glucose forecasts that can promote diabetes self-management and understand key styles and visual features that resonate with individuals with diabetes. Focus groups demonstrated that individuals rely on simple heuristics and tend to take a reactive approach to their health and nutrition management. Further, the study highlighted the need for simple and explicit, yet information-rich design. Effective visualizations were found to utilize common metaphors alongside words, numbers, and colors to convey a sense of authority and encourage action and learning. | false | false | [
"Pooja M. Desai",
"Matthew E. Levine",
"David J. Albers",
"Lena Mamykina"
] | [] | [] | [] |
CHI | 2,018 | Self-Reflection and Personal Physicalization Construction | 10.1145/3173574.3173728 | Self-reflection is a central goal of personal informatics systems, and constructing visualizations from physical tokens has been found to help people reflect on data. However, so far, constructive physicalization has only been studied in lab environments with provided datasets. Our qualitative study investigates the construction of personal physicalizations in people's domestic environments over 2-4 weeks. It contributes an understanding of (1) the process of creating personal physicalizations, (2) the types of personal insights facilitated, (3) the integration of self-reflection in the physicalization process, and (4) its benefits and challenges for self-reflection. We found that in constructive personal physicalization, data collection, construction and self-reflections are deeply intertwined. This extends previous models of visualization creation and data-driven self-reflection. We outline how benefits such as reflection through manual construction, personalization, and presence in everyday life can be transferred to a wider set of digital and physical systems. | false | false | [
"Alice Thudt",
"Uta Hinrichs",
"Samuel Huron",
"Sheelagh Carpendale"
] | [] | [] | [] |
CHI | 2,018 | SpeechBubbles: Enhancing Captioning Experiences for Deaf and Hard-of-Hearing People in Group Conversations | 10.1145/3173574.3173867 | Deaf and hard-of-hearing (DHH) individuals encounter difficulties when engaged in group conversations with hearing individuals, due to factors such as simultaneous utterances from multiple speakers and speakers whom may be potentially out of view. We interviewed and co-designed with eight DHH participants to address the following challenges: 1) associating utterances with speakers, 2) ordering utterances from different speakers, 3) displaying optimal content length, and 4) visualizing utterances from out-of-view speakers. We evaluated multiple designs for each of the four challenges through a user study with twelve DHH participants. Our study results showed that participants significantly preferred speechbubble visualizations over traditional captions. These design preferences guided our development of SpeechBubbles, a real-time speech recognition interface prototype on an augmented reality head-mounted display. From our evaluations, we further demonstrated that DHH participants preferred our prototype over traditional captions for group conversations. | false | false | [
"Yi-Hao Peng",
"Ming-Wei Hsu",
"Paul Taele",
"Ting-Yu Lin",
"Po-En Lai",
"Leon Hsu",
"Tzu-Chuan Chen",
"Te-Yen Wu",
"Yu-An Chen",
"Hsien-Hui Tang",
"Mike Y. Chen"
] | [] | [] | [] |
CHI | 2,018 | T-Cal: Understanding Team Conversational Data with Calendar-based Visualization | 10.1145/3173574.3174074 | 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 interview 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 daily conversations within an industry research lab and student group chats in a MOOC. | false | false | [
"Siwei Fu",
"Jian Zhao 0010",
"Hao Fei Cheng",
"Haiyi Zhu",
"Jennifer Marlow"
] | [] | [] | [] |
CHI | 2,018 | Tangible Drops: A Visio-Tactile Display Using Actuated Liquid-Metal Droplets | 10.1145/3173574.3173751 | We present Tangible Drops, a visio-tactile display that for the first time provides physical visualization and tactile feedback using a planar liquid interface. It presents digital information interactively by tracing dynamic patterns on horizontal flat surfaces using liquid metal drops on a programmable electrode array. It provides tactile feedback with directional information in the 2D vector plane using linear locomotion and/or vibration of the liquid metal drops. We demonstrate move, oscillate, merge, split and dispense-from-reservoir functions of the liquid metal drops by consuming low power (450 mW per electrode) and low voltage (8--15 V). We report on results of our empirical study with 12 participants on tactile feedback using 8 mm diameter drops, which indicate that Tangible Drops can convey tactile sensations such as changing speed, varying direction and controlled oscillation with no visual feedback. We present the design space and demonstrate the applications of Tangible Drops, and conclude by suggesting potential future applications for the technique. | false | false | [
"Deepak Ranjan Sahoo",
"Timothy Neate",
"Yutaka Tokuda",
"Jennifer Pearson 0001",
"Simon Robinson 0001",
"Sriram Subramanian",
"Matt Jones 0001"
] | [] | [] | [] |
CHI | 2,018 | The Effects of Adding Search Functionality to Interactive Visualizations on the Web | 10.1145/3173574.3173711 | The widespread use of text-based search in user interfaces has led designers in visualization to occasionally add search functionality to their creations. Yet it remains unclear how search may impact a person's behavior. Given the unstructured context of the web, users may not have explicit information-seeking goals and designers cannot make assumptions about user attention. To bridge this gap, we observed the impact of integrating search with five visualizations across 830 online participants. In an unguided task, we find that (1) the presence of text-based search influences people's information-seeking goals, (2) search can alter the data that people explore and how they engage with it, and (3) the effects of search are amplified in visualizations where people are familiar with the underlying dataset. These results suggest that text-search in web visualizations drives users towards more diverse information seeking goals, and may be valuable in a range of existing visualization designs. | false | false | [
"Mi Feng",
"Cheng Deng",
"Evan M. Peck",
"Lane Harrison"
] | [] | [] | [] |
CHI | 2,018 | ThermoKiosk: Investigating Roles for Digital Surveys of Thermal Experience in Workplace Comfort Management | 10.1145/3173574.3173956 | Thermal comfort in shared workplaces is often contested and impacts productivity, wellbeing, and energy use. Yet, subjective and situated comfort experiences are rarely captured and engaged with. In this paper, we explore roles for digital surveys in capturing and visualising subjective experiences of comfort in situ for comfort management. We present findings from a 3-week field trial of our prototype system called ThermoKiosk, which we deployed in an open plan, shared office with a history of thermal comfort complaints. In interviews with occupants and members of facilities management, we find that the data and interactions can play an important role in initiating dialogue to understand and handle tensions, and point to design considerations for more systematically integrating them into workplace comfort practices. | false | false | [
"Adrian K. Clear",
"Samantha Mitchell Finnigan",
"Patrick Olivier",
"Rob Comber"
] | [] | [] | [] |
CHI | 2,018 | TopicOnTiles: Tile-Based Spatio-Temporal Event Analytics via Exclusive Topic Modeling on Social Media | 10.1145/3173574.3174157 | Detecting anomalous events of a particular area in a timely manner is an important task. Geo-tagged social media data are useful resource for this task; however, the abundance of everyday language in them makes this task still challenging. To address such challenges, we present TopicOnTiles, a visual analytics system that can reveal information relevant to anomalous events in a multi-level tile-based map interface by using social media data. To this end, we adopt and improve a recently proposed topic modeling method that can extract spatio-temporally exclusive topics corresponding to a particular region and a time point. Furthermore, we utilize a tile-based map interface to efficiently handle large-scale data in parallel. Our user interface effectively highlights anomalous tiles using our novel glyph visualization that encodes the degree of anomaly computed by our exclusive topic modeling processes. To show the effectiveness of our system, we present several usage scenarios using real-world datasets as well as comprehensive user study results. | false | false | [
"Minsuk Choi",
"Dear Sungbok Shin",
"Jinho Choi 0005",
"Scott Langevin",
"Christopher Bethune",
"Philippe Horne",
"Nathan Kronenfeld",
"Ramakrishnan Kannan",
"Barry L. Drake",
"Haesun Park",
"Jaegul Choo"
] | [] | [] | [] |
CHI | 2,018 | TopoText: Context-Preserving Text Data Exploration Across Multiple Spatial Scales | 10.1145/3173574.3173611 | TopoText is a context-preserving technique for visualizing text data for multi-scale spatial aggregates to gain insight into spatial phenomena. Conventional exploration requires users to navigate across multiple scales but only presents the information related to the current scale. This limitation potentially adds more steps of interaction and cognitive overload to the users. TopoText renders multi-scale aggregates into a single visual display combining novel text-based encoding and layout methods that draw labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each individual scale, but also indicates the spatial coverage of the aggregates and their underlying hierarchical relationships. We validate TopoText with both a user study as well as several application examples. | false | false | [
"Jiawei Zhang 0003",
"Chittayong Surakitbanharn",
"Niklas Elmqvist",
"Ross Maciejewski",
"Zhenyu Cheryl Qian",
"David S. Ebert"
] | [
"HM"
] | [] | [] |
