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 |
|---|---|---|---|---|---|---|---|---|---|---|
EuroVis | 2,016 | Visibility Equalizer Cutaway Visualization of Mesoscopic Biological Models | 10.1111/cgf.12892 | In scientific illustrations and visualization, cutaway views are often employed as an effective technique for occlusion management in densely packed scenes. We propose a novel method for authoring cutaway illustrations of mesoscopic biological models. In contrast to the existing cutaway algorithms, we take advantage of the specific nature of the biological models. These models consist of thousands of instances with a comparably smaller number of different types. Our method constitutes a two stage process. In the first step, clipping objects are placed in the scene, creating a cutaway visualization of the model. During this process, a hierarchical list of stacked bars inform the user about the instance visibility distribution of each individual molecular type in the scene. In the second step, the visibility of each molecular type is fine‐tuned through these bars, which at this point act as interactive visibility equalizers. An evaluation of our technique with domain experts confirmed that our equalizer‐based approach for visibility specification is valuable and effective for both, scientific and educational purposes. | false | false | [
"Mathieu Le Muzic",
"Peter Mindek",
"Johannes Sorger",
"Ludovic Autin",
"David S. Goodsell",
"Ivan Viola"
] | [] | [] | [] |
EuroVis | 2,016 | Visual Analysis of Biomolecular Cavities: State of the Art | 10.1111/cgf.12928 | In this report we review and structure the branch of molecular visualization that is concerned with the visual analysis of cavities in macromolecular protein structures. First the necessary background, the domain terminology, and the goals of analytical reasoning are introduced. Based on a comprehensive collection of relevant research works, we present a novel classification for cavity detection approaches and structure them into four distinct classes: grid‐based, Voronoi‐based, surface‐based, and probe‐based methods. The subclasses are then formed by their combinations. We match these approaches with corresponding visualization technologies starting with direct 3D visualization, followed with non‐spatial visualization techniques that for example abstract the interactions between structures into a relational graph, straighten the cavity of interest to see its profile in one view, or aggregate the time sequence into a single contour plot. We also discuss the current state of methods for the visual analysis of cavities in dynamic data such as molecular dynamics simulations. Finally, we give an overview of the most common tools that are actively developed and used in the structural biology and biochemistry research. Our report is concluded by an outlook on future challenges in the field. | false | false | [
"Michael Krone",
"Barbora Kozlíková",
"Norbert Lindow",
"Marc Baaden",
"Daniel Baum",
"Július Parulek",
"Hans-Christian Hege",
"Ivan Viola"
] | [] | [] | [] |
EuroVis | 2,016 | Visual Analysis of Defects in Glass Fiber Reinforced Polymers for 4DCT Interrupted In situ Tests | 10.1111/cgf.12896 | Material engineers use interrupted in situ tensile testing to investigate the damage mechanisms in composite materials. For each subsequent scan, the load is incrementally increased until the specimen is completely fractured. During the interrupted in situ testing of glass fiber reinforced polymers (GFRPs) defects of four types are expected to appear: matrix fracture, fiber/matrix debonding, fiber pull‐out, and fiber fracture. There is a growing demand for the detection and analysis of these defects among the material engineers. In this paper, we present a novel workflow for the detection, classification, and visual analysis of defects in GFRPs using interrupted in situ tensile tests in combination with X‐ray Computed Tomography. The workflow is based on the automatic extraction of defects and fibers. We introduce the automatic Defect Classifier assigning the most suitable type to each defect based on its geometrical features. We present a visual analysis system that integrates four visualization methods: 1) the Defect Viewer highlights defects with visually encoded type in the context of the original CT image, 2) the Defect Density Maps provide an overview of the defect distributions according to type in 2D and 3D, 3) the Final Fracture Surface estimates the material fracture's location and displays it as a 3D surface, 4) the 3D Magic Lens enables interactive exploration by combining detailed visualizations in the region of interest with overview visualizations as context. In collaboration with material engineers, we evaluate our solution and demonstrate its practical applicability. | false | false | [