CHI | 2,018 | Towards Design Principles for Visual Analytics in Operations Contexts | 10.1145/3173574.3173712 | Operations engineering teams interact with complex data systems to make technical decisions that ensure the operational efficacy of their missions. To support these decision-making tasks, which may require elastic prioritization of goals dependent on changing conditions, custom analytics tools are often developed. We were asked to develop such a tool by a team at the NASA Jet Propulsion Laboratory, where rover telecom operators make decisions based on models predicting how much data rovers can transfer from the surface of Mars. Through research, design, implementation, and informal evaluation of our new tool, we developed principles to inform the design of visual analytics systems in operations contexts. We offer these principles as a step towards understanding the complex task of designing these systems. The principles we present are applicable to designers and developers tasked with building analytics systems in domains that face complex operations challenges such as scheduling, routing, and logistics. | false | false | [
"Matthew Conlen",
"Sara Stalla",
"Chelly Jin",
"Maggie Hendrie",
"Hillary Mushkin",
"Santiago V. Lombeyda",
"Scott Davidoff"
] | [] | [] | [] |
CHI | 2,018 | Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making | 10.1145/3173574.3173718 | Everyday predictive systems typically present point predictions, making it hard for people to account for uncertainty when making decisions. Evaluations of uncertainty displays for transit prediction have assessed people's ability to extract probabilities, but not the quality of their decisions. In a controlled, incentivized experiment, we had subjects decide when to catch a bus using displays with textual uncertainty, uncertainty visualizations, or no-uncertainty (control). Frequency-based visualizations previously shown to allow people to better extract probabilities (quantile dotplots) yielded better decisions. Decisions with quantile dotplots with 50 outcomes were(1) better on average, having expected payoffs 97% of optimal(95% CI: [95%,98%]), 5 percentage points more than control (95% CI: [2,8]); and (2) more consistent, having within-subject standard deviation of 3 percentage points (95% CI:[2,4]), 4 percentage points less than control (95% CI: [2,6]).Cumulative distribution function plots performed nearly as well, and both outperformed textual uncertainty, which was sensitive to the probability interval communicated. We discuss implications for real time transit predictions and possible generalization to other domains. | false | false | [
"Michael Fernandes",
"Logan Walls",
"Sean Munson",
"Jessica Hullman",
"Matthew Kay 0001"
] | [
"HM"
] | [] | [] |
CHI | 2,018 | Uncertainty Visualization Influences how Humans Aggregate Discrepant Information | 10.1145/3173574.3174079 | The number of sensors in our surroundings that provide the same information steadily increases. Since sensing is prone to errors, sensors may disagree. For example, a GPS-based tracker on the phone and a sensor on the bike wheel may provide discrepant estimates on traveled distance. This poses a user dilemma, namely how to reconcile the conflicting information into one estimate. We investigated whether visualizing the uncertainty associated with sensor measurements improves the quality of users' inference. We tested four visualizations with increasingly detailed representation of uncertainty. Our study repeatedly presented two sensor measurements with varying degrees of inconsistency to participants who indicated their best guess of the "true" value. We found that uncertainty information improves users' estimates, especially if sensors differ largely in their associated variability. Improvements were larger for information-rich visualizations. Based on our findings, we provide an interactive tool to select the optimal visualization for displaying conflicting information. | false | false | [
"Miriam Greis",
"Aditi Joshi",
"Ken Singer",
"Albrecht Schmidt 0001",
"Tonja Machulla"
] | [] | [] | [] |
CHI | 2,018 | Understanding Older Users' Acceptance of Wearable Interfaces for Sensor-based Fall Risk Assessment | 10.1145/3173574.3173693 | Algorithms processing data from wearable sensors promise to more accurately predict risks of falling -- a significant concern for older adults. Substantial engineering work is dedicated to increasing the prediction accuracy of these algorithms; yet fewer efforts are dedicated to better engaging users through interactive visualizations in decision-making using these data. We present an investigation of the acceptance of a sensor-based fall risk assessment wearable device. A participatory design was employed to develop a mobile interface providing visualizations of sensor data and algorithmic assessments of fall risks. We then investigated the acceptance of this interface and its potential to motivate behavioural changes through a field deployment, which suggested that the interface and its belt-mounted wearable sensors are perceived as usable. We also found that providing contextual information for fall risk estimation combined with relevant practical fall prevention instructions may facilitate the acceptance of such technologies, potentially leading to behaviour change. | false | false | [
"Alan Yusheng Wu",
"Cosmin Munteanu"
] | [] | [] | [] |
CHI | 2,018 | Using Animation to Alleviate Overdraw in Multiclass Scatterplot Matrices | 10.1145/3173574.3173991 | The scatterplot matrix (SPLOM) is a commonly used technique for visualizing multiclass multivariate data. However, multiclass SPLOMs have issues with overdraw (overlapping points), and most existing techniques for alleviating overdraw focus on individual scatterplots with a single class. This paper explores whether animation using flickering points is an effective way to alleviate overdraw in these multiclass SPLOMs. In a user study with 69 participants, we found that users not only performed better at identifying dense regions using animated SPLOMs, but also found them easier to interpret and preferred them to static SPLOMs. These results open up new directions for future work on alleviating overdraw for multiclass SPLOMs, and provide insights for applying animation to alleviate overdraw in other settings. | false | false | [
"Helen Chen",
"Sophie Engle",
"Alark Joshi",
"Eric D. Ragan",
"Beste F. Yuksel",
"Lane Harrison"
] | [] | [] | [] |
CHI | 2,018 | Visualizing API Usage Examples at Scale | 10.1145/3173574.3174154 | Using existing APIs properly is a key challenge in programming, given that libraries and APIs are increasing in number and complexity. Programmers often search for online code examples in Q&A forums and read tutorials and blog posts to learn how to use a given API. However, there are often a massive number of related code examples and it is difficult for a user to understand the commonalities and variances among them, while being able to drill down to concrete details. We introduce an interactive visualization for exploring a large collection of code examples mined from open-source repositories at scale. This visualization summarizes hundreds of code examples in one synthetic code skeleton with statistical distributions for canonicalized statements and structures enclosing an API call. We implemented this interactive visualization for a set of Java APIs and found that, in a lab study, it helped users (1) answer significantly more API usage questions correctly and comprehensively and (2) explore how other programmers have used an unfamiliar API. | false | false | [
"Elena L. Glassman",
"Tianyi Zhang 0001",
"Björn Hartmann",
"Miryung Kim"
] | [] | [] | [] |
CHI | 2,018 | vrSocial: Toward Immersive Therapeutic VR Systems for Children with Autism | 10.1145/3173574.3173778 | Social communication frequently includes nuanced nonverbal communication cues, including eye contact, gestures, facial expressions, body language, and tone of voice. This type of communication is central to face-to-face interaction, but can be challenging for children and adults with autism. Innovative technologies can provide support by augmenting human-delivered cuing and automated prompting. Specifically, immersive virtual reality (VR) offers an option to generalize social skill interventions by concretizing nonverbal information in real-time social interactions. In this work, we explore the design and evaluation of three nonverbal communication applications in immersive VR. The results of this work indicate that delivering real-time visualizations of proximity, speaker volume, and duration of one's speech is feasible in immersive VR and effective for real-time support for proximity regulation for children with autism. We conclude with design considerations for therapeutic VR systems. | false | false | [
"LouAnne E. Boyd",
"Saumya Gupta",
"Sagar B. Vikmani",
"Carlos M. Gutierrez",
"Junxiang Yang",
"Erik Linstead",
"Gillian R. Hayes"
] | [] | [] | [] |
CHI | 2,018 | What Moves Players?: Visual Data Exploration of Twitter and Gameplay Data | 10.1145/3173574.3174134 | In recent years, microblogging platforms have not only become an important communication channel for the game industry to generate and uphold audience interest but also a rich resource for gauging player opinion. In this paper we use data gathered from Twitter to examine which topics matter to players and to identify influential members of a game's community. By triangulating in-game data with Twitter activity we explore how tweets can provide contextual information for understanding fluctuations in in-game activity. To facilitate analysis of the data we introduce a visual data exploration tool and use it to analyze tweets related to the game Destiny. In total, we collected over one million tweets from about 250,000 users over a 14-month period and gameplay data from roughly 3,500 players over a six-month period. | false | false | [
"Christian Drescher",
"Guenter Wallner",
"Simone Kriglstein",
"Rafet Sifa",
"Anders Drachen",
"Margit Pohl"
] | [] | [] | [] |
CHI | 2,018 | What's the Difference?: Evaluating Variations of Multi-Series Bar Charts for Visual Comparison Tasks | 10.1145/3173574.3173878 | An increasingly common approach to data analysis involves using information dashboards to visually compare changing data. However, layout constraints coupled with varying levels of visualization literacy among dashboard users make facilitating visual comparison in dashboards a challenging task. In this paper, we evaluate variants of bar charts, one of the most prevalent class of charts used in dashboards. We report an online experiment (N = 74) conducted to evaluate four alternative designs: 1) grouped bar chart, 2) grouped bar chart with difference overlays, 3) bar chart with difference overlays, and 4) difference bar chart. Results show that charts with difference overlays facilitate a wider range of comparison tasks while performing comparably to charts without them on individual tasks. Finally, we discuss the implications of our findings, with a focus on supporting visual comparison in dashboards. | false | false | [