"Alexander Amirkhanov",
"Artem Amirkhanov",
"Dietmar Salaberger",
"Johann Kastner",
"M. Eduard Gröller",
"Christoph Heinzl"
] | [] | [] | [] |
EuroVis | 2,016 | Visual Analysis of Governing Topological Structures in Excitable Network Dynamics | 10.1111/cgf.12906 | To understand how topology shapes the dynamics in excitable networks is one of the fundamental problems in network science when applied to computational systems biology and neuroscience. Recent advances in the field discovered the influential role of two macroscopic topological structures, namely hubs and modules. We propose a visual analytics approach that allows for a systematic exploration of the role of those macroscopic topological structures on the dynamics in excitable networks. Dynamical patterns are discovered using the dynamical features of excitation ratio and co‐activation. Our approach is based on the interactive analysis of the correlation of topological and dynamical features using coordinated views. We designed suitable visual encodings for both the topological and the dynamical features. A degree map and an adjacency matrix visualization allow for the interaction with hubs and modules, respectively. A barycentric‐coordinates layout and a multi‐dimensional scaling approach allow for the analysis of excitation ratio and co‐activation, respectively. We demonstrate how the interplay of the visual encodings allows us to quickly reconstruct recent findings in the field within an interactive analysis and even discovered new patterns. We apply our approach to network models of commonly investigated topologies as well as to the structural networks representing the connectomes of different species. We evaluate our approach with domain experts in terms of its intuitiveness, expressiveness, and usefulness. | false | false | [
"Quynh Quang Ngo",
"Marc-Thorsten Hütt",
"Lars Linsen"
] | [] | [] | [] |
EuroVis | 2,016 | Visual Analysis of Spatial Variability and Global Correlations in Ensembles of Iso-Contours | 10.1111/cgf.12898 | For an ensemble of iso‐contours in multi‐dimensional scalar fields, we present new methods to a) visualize their dominant spatial patterns of variability, and b) to compute the conditional probability of the occurrence of a contour at one location given the occurrence at some other location. We first show how to derive a statistical model describing the contour variability, by representing the contours implicitly via signed distance functions and clustering similar functions in a reduced order space. We show that the spatial patterns of the ensemble can then be derived by analytically transforming the boundaries of a confidence interval computed from each cluster into the spatial domain. Furthermore, we introduce a mathematical basis for computing correlations between the occurrences of iso‐contours at different locations. We show that the computation of these correlations can be posed in the reduced order space as an integration problem over a region bounded by four hyper‐planes. To visualize the derived statistical properties we employ a variant of variability plots for streamlines, now including the color coding of probabilities of joint contour occurrences. We demonstrate the use of the proposed techniques for ensemble exploration in a number of 2D and 3D examples, using artificial and meteorological data sets. | false | false | [
"Florian Ferstl",
"Mathias Kanzler",
"Marc Rautenhaus",
"Rüdiger Westermann"
] | [] | [] | [] |
EuroVis | 2,016 | Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response | 10.1111/cgf.12899 | In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient‐specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter‐patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling. | false | false | [
"Renata Georgia Raidou",
"Oscar Casares-Magaz",
"Ludvig P. Muren",
"Uulke A. van der Heide",
"Jarle Rørvik",
"Marcel Breeuwer",
"Anna Vilanova Bartrolí"
] | [] | [] | [] |
EuroVis | 2,016 | Visual Debugging Techniques for Reactive Data Visualization | 10.1111/cgf.12903 | Interaction is critical to effective visualization, but can be difficult to author and debug due to dependencies among input events, program state, and visual output. Recent advances leverage reactive semantics to support declarative design and avoid the “spaghetti code” of imperative event handlers. While reactive programming improves many aspects of development, textual specifications still fail to convey the complex runtime dynamics. In response, we contribute a set of visual debugging techniques to reveal the runtime behavior of reactive visualizations. A timeline view records input events and dynamic variable updates, allowing designers to replay and inspect the propagation of values step‐by‐step. On‐demand annotations overlay the output visualization to expose relevant state and scale mappings in‐situ. Dynamic tables visualize how backing datasets change over time. To evaluate the effectiveness of these techniques, we study how first‐time Vega users debug interactions in faulty, unfamiliar specifications; with no prior knowledge, participants were able to accurately trace errors through the specification. | false | false | [