"Arjun Srinivasan",
"Matthew Brehmer",
"Bongshin Lee",
"Steven Mark Drucker"
] | [] | [] | [] |
CHI | 2,018 | When David Meets Goliath: Combining Smartwatches with a Large Vertical Display for Visual Data Exploration | 10.1145/3173574.3173593 | We explore the combination of smartwatches and a large interactive display to support visual data analysis. These two extremes of interactive surfaces are increasingly popular, but feature different characteristics-display and input modalities, personal/public use, performance, and portability. In this paper, we first identify possible roles for both devices and the interplay between them through an example scenario. We then propose a conceptual framework to enable analysts to explore data items, track interaction histories, and alter visualization configurations through mechanisms using both devices in combination. We validate an implementation of our framework through a formative evaluation and a user study. The results show that this device combination, compared to just a large display, allows users to develop complex insights more fluidly by leveraging the roles of the two devices. Finally, we report on the interaction patterns and interplay between the devices for visual exploration as observed during our study. | false | false | [
"Tom Horak",
"Sriram Karthik Badam",
"Niklas Elmqvist",
"Raimund Dachselt"
] | [
"HM"
] | [] | [] |
VAST | 2,017 | A Utility-Aware Visual Approach for Anonymizing Multi-Attribute Tabular Data | 10.1109/TVCG.2017.2745139 | Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach. | false | false | [
"Xumeng Wang",
"Jia-Kai Chou",
"Wei Chen 0001",
"Huihua Guan",
"Wenlong Chen",
"Tianyi Lao",
"Kwan-Liu Ma"
] | [] | [] | [] |
VAST | 2,017 | A Visual Analytics System for Optimizing Communications in Massively Parallel Applications | 10.1109/VAST.2017.8585646 | Current and future supercomputers have tens of thousands of compute nodes interconnected with high-dimensional networks and complex network topologies for improved performance. Application developers are required to write scalable parallel programs in order to achieve high throughput on these machines. Application performance is largely determined by efficient inter-process communication. A common way to analyze and optimize performance is through profiling parallel codes to identify communication bottlenecks. However, understanding gigabytes of profiled at a is not a trivial task. In this paper, we present a visual analytics system for identifying the scalability bottlenecks and improving the communication efficiency of massively parallel applications. Visualization methods used in this system are designed to comprehend large-scale and varied communication patterns on thousands of nodes in complex networks such as the 5D torus and the dragonfly. We also present efficient rerouting and remapping algorithms that can be coupled with our interactive visual analytics design for performance optimization. We demonstrate the utility of our system with several case studies using three benchmark applications on two leading supercomputers. The mapping suggestion from our system led to 38% improvement in hop-bytes for Mini AMR application on 4,096 MPI processes. | false | false | [
"Takanori Fujiwara",
"Preeti Malakar",
"Khairi Reda",
"Venkatram Vishwanath",
"Michael E. Papka",
"Kwan-Liu Ma"
] | [] | [] | [] |
VAST | 2,017 | A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations | 10.1109/VAST.2017.8585720 | Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages “instance-level explanations”, measures of local feature relevance that explain single instances, and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct / incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns. The workflow is derived from a long-term collaboration with a group of machine learning and healthcare professionals who used our method to make sense of machine learning models they developed. The case study from this collaboration demonstrates that the proposed workflow helps experts derive useful knowledge about the model and the phenomena it describes, thus experts can generate useful hypotheses on how a model can be improved. | false | false | [
"Josua Krause",
"Aritra Dasgupta",
"Jordan Swartz",
"Yindalon Aphinyanagphongs",
"Enrico Bertini"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1705.01968v3",
"icon": "paper"
}
] |
VAST | 2,017 | ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models | 10.1109/TVCG.2017.2744718 | While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance-and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models. | false | false | [
"Minsuk Kahng",
"Pierre Y. Andrews",
"Aditya Kalro",
"Polo Chau"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1704.01942v2",
"icon": "paper"
}
] |
VAST | 2,017 | Analyzing the Training Processes of Deep Generative Models | 10.1109/TVCG.2017.2744938 | Among the many types of deep models, deep generative models (DGMs) provide a solution to the important problem of unsupervised and semi-supervised learning. However, training DGMs requires more skill, experience, and know-how because their training is more complex than other types of deep models such as convolutional neural networks (CNNs). We develop a visual analytics approach for better understanding and diagnosing the training process of a DGM. To help experts understand the overall training process, we first extract a large amount of time series data that represents training dynamics (e.g., activation changes over time). A blue-noise polyline sampling scheme is then introduced to select time series samples, which can both preserve outliers and reduce visual clutter. To further investigate the root cause of a failed training process, we propose a credit assignment algorithm that indicates how other neurons contribute to the output of the neuron causing the training failure. Two case studies are conducted with machine learning experts to demonstrate how our approach helps understand and diagnose the training processes of DGMs. We also show how our approach can be directly used to analyze other types of deep models, such as CNNs. | false | false | [
"Mengchen Liu",
"Jiaxin Shi",
"Kelei Cao",
"Jun Zhu 0001",
"Shixia Liu"
] | [] | [] | [] |
VAST | 2,017 | Applying Pragmatics Principles for Interaction with Visual Analytics | 10.1109/TVCG.2017.2744684 | Interactive visual data analysis is most productive when users can focus on answering the questions they have about their data, rather than focusing on how to operate the interface to the analysis tool. One viable approach to engaging users in interactive conversations with their data is a natural language interface to visualizations. These interfaces have the potential to be both more expressive and more accessible than other interaction paradigms. We explore how principles from language pragmatics can be applied to the flow of visual analytical conversations, using natural language as an input modality. We evaluate the effectiveness of pragmatics support in our system Evizeon, and present design considerations for conversation interfaces to visual analytics tools. | false | false | [
"Enamul Hoque",
"Vidya Setlur",
"Melanie Tory",
"Isaac Dykeman"
] | [] | [] | [] |
VAST | 2,017 | Beyond Tasks: An Activity Typology for Visual Analytics | 10.1109/TVCG.2017.2745180 | As Visual Analytics (VA) research grows and diversifies to encompass new systems, techniques, and use contexts, gaining a holistic view of analytic practices is becoming ever more challenging. However, such a view is essential for researchers and practitioners seeking to develop systems for broad audiences that span multiple domains. In this paper, we interpret VA research through the lens of Activity Theory (AT) - a framework for modelling human activities that has been influential in the field of Human-Computer Interaction. We first provide an overview of Activity Theory, showing its potential for thinking beyond tasks, representations, and interactions to the broader systems of activity in which interactive tools are embedded and used. Next, we describe how Activity Theory can be used as an organizing framework in the construction of activity typologies, building and expanding upon the tradition of abstract task taxonomies in the field of Information Visualization. We then apply the resulting process to create an activity typology for Visual Analytics, synthesizing a wide range of systems and activity concepts from the literature. Finally, we use this typology as the foundation of an activity-centered design process, highlighting both tensions and opportunities in the design space of VA systems. | false | false | [
"Darren Edge",
"Nathalie Henry Riche",
"Jonathan Larson",
"Christopher M. White"
] | [] | [] | [] |
VAST | 2,017 | BiDots: Visual Exploration of Weighted Biclusters | 10.1109/TVCG.2017.2744458 | 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. | false | false | [
"Jian Zhao 0010",
"Maoyuan Sun",
"Francine Chen 0001",
"Patrick Chiu"
] | [] | [] | [] |
VAST | 2,017 | Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis | 10.1109/TVCG.2017.2745181 | Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach. | false | false | [
"Manuel Stein",
"Halldór Janetzko",
"Andreas Lamprecht",
"Thorsten Breitkreutz",
"Philipp Zimmermann",
"Bastian Goldlücke",
"Tobias Schreck",
"Gennady L. Andrienko",
"Michael Grossniklaus",
"Daniel A. Keim"
] | [] | [] | [] |
VAST | 2,017 | Clustering Trajectories by Relevant Parts for Air Traffic Analysis | 10.1109/TVCG.2017.2744322 | Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering. | false | false | [
"Gennady L. Andrienko",
"Natalia V. Andrienko",
"Georg Fuchs",
"Jose Manuel Cordero Garcia"
] | [] | [] | [] |
VAST | 2,017 | Clustervision: Visual Supervision of Unsupervised Clustering | 10.1109/TVCG.2017.2745085 | Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data. | false | false | [
"Bum Chul Kwon",
"Benjamin Eysenbach",
"Janu Verma",
"Kenney Ng",
"Christopher deFilippi",
"Walter F. Stewart",
"Adam Perer"
] | [] | [] | [] |