"Jane Hoffswell",
"Arvind Satyanarayan",
"Jeffrey Heer"
] | [] | [] | [] |
EuroVis | 2,016 | Visualizing Co-occurrence of Events in Populations of Viral Genome Sequences | 10.1111/cgf.12891 | Virologists are not only interested in point mutations in a genome, but also in relationships between mutations. In this work, we present a design study to support the discovery of correlated mutation events (called co‐occurrences) in populations of viral genomes. The key challenge is to identify potentially interesting pairs of events within the vast space of event combinations. In our work, we identify analyst requirements and develop a prototype through a participatory process. The key ideas of our approach are to use interest metrics to create dynamic filtering that guides the viewer to interesting and relevant correlations of genome mutations, and to provide visual encodings designed to fit scientists' mental map of the data, along with dynamic filtering techniques. We demonstrate the strength of our approach in virology‐situated case studies, and offer suggestions for extending our strategy to other sequence‐based domains. | false | false | [
"Alper Sarikaya",
"M. Correli",
"Joao M. Dinis",
"David H. O'Connor",
"Michael Gleicher"
] | [] | [] | [] |
EuroVis | 2,016 | Visualizing the Impact of Geographical Variations on Multivariate Clustering | 10.1111/cgf.12886 | Traditional multivariate clustering approaches are common in many geovisualization applications. These algorithms are used to define geodemographic profiles, ecosystems and various other land use patterns that are based on multivariate measures. Cluster labels are then projected onto a choropleth map to enable analysts to explore spatial dependencies and heterogeneity within the multivariate attributes. However, local variations in the data and choices of clustering parameters can greatly impact the resultant visualization. In this work, we develop a visual analytics framework for exploring and comparing the impact of geographical variations for multivariate clustering. Our framework employs a variety of graphical configurations and summary statistics to explore the spatial extents of clustering. It also allows users to discover patterns that can be concealed by traditional global clustering via several interactive visualization techniques including a novel drag & drop clustering difference view. We demonstrate the applicability of our framework over a demographics dataset containing quick facts about counties in the continental United States and demonstrate the need for analytical tools that can enable users to explore and compare clustering results over varying geographical features and scales. | false | false | [
"Yifan Zhang 0007",
"Wei Luo",
"Elizabeth A. Mack",
"Ross Maciejewski"
] | [] | [] | [] |
CHI | 2,016 | 'A bit like British Weather, I suppose': Design and Evaluation of the Temperature Calendar | 10.1145/2858036.2858367 | In this paper we present the design and evaluation of the Temperature Calendar -- a visualization of temperature variation within a workplace over the course of the past week. This highlights deviation from organizational temperature policy, and aims to bring staff "into the loop" of understanding and managing heating, and so reduce energy waste. The display was deployed for three weeks in five public libraries. Analysis of interaction logs, questionnaires and interviews shows that staff used the displays to understand heating in their buildings, and took action reflecting this new understanding. Bringing together our results, we discuss design implications for workplace displays, and an analysis of carbon emissions generated in constructing and operating our design. More in general, the findings helped us to reflect on the role of policy on energy consumption, and the potential for the HCI community to engage with its application, as well as its definition or modification. | false | false | [
"Enrico Costanza",
"Ben Bedwell",
"Michael O. Jewell",
"James A. Colley",
"Tom Rodden"
] | [] | [] | [] |
CHI | 2,016 | A Comparative Evaluation on Online Learning Approaches using Parallel Coordinate Visualization | 10.1145/2858036.2858101 | As visualizations are increasingly used as a storytelling medium for the general public, it becomes important to help people learn how to understand visualizations. Prior studies indicate that interactive multimedia learning environments can increase the effectiveness of learning [11]. To investigate the efficacy of the multimedia learning environments for data visualization education, we compared four online learning approaches 1) baseline (i.e., no tutorial), 2) static tutorial, 3) video tutorial, and 4) interactive tutorial-through a crowdsourced user study. We measured participants' learning outcomes in using parallel coordinates with 18 tasks. Results show that participants with the interactive condition achieved higher scores than those with the static and baseline conditions, and reported that they had a more engaging experience than those with the static condition. | false | false | [
"Bum Chul Kwon",
"Bongshin Lee"
] | [] | [] | [] |
CHI | 2,016 | A Comparison of Cooperative and Competitive Visualizations for Co-located Collaboration | 10.1145/2858036.2858072 | We present a study that investigates the influence of different types of visualizations on collaboration. The visualizations present the group's performance either in a more cooperative or more competitive way. Decades of research suggest that cooperation leads to greater productivity than competition. However, most of the existing group mirror visualizations achieve an increase in productivity and better self-regulation by enabling a direct comparison of performance within the group. We conducted a repeated measures study with 12 groups that were supported by visualizations that displayed the number of ideas of a brainstorming session (1) per person (competitive condition) (2) per group (cooperative condition), (3) per person and per group (mixed condition) and (4) without visualization (baseline). Results indicate that groups that see a combination of individual and group performance (mixed condition) are more productive, more satisfied with their results and participate in a more balanced way. | false | false | [
"Sarah Tausch",
"Stephanie Ta",
"Heinrich Hussmann"
] | [] | [] | [] |
CHI | 2,016 | AniSAM & AniAvatar: Animated Visualizations of Affective States | 10.1145/2858036.2858365 | Tools that provide visual feedback about emotions to the user in the form of an avatar or an emoticon have become increasingly important. While a great deal of effort has already been put into the reliable and accurate automatic detection of emotions, only very little is known about how this information about affective states should be displayed in a comprehensible way to the user. In the present study, three newly developed feedback tools were evaluated. The tools were developed on the basis of an existing non-verbal questionnaire to represent two dimensions of emotion (i.e. valence and arousal) based on the circumplex model of affect. A total number of 826 participants were tested, using different vignettes that describe situations with specific affective content. Employing three newly developed affective feedback tools (AniSAM, AniAvatar and MergedSAM), the ratings obtained were compared to ratings using the original SAM instrument, a well-established questionnaire to measure affect. Results indicated that the animated feedback increased the accuracy of the arousal representation. Furthermore, valence feedback was more accurate when provided with an animated manikin-based tool rather than an avatar-based tool. This provided first evidence for the usefulness of animated tools offering visual feedback on user emotion. All instruments need to undergo further development. AniSAM and AniAvatar can be downloaded for purposes of practical applications and further research. | false | false | [
"Andreas Sonderegger",
"Klaus Heyden",
"Alain Chavaillaz",
"Jürgen S. Sauer"
] | [] | [] | [] |
CHI | 2,016 | Augmenting the Field-of-View of Head-Mounted Displays with Sparse Peripheral Displays | 10.1145/2858036.2858212 | In this paper, we explore the concept of a sparse peripheral display, which augments the field-of-view of a head-mounted display with a lightweight, low-resolution, inexpensively produced array of LEDs surrounding the central high-resolution display. We show that sparse peripheral displays expand the available field-of-view up to 190º horizontal, nearly filling the human field-of-view. We prototyped two proof-of-concept implementations of sparse peripheral displays: a virtual reality headset, dubbed SparseLightVR, and an augmented reality headset, called SparseLightAR. Using SparseLightVR, we conducted a user study to evaluate the utility of our implementation, and a second user study to assess different visualization schemes in the periphery and their effect on simulator sickness. Our findings show that sparse peripheral displays are useful in conveying peripheral information and improving situational awareness, are generally preferred, and can help reduce motion sickness in nausea-susceptible people. | false | false | [