VAST | 2,017 | Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study | 10.1109/TVCG.2017.2744818 | Labeling data instances is an important task in machine learning and visual analytics. Both fields provide a broad set of labeling strategies, whereby machine learning (and in particular active learning) follows a rather model-centered approach and visual analytics employs rather user-centered approaches (visual-interactive labeling). Both approaches have individual strengths and weaknesses. In this work, we conduct an experiment with three parts to assess and compare the performance of these different labeling strategies. In our study, we (1) identify different visual labeling strategies for user-centered labeling, (2) investigate strengths and weaknesses of labeling strategies for different labeling tasks and task complexities, and (3) shed light on the effect of using different visual encodings to guide the visual-interactive labeling process. We further compare labeling of single versus multiple instances at a time, and quantify the impact on efficiency. We systematically compare the performance of visual interactive labeling with that of active learning. Our main findings are that visual-interactive labeling can outperform active learning, given the condition that dimension reduction separates well the class distributions. Moreover, using dimension reduction in combination with additional visual encodings that expose the internal state of the learning model turns out to improve the performance of visual-interactive labeling. | false | false | [
"Jürgen Bernard",
"Marco Hutter 0002",
"Matthias Zeppelzauer",
"Dieter W. Fellner",
"Michael Sedlmair"
] | [] | [] | [] |
VAST | 2,017 | ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding | 10.1109/TVCG.2017.2744478 | Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building a concept from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of natural language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides a user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts, we introduce a bipolar concept model and support for specifying irrelevant words. We validate the interactive lexicon building interface by a user study and expert reviews. Quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones. | false | false | [
"Deok Gun Park 0001",
"Seungyeon Kim",
"Jurim Lee",
"Jaegul Choo",
"Nicholas Diakopoulos",
"Niklas Elmqvist"
] | [] | [] | [] |
VAST | 2,017 | CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization | 10.1109/VAST.2017.8585484 | We present CRICTO, a new crowdsourcing visual analytics environment for making sense of and analyzing text data, whereby multiple crowdworkers are able to parallelize the simple information schematization tasks of relating and connecting entities across documents. The diverse links from these schematization tasks are then automatically combined and the system visualizes them based on the semantic types of the linkages. CRICTO also includes several tools that allow analysts to interactively explore and refine crowdworkers' results to better support their own sensemaking processes. We evaluated CRICTO's techniques and analysis workflow with deployments of CRICTO using Amazon Mechanical Turk and a user study that assess the effect of crowdsourced schematization in sensemaking tasks. The results of our evaluation show that CRICTO's crowdsourcing approaches and workflow help analysts explore diverse aspects of datasets, and uncover more accurate hidden stories embedded in the text datasets. | false | false | [
"Haeyong Chung",
"Sai Prashanth Dasari",
"Santhosh Nandhakumar",
"Christopher Andrews"
] | [] | [] | [] |
VAST | 2,017 | CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data | 10.1109/VAST.2017.8585658 | Social media data bear valuable insights regarding events that occur around the world. Events are inherently temporal and spatial. Existing visual text analysis systems have focused on detecting and analyzing past and ongoing events. Few have leveraged social media information to look for events that may occur in the future. In this paper, we present an interactive visual analytic system, CrystalBall, that automatically identifies and ranks future events from Twitter streams. CrystalBall integrates new methods to discover events with interactive visualizations that permit sensemaking of the identified future events. Our computational methods integrate seven different measures to identify and characterize future events, leveraging information regarding time, location, social networks, and the informativeness of the messages. A visual interface is tightly coupled with the computational methods to present a concise summary of the possible future events. A novel connection graph and glyphs are designed to visualize the characteristics of the future events. To demonstrate the efficacy of CrystalBall in identifying future events and supporting interactive analysis, we present multiple case studies and validation studies on analyzing events derived from Twitter data. | false | false | [
"Isaac Cho",
"Ryan Wesslen",
"Svitlana Volkova",
"William Ribarsky",
"Wenwen Dou"
] | [] | [] | [] |
VAST | 2,017 | DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks | 10.1109/TVCG.2017.2744358 | Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems. | false | false | [
"Nicola Pezzotti",
"Thomas Höllt",
"Jan C. van Gemert",
"Boudewijn P. F. Lelieveldt",
"Elmar Eisemann",
"Anna Vilanova"
] | [] | [] | [] |
VAST | 2,017 | Do Convolutional Neural Networks Learn Class Hierarchy? | 10.1109/TVCG.2017.2744683 | Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data. | false | false | [
"Bilal Alsallakh",
"Amin Jourabloo",
"Mao Ye",
"Xiaoming Liu 0002",
"Ren Liu"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1710.06501v1",
"icon": "paper"
}
] |
VAST | 2,017 | Dynamic Influence Networks for Rule-Based Models | 10.1109/TVCG.2017.2745280 | We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle. | false | false | [
"Angus G. Forbes",
"Andrew Thomas Burks",
"Kristine Lee",
"Xing Li",
"Pierre Boutillier",
"Jean Krivine",
"Walter Fontana"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1711.00967v1",
"icon": "paper"
}
] |
VAST | 2,017 | E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media | 10.1109/VAST.2017.8585638 | 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. | false | false | [
"Siming Chen 0001",
"Shuai Chen 0001",
"Lijing Lin",
"Xiaoru Yuan",
"Christy Jie Liang",
"Xiaolong Zhang 0001"
] | [] | [] | [] |
VAST | 2,017 | EVA: Visual Analytics to Identify Fraudulent Events | 10.1109/TVCG.2017.2744758 | Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. Visual Analytics techniques have potential to considerably enhance the knowledge discovery process and increase the detection and prediction accuracy of financial fraud detection systems. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms. | false | false | [
"Roger A. Leite",
"Theresia Gschwandtner",
"Silvia Miksch",
"Simone Kriglstein",
"Margit Pohl",
"Erich Gstrein",
"Johannes Kuntner"
] | [] | [] | [] |
VAST | 2,017 | EventThread: Visual Summarization and Stage Analysis of Event Sequence Data | 10.1109/TVCG.2017.2745320 | Event sequence data such as electronic health records, a person's academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user. | false | false | [
"Shunan Guo",
"Ke Xu",
"Rongwen Zhao",
"David Gotz",
"Hongyuan Zha",
"Nan Cao"
] | [] | [] | [] |
VAST | 2,017 | Graphiti: Interactive Specification of Attribute-Based Edges for Network Modeling and Visualization | 10.1109/TVCG.2017.2744843 | Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types. | false | false | [
"Arjun Srinivasan",
"Hyunwoo Park",
"Alex Endert",
"Rahul C. Basole"
] | [] | [] | [] |
VAST | 2,017 | How Do Ancestral Traits Shape Family Trees Over Generations? | 10.1109/TVCG.2017.2744080 | 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). | false | false | [
"Siwei Fu",
"Hao Dong 0008",
"Weiwei Cui",
"Jian Zhao 0010",
"Huamin Qu"
] | [] | [] | [] |
VAST | 2,017 | Interactive Visual Alignment of Medieval Text Versions | 10.1109/VAST.2017.8585505 | Textual criticism consists of the identification and analysis of variant readings among different versions of a text. Being a relatively simple task for modern languages, the collation of medieval text traditions ranges from the complex to the virtually impossible depending on the degree of instability of textual transmission. We present a visual analytics environment that supports computationally aligning such complex textual differences typical of orally inflected medieval poetry. For the purpose of analyzing alignment, we provide interactive visualizations for different text hierarchy levels, specifically, a meso reading view to support investigating repetition and variance at the line level across text segments. In addition to outlining important aspects of our interdisciplinary collaboration, we emphasize the utility of the proposed system by various usage scenarios in medieval French literature. | false | false | [
"Stefan Jänicke",
"David Joseph Wrisley"
] | [] | [] | [] |
VAST | 2,017 | LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets | 10.1109/TVCG.2017.2744098 | Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the$x$axis and$y$axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS ($x$axis) and the variation of LTS in structures (the combination of$x$axis and$y$axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach. | false | false | [
"Jiazhi Xia",
"Fenjin Ye",
"Wei Chen 0001",
"Yusi Wang",
"Weifeng Chen 0002",
"Yuxin Ma",
"Anthony K. H. Tung"
] | [] | [] | [] |
VAST | 2,017 | Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces | 10.1109/VAST.2017.8585613 | Subspace analysis methods have gained interest for identifying patterns in subspaces of high-dimensional data. Existing techniques allow to visualize and compare patterns in subspaces. However, many subspace analysis methods produce an abundant amount of patterns, which often remain redundant and are difficult to relate. Creating effective layouts for comparison of subspace patterns remains challenging. We introduce Pattern Trails, a novel approach for visually ordering and comparing subspace patterns. Central to our approach is the notion of pattern transitions as an interpretable structure imposed to order and compare patterns between subspaces. The basic idea is to visualize projections of subspaces side-by-side, and indicate changes between adjacent patterns in the subspaces by a linked representation, hence introducing pattern transitions. Our contributions comprise a systematization for how pairs of subspace patterns can be compared, and how changes can be interpreted in terms of pattern transitions. We also contribute a technique for visual subspace analysis based on a data-driven similarity measure between subspace representations. This measure is useful to order the patterns, and interactively group subspaces to reduce redundancy. We demonstrate the usefulness of our approach by application to several use cases, indicating that data can be meaningfully ordered and interpreted in terms of pattern transitions. | false | false | [