"Robert Xiao",
"Hrvoje Benko"
] | [] | [] | [] |
CHI | 2,016 | Can Eye Help You?: Effects of Visualizing Eye Fixations on Remote Collaboration Scenarios for Physical Tasks | 10.1145/2858036.2858438 | In this work, we investigate how remote collaboration between a local worker and a remote collaborator will change if eye fixations of the collaborator are presented to the worker. We track the collaborator's points of gaze on a monitor screen displaying a physical workspace and visualize them onto the space by a projector or through an optical see-through head-mounted display. Through a series of user studies, we have found the followings: 1) Eye fixations can serve as a fast and precise pointer to objects of the collaborator's interest. 2) Eyes and other modalities, such as hand gestures and speech, are used differently for object identification and manipulation. 3) Eyes are used for explicit instructions only when they are combined with speech. 4) The worker can predict some intentions of the collaborator such as his/her current interest and next instruction. | false | false | [
"Keita Higuchi",
"Ryo Yonetani",
"Yoichi Sato"
] | [] | [] | [] |
CHI | 2,016 | Chronicler: Interactive Exploration of Source Code History | 10.1145/2858036.2858442 | Exploring source code history is an important task for software maintenance. Traditionally, source code history is navigated on the granularity of individual files. This is not fine-grained enough to support users in exploring the evolution of individual code elements. We suggest to consider the history of individual elements within the tree structure inherent to source code. A history graph created from these trees then enables new ways to explore events of interest defined by structural changes in the source code. We present Tree Flow, a visualization of these structural changes designed to enable users to choose the appropriate level of detail for the task at hand. In a user study, we show that both Chronicler and the history aware timeline, two prototype systems combining history graph navigation with a traditional source code view, outperform the more traditional history navigation on a file basis and users strongly prefer Chronicler for the exploration of source code. | false | false | [
"Moritz Wittenhagen",
"Christian Cherek",
"Jan O. Borchers"
] | [] | [] | [] |
CHI | 2,016 | ChronoFab: Fabricating Motion | 10.1145/2858036.2858138 | We present ChronoFab, a 3D modeling tool to craft motion sculptures, tangible representations of 3D animated models, visualizing an object's motion with static, transient, ephemeral visuals that are left behind. Our tool casts 3D modeling as a dynamic art-form by employing 3D animation and dynamic simulation for the modeling of motion sculptures. Our work is inspired by the rich history of stylized motion depiction techniques in existing 3D motion sculptures and 2D comic art. Based on a survey of such techniques, we present an interface that enables users to rapidly explore and craft a variety of static 3D motion depiction techniques, including motion lines, multiple stroboscopic stamps, sweeps and particle systems, using a 3D animated object as input. In a set of professional and non-professional usage sessions, ChronoFab was found to be a superior tool for the authoring of motion sculptures, compared to traditional 3D modeling workflows, reducing task completion times by 79%. | false | false | [
"Rubaiat Habib Kazi",
"Tovi Grossman",
"Cory Mogk",
"Ryan M. Schmidt",
"George W. Fitzmaurice"
] | [] | [] | [] |
CHI | 2,016 | Egocentric Analysis of Dynamic Networks with EgoLines | 10.1145/2858036.2858488 | The egocentric analysis of dynamic networks focuses on discovering the temporal patterns of a subnetwork around a specific central actor (i.e., an ego-network). These types of analyses are useful in many application domains, such as social science and business intelligence, providing insights about how the central actor interacts with the outside world. We present EgoLines, an interactive visualization to support the egocentric analysis of dynamic networks. Using a "subway map" metaphor, a user can trace an individual actor over the evolution of the ego-network. The design of EgoLines is grounded in a set of key analytical questions pertinent to egocentric analysis, derived from our interviews with three domain experts and general network analysis tasks. We demonstrate the effectiveness of EgoLines in egocentric analysis tasks through a controlled experiment with 18 participants and a use-case developed with a domain expert. | false | false | [