"Dominik Jäckle",
"Michael Blumenschein",
"Michael Behrisch 0001",
"Daniel A. Keim",
"Tobias Schreck"
] | [] | [] | [] |
VAST | 2,017 | PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models | 10.1109/TVCG.2017.2745118 | PhenoLines is a visual analysis tool for the interpretation of disease subtypes, derived from the application of topic models to clinical data. Topic models enable one to mine cross-sectional patient comorbidity data (e.g., electronic health records) and construct disease subtypes-each with its own temporally evolving prevalence and co-occurrence of phenotypes-without requiring aligned longitudinal phenotype data for all patients. However, the dimensionality of topic models makes interpretation challenging, and de facto analyses provide little intuition regarding phenotype relevance or phenotype interrelationships. PhenoLines enables one to compare phenotype prevalence within and across disease subtype topics, thus supporting subtype characterization, a task that involves identifying a proposed subtype's dominant phenotypes, ages of effect, and clinical validity. We contribute a data transformation workflow that employs the Human Phenotype Ontology to hierarchically organize phenotypes and aggregate the evolving probabilities produced by topic models. We introduce a novel measure of phenotype relevance that can be used to simplify the resulting topology. The design of PhenoLines was motivated by formative interviews with machine learning and clinical experts. We describe the collaborative design process, distill high-level tasks, and report on initial evaluations with machine learning experts and a medical domain expert. These results suggest that PhenoLines demonstrates promising approaches to support the characterization and optimization of topic models. | false | false | [
"Michael Glueck",
"Mahdi Pakdaman Naeini",
"Finale Doshi-Velez",
"Fanny Chevalier",
"Azam Khan",
"Daniel J. Wigdor",
"Michael Brudno"
] | [] | [] | [] |
VAST | 2,017 | Podium: Ranking Data Using Mixed-Initiative Visual Analytics | 10.1109/TVCG.2017.2745078 | People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user's data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user's subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings. Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas. | false | false | [
"Emily Wall",
"Subhajit Das 0002",
"Ravish Chawla",
"Bharath Kalidindi",
"Eli T. Brown",
"Alex Endert"
] | [] | [] | [] |
VAST | 2,017 | Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework | 10.1109/TVCG.2017.2745080 | Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures. | false | false | [
"Mennatallah El-Assady",
"Rita Sevastjanova",
"Fabian Sperrle",
"Daniel A. Keim",
"Christopher Collins 0001"
] | [
"HM"
] | [] | [] |
VAST | 2,017 | QSAnglyzer: Visual Analytics for Prismatic Analysis of Question Answering System Evaluations | 10.1109/VAST.2017.8585733 | Developing sophisticated artificial intelligence (AI) systems requires AI researchers to experiment with different designs and analyze results from evaluations (we refer this task as evaluation analysis). In this paper, we tackle the challenges of evaluation analysis in the domain of question-answering (QA) systems. Through in-depth studies with QA researchers, we identify tasks and goals of evaluation analysis and derive a set of design rationales, based on which we propose a novel approach termed prismatic analysis. Prismatic analysis examines data through multiple ways of categorization (referred as angles). Categories in each angle are measured by aggregate metrics to enable diverse comparison scenarios. To facilitate prismatic analysis of QA evaluations, we design and implement the Question Space Anglyzer (QSAnglyzer), a visual analytics (VA) tool. In QSAnglyzer, the high-dimensional space formed by questions is divided into categories based on several angles (e.g., topic and question type). Each category is aggregated by accuracy, the number of questions, and accuracy variance across evaluations. QSAnglyzer visualizes these angles so that QA researchers can examine and compare evaluations from various aspects both individually and collectively. Furthermore, QA researchers filter questions based on any angle by clicking to construct complex queries. We validate QSAnglyzer through controlled experiments and by expert reviews. The results indicate that when using QSAnglyzer, users perform analysis tasks faster (p <; 0.01) and more accurately (p <; 0.05), and are quick to gain new insight. We discuss how prismatic analysis and QSAnglyzer scaffold evaluation analysis, and provide directions for future research. | false | false | [
"Nan-Chen Chen",
"Been Kim"
] | [] | [] | [] |
VAST | 2,017 | Sequence Synopsis: Optimize Visual Summary of Temporal Event Data | 10.1109/TVCG.2017.2745083 | Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback. | false | false | [
"Yuanzhe Chen",
"Panpan Xu",
"Ren Liu"
] | [] | [] | [] |
VAST | 2,017 | SkyLens: Visual Analysis of Skyline on Multi-Dimensional Data | 10.1109/TVCG.2017.2744738 | Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens. | false | false | [
"Xun Zhao",
"Yanhong Wu",
"Weiwei Cui",
"Xinnan Du",
"Yuan Chen",
"Yong Wang 0021",
"Dik Lun Lee",
"Huamin Qu"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1708.03462v2",
"icon": "paper"
}
] |
VAST | 2,017 | SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance | 10.1109/TVCG.2017.2744805 | Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself. | false | false | [
"Dominik Sacha",
"Matthias Kraus",
"Jürgen Bernard",
"Michael Behrisch 0001",
"Tobias Schreck",
"Yuki Asano",
"Daniel A. Keim"
] | [] | [] | [] |
VAST | 2,017 | Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs | 10.1109/TVCG.2017.2745279 | 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. | false | false | [
"Jian Zhao 0010",
"Michael Glueck",
"Petra Isenberg",
"Fanny Chevalier",
"Azam Khan"
] | [
"HM"
] | [] | [] |
VAST | 2,017 | The "y" of it Matters, Even for Storyline Visualization | 10.1109/VAST.2017.8585487 | Storylines are adept at communicating complex change by encoding time on the x-axis and using the proximity of lines in the y direction to represent interaction between entities. The original definition of a storyline visualization requires data defined in terms of explicit interaction groups. Relaxing this definition allows storyline visualization to be applied more generally, but this creates questions about how the y-coordinate should encode interactions when this is tied to a particular place or state. To answer this question, we conducted a design study where we considered two layout algorithm design alternatives within a geo-temporal analysis tool written to solve part of the VAST Challenge 2014. We measured the performance of users at overview and detail oriented tasks between two storyline layout algorithms. To the best of our knowledge, this paper is the first work to question the design principles for storyline visualization, and what we found surprised us. For overview tasks with the alternative layout, which has a consistent encoding for the y-coordinate, users performed moderately better (p <; .05) than the storyline layout based on existing design constraints and aesthetic criteria. Our empirical findings were also supported by first-hand accounts taken from interviews with multiple expert analysts, who suggested that the inconsistent meaning of the y-axis was misleading. These findings led us to design a new storyline layout algorithm that is a “best of both” where the y-axis has a consistent meaning but aesthetic criteria (e.g., line crossings) are considered. | false | false | [
"Dustin Arendt",
"Meg Pirrung"
] | [] | [] | [] |
VAST | 2,017 | The Anchoring Effect in Decision-Making with Visual Analytics | 10.1109/VAST.2017.8585665 | Anchoring effect is the tendency to focus too heavily on one piece of information when making decisions. In this paper, we present a novel, systematic study and resulting analyses that investigate the effects of anchoring effect on human decision-making using visual analytic systems. Visual analytics interfaces typically contain multiple views that present various aspects of information such as spatial, temporal, and categorical. These views are designed to present complex, heterogeneous data in accessible forms that aid decision-making. However, human decision-making is often hindered by the use of heuristics, or cognitive biases, such as anchoring effect. Anchoring effect can be triggered by the order in which information is presented or the magnitude of information presented. Through carefully designed laboratory experiments, we present evidence of anchoring effect in analysis with visual analytics interfaces when users are primed by representation of different pieces of information. We also describe detailed analyses of users' interaction logs which reveal the impact of anchoring bias on the visual representation preferred and paths of analysis. We discuss implications for future research to possibly detect and alleviate anchoring bias. | false | false | [
"Isaac Cho",
"Ryan Wesslen",
"Alireza Karduni",
"Sashank Santhanam",
"Samira Shaikh",
"Wenwen Dou"
] | [] | [] | [] |
VAST | 2,017 | The Interactive Visualization Gap in Initial Exploratory Data Analysis | 10.1109/TVCG.2017.2743990 | Data scientists and other analytic professionals often use interactive visualization in the dissemination phase at the end of a workflow during which findings are communicated to a wider audience. Visualization scientists, however, hold that interactive representation of data can also be used during exploratory analysis itself. Since the use of interactive visualization is optional rather than mandatory, this leaves a “visualization gap” during initial exploratory analysis that is the onus of visualization researchers to fill. In this paper, we explore areas where visualization would be beneficial in applied research by conducting a design study using a novel variation on contextual inquiry conducted with professional data analysts. Based on these interviews and experiments, we propose a set of interactive initial exploratory visualization guidelines which we believe will promote adoption by this type of user. | false | false | [
"Andrea Batch",
"Niklas Elmqvist"
] | [] | [] | [] |
VAST | 2,017 | The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics | 10.1109/VAST.2017.8585498 | Visual Analytics (VA) aims to combine the strengths of humans and computers for effective data analysis. In this endeavor, humans' tacit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the analytic process. While VA environments are starting to include features to formalize, store, and utilize such knowledge, the mechanisms and degree in which these environments integrate explicit knowledge varies widely. Additionally, this important class of VA environments has never been elaborated on by existing work on VA theory. This paper proposes a conceptual model of Knowledge-assisted VA conceptually grounded on the visualization model by van Wijk. We apply the model to describe various examples of knowledge-assisted VA from the literature and elaborate on three of them in finer detail. Moreover, we illustrate the utilization of the model to compare different design alternatives and to evaluate existing approaches with respect to their use of knowledge. Finally, the model can inspire designers to generate novel VA environments using explicit knowledge effectively. | false | false | [
"Paolo Federico 0001",
"Markus Wagner 0008",
"Alexander Rind",
"Albert Amor-Amoros",
"Silvia Miksch",
"Wolfgang Aigner"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "https://research.fhstp.ac.at/content/download/89486/file/federico-wagner_2017_knava-model.pdf",
"icon": "paper"
}
] |
VAST | 2,017 | Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics | 10.1109/TVCG.2017.2745258 | Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms. | false | false | [
"John E. Wenskovitch",
"Ian Crandell",
"Naren Ramakrishnan",
"Leanna House",
"Scotland Leman",
"Chris North 0001"
] | [] | [] | [] |
VAST | 2,017 | TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees | 10.1109/TVCG.2017.2745158 | Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees. | false | false | [
"Thomas Mühlbacher",
"Lorenz Linhardt",
"Torsten Möller",
"Harald Piringer"
] | [] | [] | [] |
VAST | 2,017 | Understanding a Sequence of Sequences: Visual Exploration of Categorical States in Lake Sediment Cores | 10.1109/TVCG.2017.2744686 | This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analysis of lake sediment cores. The goal is to gain hypotheses about landscape evolution and climate conditions in the past. To this end, geoscientists identify which categorical sequences are similar in the sense that they indicate similar conditions. Categorical sequences are similar if they have similar meaning (semantic similarity) and appear in similar time periods (temporal similarity). For data sets with many different categorical sequences, the task to identify similar sequences becomes a challenge. Our contribution is a tailored visual analysis concept that effectively supports the analytical process. Our visual interface comprises coupled visualizations of semantics and temporal context for the exploration and assessment of the similarity of categorical sequences. Integrated automatic methods reduce the analytical effort substantially. They (1) extract unique sequences in the data and (2) rank sequences by a similarity measure during the search for similar sequences. We evaluated our concept by demonstrations of our prototype to a larger audience and hands-on analysis sessions for two different lakes. According to geoscientists, our approach fills an important methodological gap in the application domain. | false | false | [
"Andrea Unger",
"Nadine Drager",
"Mike Sips",
"Dirk J. Lehmann"
] | [] | [] | [] |
VAST | 2,017 | Understanding Hidden Memories of Recurrent Neural Networks | 10.1109/VAST.2017.8585721 | Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts. | false | false | [
"Yao Ming",
"Shaozu Cao",
"Ruixiang Zhang",
"Zhen Li 0044",
"Yuanzhe Chen",
"Yangqiu Song",
"Huamin Qu"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1710.10777v1",
"icon": "paper"
}
] |
VAST | 2,017 | Understanding the Relationship Between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy | 10.1109/TVCG.2017.2744418 | The fields of operations research and computer science have long sought to find automatic solver techniques that can find high-quality solutions to difficult real-world optimisation problems. The traditional workflow is to exactly model the problem and then enter this model into a general-purpose “black-box” solver. In practice, however, many problems cannot be solved completely automatically, but require a “human-in-the-loop” to iteratively refine the model and give hints to the solver. In this paper, we explore the parallels between this interactive optimisation workflow and the visual analytics sense-making loop. We assert that interactive optimisation is essentially a visual analytics task and propose a problem-solving loop analogous to the sense-making loop. We explore these ideas through an in-depth analysis of a use-case in prostate brachytherapy, an application where interactive optimisation may be able to provide significant assistance to practitioners in creating prostate cancer treatment plans customised to each patient's tumour characteristics. However, current brachytherapy treatment planning is usually a careful, mostly manual process involving multiple professionals. We developed a prototype interactive optimisation tool for brachytherapy that goes beyond current practice in supporting focal therapy - targeting tumour cells directly rather than simply seeking coverage of the whole prostate gland. We conducted semi-structured interviews, in two stages, with seven radiation oncology professionals in order to establish whether they would prefer to use interactive optimisation for treatment planning and whether such a tool could improve their trust in the novel focal therapy approach and in machine generated solutions to the problem. | false | false | [
"Jie Liu",
"Tim Dwyer",
"Kim Marriott",
"Jeremy Millar",
"Annette Haworth"
] | [] | [] | [] |
VAST | 2,017 | VIGOR: Interactive Visual Exploration of Graph Query Results | 10.1109/TVCG.2017.2744898 | Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR's ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors. | false | false | [
"Robert S. Pienta",
"Fred Hohman",
"Alex Endert",
"Acar Tamersoy",
"Kevin A. Roundy",
"Christopher Gates 0002",
"Shamkant B. Navathe",
"Polo Chau"
] | [] | [] | [] |
VAST | 2,017 | Visual Causality Analysis Made Practical | 10.1109/VAST.2017.8585647 | Deriving the exact casual model that governs the relations between variables in a multidimensional dataset is difficult in practice. It is because causal inference algorithms by themselves typically cannot encode an adequate amount of domain knowledge to break all ties. Visual analytic approaches are considered a feasible alternative to fully automated methods. However, their application in real-world scenarios can be tedious. This paper focuses on these practical aspects of visual causality analysis. The most imperative of these aspects is posed by Simpson' Paradox. It implies the existence of multiple causal models differing in both structure and parameter depending on how the data is subdivided. We propose a comprehensive interface that engages human experts in identifying these subdivisions and allowing them to establish the corresponding causal models via a rich set of interactive facilities. Other features of our interface include: (1) a new causal network visualization that emphasizes the flow of causal dependencies, (2) a model scoring mechanism with visual hints for interactive model refinement, and (3) flexible approaches for handling heterogeneous data. Various real-world data examples are given. | false | false | [
"Jun Wang",
"Klaus Mueller 0001"
] | [] | [] | [] |
VAST | 2,017 | Visual Diagnosis of Tree Boosting Methods | 10.1109/TVCG.2017.2744378 | Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms. | false | false | [
"Shixia Liu",
"Jiannan Xiao",
"Junlin Liu",
"Xiting Wang",
"Jing Wu 0004",
"Jun Zhu 0001"
] | [] | [] | [] |
VAST | 2,017 | Visualizing Big Data Outliers Through Distributed Aggregation | 10.1109/TVCG.2017.2744685 | Visualizing outliers in massive datasets requires statistical pre-processing in order to reduce the scale of the problem to a size amenable to rendering systems like D3, Plotly or analytic systems like R or SAS. This paper presents a new algorithm, calledhdoutliers, for detecting multidimensional outliers. It is unique for a) dealing with a mixture of categorical and continuous variables, b) dealing with big-p (many columns of data), c) dealing with big-$n$(many rows of data), d) dealing with outliers that mask other outliers, and e) dealing consistently with unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers,hdoutliersis based on a distributional model that allows outliers to be tagged with a probability. This critical feature reduces the likelihood of false discoveries. | false | false | [
"Leland Wilkinson"
] | [] | [] | [] |
VAST | 2,017 | Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses | 10.1109/TVCG.2017.2745178 | In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of “Tropical Cyclone Karl”, guiding the user towards the cluster robustness information required for subsequent ensemble analysis. | false | false | [
"Alexander Kumpf",
"Bianca Tost",
"Marlene Baumgart",
"Michael Riemer",
"Rüdiger Westermann",
"Marc Rautenhaus"
] | [] | [] | [] |
VAST | 2,017 | Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow | 10.1109/TVCG.2017.2744878 | We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model's modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models. | false | false | [
"Kanit Wongsuphasawat",
"Daniel Smilkov",
"James Wexler",
"Jimbo Wilson",
"Dan Mané",
"Doug Fritz",
"Dilip Krishnan",
"Fernanda B. Viégas",
"Martin Wattenberg"
] | [
"BP"
] | [] | [] |
VAST | 2,017 | Visualizing Real-Time Strategy Games: The Example of StarCraft II | 10.1109/VAST.2017.8585594 | We present a visualization system for users to examine real-time strategy games, which have become very popular globally in recent years. Unlike previous systems that focus on showing statistics and build order, our system can depict the most important part - battles in the games. Specifically, we visualize detailed movements of armies belonging to respective nations on a map and enable users to examine battles from a global view to a local view. In the global view, battles are depicted by curved arrows revealing how the armies enter and escape from the battlefield. By observing the arrows and the height map, users can make sense of offensive and defensive strategies easily. In the local view, units of each type are rendered on the map, and their movements are represented by animation. We also render an attack line between a pair of units if one of them can attack the other to help users analyze the advantages and disadvantages of a particular formation. Accordingly, users can utilize our system to discover statistics, build order, and battles, and learn strategies from games played by professionals. | false | false | [
"Yen-Ting Kuan",
"Yu-Shuen Wang",
"Jung-Hong Chuang"
] | [] | [] | [] |
VAST | 2,017 | Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data | 10.1109/TVCG.2017.2744419 | The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity of identifying anomalies in large, dynamic and heterogeneous data, interpreting anomalies in their multifaceted, spatiotemporal context, and allowing users to provide feedback in the analysis loop. In this work, we introduce a unified visual interactive system and framework, Voila, for interactively detecting anomalies in spatiotemporal data collected from a streaming data source. The system is designed to meet two requirements in real-world applications, i.e., online monitoring and interactivity. We propose a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input. Using the “smart city” as an example scenario, we demonstrate the effectiveness of the proposed framework through quantitative evaluation and qualitative case studies. | false | false | [
"Nan Cao",
"Chaoguang Lin",
"Qiuhan Zhu",
"Yu-Ru Lin",
"Xian Teng",
"Xidao Wen"
] | [] | [] | [] |
VAST | 2,017 | Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive Visual Analytics | 10.1109/VAST.2017.8585669 | Visual analytic tools combine the complementary strengths of humans and machines in human-in-the-loop systems. Humans provide invaluable domain expertise and sensemaking capabilities to this discourse with analytic models; however, little consideration has yet been given to the ways inherent human biases might shape the visual analytic process. In this paper, we establish a conceptual framework for considering bias assessment through human-in-the-loop systems and lay the theoretical foundations for bias measurement. We propose six preliminary metrics to systematically detect and quantify bias from user interactions and demonstrate how the metrics might be implemented in an existing visual analytic system, InterAxis. We discuss how our proposed metrics could be used by visual analytic systems to mitigate the negative effects of cognitive biases by making users aware of biased processes throughout their analyses. | false | false | [
"Emily Wall",
"Leslie M. Blaha",
"Lyndsey Franklin",
"Alex Endert"
] | [] | [] | [] |
SciVis | 2,017 | A Virtual Reality Visualization Tool for Neuron Tracing | 10.1109/TVCG.2017.2744079 | Tracing neurons in large-scale microscopy data is crucial to establishing a wiring diagram of the brain, which is needed to understand how neural circuits in the brain process information and generate behavior. Automatic techniques often fail for large and complex datasets, and connectomics researchers may spend weeks or months manually tracing neurons using 2D image stacks. We present a design study of a new virtual reality (VR) system, developed in collaboration with trained neuroanatomists, to trace neurons in microscope scans of the visual cortex of primates. We hypothesize that using consumer-grade VR technology to interact with neurons directly in 3D will help neuroscientists better resolve complex cases and enable them to trace neurons faster and with less physical and mental strain. We discuss both the design process and technical challenges in developing an interactive system to navigate and manipulate terabyte-sized image volumes in VR. Using a number of different datasets, we demonstrate that, compared to widely used commercial software, consumer-grade VR presents a promising alternative for scientists. | false | false | [
"Will Usher 0001",
"Pavol Klacansky",
"Frederick Federer",
"Peer-Timo Bremer",
"Aaron Knoll",
"Jeff Yarch",
"Alessandra Angelucci",
"Valerio Pascucci"
] | [] | [] | [] |
SciVis | 2,017 | Abstractocyte: A Visual Tool for Exploring Nanoscale Astroglial Cells | 10.1109/TVCG.2017.2744278 | This paper presents Abstractocyte, a system for the visual analysis of astrocytes and their relation to neurons, in nanoscale volumes of brain tissue. Astrocytes are glial cells, i.e., non-neuronal cells that support neurons and the nervous system. The study of astrocytes has immense potential for understanding brain function. However, their complex and widely-branching structure requires high-resolution electron microscopy imaging and makes visualization and analysis challenging. Furthermore, the structure and function of astrocytes is very different from neurons, and therefore requires the development of new visualization and analysis tools. With Abstractocyte, biologists can explore the morphology of astrocytes using various visual abstraction levels, while simultaneously analyzing neighboring neurons and their connectivity. We define a novel, conceptual 2D abstraction space for jointly visualizing astrocytes and neurons. Neuroscientists can choose a specific joint visualization as a point in this space. Interactively moving this point allows them to smoothly transition between different abstraction levels in an intuitive manner. In contrast to simply switching between different visualizations, this preserves the visual context and correlations throughout the transition. Users can smoothly navigate from concrete, highly-detailed 3D views to simplified and abstracted 2D views. In addition to investigating astrocytes, neurons, and their relationships, we enable the interactive analysis of the distribution of glycogen, which is of high importance to neuroscientists. We describe the design of Abstractocyte, and present three case studies in which neuroscientists have successfully used our system to assess astrocytic coverage of synapses, glycogen distribution in relation to synapses, and astrocytic-mitochondria coverage. | false | false | [
"Haneen Mohammed",
"Ali K. Al-Awami",
"Johanna Beyer",
"Corrado Calì",
"Pierre J. Magistretti",
"Hanspeter Pfister",
"Markus Hadwiger"
] | [] | [] | [] |
SciVis | 2,017 | Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization | 10.1109/TVCG.2017.2744459 | Although visualization design models exist in the literature in the form of higher-level methodological frameworks, these models do not present a clear methodological prescription for the domain characterization step. This work presents a framework and end-to-end model for requirements engineering in problem-driven visualization application design. The framework and model are based on the activity-centered design paradigm, which is an enhancement of human-centered design. The proposed activity-centered approach focuses on user tasks and activities, and allows an explicit link between the requirements engineering process with the abstraction stage - and its evaluation - of existing, higher-level visualization design models. In a departure from existing visualization design models, the resulting model: assigns value to a visualization based on user activities; ranks user tasks before the user data; partitions requirements in activity-related capabilities and nonfunctional characteristics and constraints; and explicitly incorporates the user workflows into the requirements process. A further merit of this model is its explicit integration of functional specifications, a concept this work adapts from the software engineering literature, into the visualization design nested model. A quantitative evaluation using two sets of interdisciplinary projects supports the merits of the activity-centered model. The result is a practical roadmap to the domain characterization step of visualization design for problem-driven data visualization. Following this domain characterization model can help remove a number of pitfalls that have been identified multiple times in the visualization design literature. | false | false | [
"G. Elisabeta Marai"
] | [] | [] | [] |
SciVis | 2,017 | An Intelligent System Approach for Probabilistic Volume Rendering Using Hierarchical 3D Convolutional Sparse Coding | 10.1109/TVCG.2017.2744078 | In this paper, we propose a novel machine learning-based voxel classification method for highly-accurate volume rendering. Unlike conventional voxel classification methods that incorporate intensity-based features, the proposed method employs dictionary based features learned directly from the input data using hierarchical multi-scale 3D convolutional sparse coding, a novel extension of the state-of-the-art learning-based sparse feature representation method. The proposed approach automatically generates high-dimensional feature vectors in up to 75 dimensions, which are then fed into an intelligent system built on a random forest classifier for accurately classifying voxels from only a handful of selection scribbles made directly on the input data by the user. We apply the probabilistic transfer function to further customize and refine the rendered result. The proposed method is more intuitive to use and more robust to noise in comparison with conventional intensity-based classification methods. We evaluate the proposed method using several synthetic and real-world volume datasets, and demonstrate the methods usability through a user study. | false | false | [
"Tran Minh Quan",
"Junyoung Choi",
"Haejin Jeong",
"Won-Ki Jeong"
] | [] | [] | [] |
SciVis | 2,017 | BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI | 10.1109/TVCG.2017.2744479 | Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. We demonstrate the application of BASTet and OpenMSI in practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI. | false | false | [
"Oliver Rübel",
"Benjamin P. Bowen"
] | [] | [] | [] |
SciVis | 2,017 | Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks | 10.1109/TVCG.2017.2744321 | Complex networks require effective tools and visualizations for their analysis and comparison. Clique communities have been recognized as a powerful concept for describing cohesive structures in networks. We propose an approach that extends the computation of clique communities by considering persistent homology, a topological paradigm originally introduced to characterize and compare the global structure of shapes. Our persistence-based algorithm is able to detect clique communities and to keep track of their evolution according to different edge weight thresholds. We use this information to define comparison metrics and a new centrality measure, both reflecting the relevance of the clique communities inherent to the network. Moreover, we propose an interactive visualization tool based on nested graphs that is capable of compactly representing the evolving relationships between communities for different thresholds and clique degrees. We demonstrate the effectiveness of our approach on various network types. | false | false | [
"Bastian Rieck",
"Ulderico Fugacci",
"Jonas Lukasczyk",
"Heike Leitte"
] | [] | [] | [] |
SciVis | 2,017 | Decision Graph Embedding for High-Resolution Manometry Diagnosis | 10.1109/TVCG.2017.2744299 | High-resolution manometry is an imaging modality which enables the categorization of esophageal motility disorders. Spatio-temporal pressure data along the esophagus is acquired using a tubular device and multiple test swallows are performed by the patient. Current approaches visualize these swallows as individual instances, despite the fact that aggregated metrics are relevant in the diagnostic process. Based on the current Chicago Classification, which serves as the gold standard in this area, we introduce a visualization supporting an efficient and correct diagnosis. To reach this goal, we propose a novel decision graph representing the Chicago Classification with workflow optimization in mind. Based on this graph, we are further able to prioritize the different metrics used during diagnosis and can exploit this prioritization in the actual data visualization. Thus, different disorders and their related parameters are directly represented and intuitively influence the appearance of our visualization. Within this paper, we introduce our novel visualization, justify the design decisions, and provide the results of a user study we performed with medical students as well as a domain expert. On top of the presented visualization, we further discuss how to derive a visual signature for individual patients that allows us for the first time to perform an intuitive comparison between subjects, in the form of small multiples. | false | false | [
"Julian Kreiser",
"Alexander Hann",
"Eugen Zizer",
"Timo Ropinski"
] | [] | [] | [] |
SciVis | 2,017 | Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing | 10.1109/TVCG.2017.2744059 | We propose a dynamically load-balanced algorithm for parallel particle tracing, which periodically attempts to evenly redistribute particles across processes based on k-d tree decomposition. Each process is assigned with (1) a statically partitioned, axis-aligned data block that partially overlaps with neighboring blocks in other processes and (2) a dynamically determined k-d tree leaf node that bounds the active particles for computation; the bounds of the k-d tree nodes are constrained by the geometries of data blocks. Given a certain degree of overlap between blocks, our method can balance the number of particles as much as possible. Compared with other load-balancing algorithms for parallel particle tracing, the proposed method does not require any preanalysis, does not use any heuristics based on flow features, does not make any assumptions about seed distribution, does not move any data blocks during the run, and does not need any master process for work redistribution. Based on a comprehensive performance study up to 8K processes on a Blue Gene/Q system, the proposed algorithm outperforms baseline approaches in both load balance and scalability on various flow visualization and analysis problems. | false | false | [
"Jiang Zhang 0002",
"Hanqi Guo 0001",
"Fan Hong",
"Xiaoru Yuan",
"Tom Peterka"
] | [] | [] | [] |
SciVis | 2,017 | Globe Browsing: Contextualized Spatio-Temporal Planetary Surface Visualization | 10.1109/TVCG.2017.2743958 | Results of planetary mapping are often shared openly for use in scientific research and mission planning. In its raw format, however, the data is not accessible to non-experts due to the difficulty in grasping the context and the intricate acquisition process. We present work on tailoring and integration of multiple data processing and visualization methods to interactively contextualize geospatial surface data of celestial bodies for use in science communication. As our approach handles dynamic data sources, streamed from online repositories, we are significantly shortening the time between discovery and dissemination of data and results. We describe the image acquisition pipeline, the pre-processing steps to derive a 2.5D terrain, and a chunked level-of-detail, out-of-core rendering approach to enable interactive exploration of global maps and high-resolution digital terrain models. The results are demonstrated for three different celestial bodies. The first case addresses high-resolution map data on the surface of Mars. A second case is showing dynamic processes, such as concurrent weather conditions on Earth that require temporal datasets. As a final example we use data from the New Horizons spacecraft which acquired images during a single flyby of Pluto. We visualize the acquisition process as well as the resulting surface data. Our work has been implemented in the OpenSpace software [8], which enables interactive presentations in a range of environments such as immersive dome theaters, interactive touch tables, and virtual reality headsets. | false | false | [
"Karl Bladin",
"Emil Axelsson",
"Erik Broberg",
"Carter Emmart",
"Patric Ljung",
"Alexander Bock 0002",
"Anders Ynnerman"
] | [
"BP"
] | [] | [] |
SciVis | 2,017 | Instant Construction and Visualization of Crowded Biological Environments | 10.1109/TVCG.2017.2744258 | We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments distributed according to a Halton sequence. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV. | false | false | [
"Tobias Klein",
"Ludovic Autin",
"Barbora Kozlíková",
"David S. Goodsell",
"Arthur J. Olson",
"M. Eduard Gröller",
"Ivan Viola"
] | [
"HM"
] | [] | [] |
SciVis | 2,017 | Interactive Design and Visualization of Branched Covering Spaces | 10.1109/TVCG.2017.2744038 | Branched covering spaces are a mathematical concept which originates from complex analysis and topology and has applications in tensor field topology and geometry remeshing. Given a manifold surface and an$N$-way rotational symmetry field, a branched covering space is a manifold surface that has an$N$-to-1 map to the original surface except at theramification points, which correspond to the singularities in the rotational symmetry field. Understanding the notion and mathematical properties of branched covering spaces is important to researchers in tensor field visualization and geometry processing, and their application areas. In this paper, we provide a framework to interactively design and visualize the branched covering space (BCS) of an input mesh surface and a rotational symmetry field defined on it. In our framework, the user can visualize not only the BCSs but also their construction process. In addition, our system allows the user to design the geometric realization of the BCS using mesh deformation techniques as well as connecting tubes. This enables the user to verify important facts about BCSs such as that they are manifold surfaces around singularities, as well as theRiemann-Hurwitz formulawhich relates the Euler characteristic of the BCS to that of the original mesh. Our system is evaluated by student researchers in scientific visualization and geometry processing as well as faculty members in mathematics at our university who teach topology. We include their evaluations and feedback in the paper. | false | false | [
"Lawrence Roy",
"Prashant Kumar",
"Sanaz Golbabaei",
"Yue Zhang 0009",
"Eugene Zhang"
] | [] | [] | [] |
SciVis | 2,017 | Interactive Dynamic Volume Illumination with Refraction and Caustics | 10.1109/TVCG.2017.2744438 | In recent years, significant progress has been made in developing high-quality interactive methods for realistic volume illumination. However, refraction - despite being an important aspect of light propagation in participating media - has so far only received little attention. In this paper, we present a novel approach for refractive volume illumination including caustics capable of interactive frame rates. By interleaving light and viewing ray propagation, our technique avoids memory-intensive storage of illumination information and does not require any precomputation. It is fully dynamic and all parameters such as light position and transfer function can be modified interactively without a performance penalty. | false | false | [
"Jens G. Magnus",
"Stefan Bruckner"
] | [] | [] | [] |
SciVis | 2,017 | Multiscale Visualization and Scale-Adaptive Modification of DNA Nanostructures | 10.1109/TVCG.2017.2743981 | We present an approach to represent DNA nanostructures in varying forms of semantic abstraction, describe ways to smoothly transition between them, and thus create a continuous multiscale visualization and interaction space for applications in DNA nanotechnology. This new way of observing, interacting with, and creating DNA nanostructures enables domain experts to approach their work in any of the semantic abstraction levels, supporting both low-level manipulations and high-level visualization and modifications. Our approach allows them to deal with the increasingly complex DNA objects that they are designing, to improve their features, and to add novel functions in a way that no existing single-scale approach offers today. For this purpose we collaborated with DNA nanotechnology experts to design a set of ten semantic scales. These scales take the DNA's chemical and structural behavior into account and depict it from atoms to the targeted architecture with increasing levels of abstraction. To create coherence between the discrete scales, we seamlessly transition between them in a well-defined manner. We use special encodings to allow experts to estimate the nanoscale object's stability. We also add scale-adaptive interactions that facilitate the intuitive modification of complex structures at multiple scales. We demonstrate the applicability of our approach on an experimental use case. Moreover, feedback from our collaborating domain experts confirmed an increased time efficiency and certainty for analysis and modification tasks on complex DNA structures. Our method thus offers exciting new opportunities with promising applications in medicine and biotechnology. | false | false | [
"Haichao Miao",
"Elisa De Llano",
"Johannes Sorger",
"Yasaman Ahmadi",
"Tadija Kekic",
"Tobias Isenberg 0001",
"M. Eduard Gröller",
"Ivan Barisic",
"Ivan Viola"
] | [] | [] | [] |
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