"Jian Zhao 0010",
"Michael Glueck",
"Fanny Chevalier",
"Yanhong Wu",
"Azam Khan"
] | [] | [] | [] |
CHI | 2,016 | Enhancing Cross-Device Interaction Scripting with Interactive Illustrations | 10.1145/2858036.2858382 | Cross-device interactions involve input and output on multiple computing devices. Implementing and reasoning about interactions that cover multiple devices with a diversity of form factors and capabilities can be complex. To assist developers in programming cross-device interactions, we created DemoScript, a technique that automatically analyzes a cross-device interaction program while it is being written. DemoScript visually illustrates the step-by-step execution of a selected portion or the entire program with a novel, automatically generated cross-device storyboard visualization. In addition to helping developers understand the behavior of the program, DemoScript also allows developers to revise their program by interactively manipulating the cross-device storyboard. We evaluated DemoScript with 8 professional programmers and found that DemoScript significantly improved development efficiency by helping developers interpret and manage cross-device interaction; it also encourages testing to think through the script in a development process. | false | false | [
"Pei-Yu (Peggy) Chi",
"Yang Li 0058",
"Björn Hartmann"
] | [] | [] | [] |
CHI | 2,016 | Evaluating Information Visualization via the Interplay of Heuristic Evaluation and Question-Based Scoring | 10.1145/2858036.2858280 | In an instructional setting it can be difficult to accurately assess the quality of information visualizations of several variables. Instead of a standard design critique, an alternative is to ask potential readers of the chart to answer questions about it. A controlled study with 47 participants shows a good correlation between aggregated novice heuristic evaluation scores and results of answering questions about the data, suggesting that the two forms of assessment can be complementary. Using both metrics in parallel can yield further benefits; discrepancies between them may reveal incorrect application of heuristics or other issues. | false | false | [
"Marti A. Hearst",
"Paul Laskowski",
"Luis Silva"
] | [] | [] | [] |
CHI | 2,016 | Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants | 10.1145/2858036.2858368 | While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are making interactive network drawing increasingly feasible. We describe the translation of an analog-based technique for capturing personal networks into a digital framework termed netCanvas that addresses many existing shortcomings such as: 1) complex data entry; 2) extensive interviewer intervention and field setup; 3) difficulties in data reuse; and 4) a lack of dynamic visualizations. We test this implementation within a health behavior study of a high-risk and difficult-to-reach population. We provide a within--subjects comparison between paper and touchscreens. We assert that touchscreen-based social network capture is now a viable alternative for highly sensitive data and social network data entry tasks. | false | false | [
"Bernie Hogan",
"Joshua R. Melville",
"Gregory Lee Phillips II",
"Patrick Janulis",
"Noshir Contractor",
"Brian S. Mustanski",
"Michelle Birkett"
] | [] | [] | [] |
CHI | 2,016 | Fast, Cheap, and Good: Why Animated GIFs Engage Us | 10.1145/2858036.2858532 | Animated GIFs have been around since 1987 and recently gained more popularity on social networking sites. Tumblr, a large social networking and micro blogging platform, is a popular venue to share animated GIFs. Tumblr users follow blogs, generating a feed or posts, and choose to "like' or to "reblog' favored posts. In this paper, we use these actions as signals to analyze the engagement of over 3.9 million posts, and conclude that animated GIFs are significantly more engaging than other kinds of media. We follow this finding with deeper visual analysis of nearly 100k animated GIFs and pair our results with interviews with 13 Tumblr users to find out what makes animated GIFs engaging. We found that the animation, lack of sound, immediacy of consumption, low bandwidth and minimal time demands, the storytelling capabilities and utility for expressing emotions were significant factors in making GIFs the most engaging content on Tumblr. We also found that engaging GIFs contained faces and had higher motion energy, uniformity, resolution and frame rate. Our findings connect to media theories and have implications in design of effective content dashboards, video summarization tools and ranking algorithms to enhance engagement. | false | false | [
"Saeideh Bakhshi",
"David A. Shamma",
"Lyndon Kennedy",
"Yale Song",
"Paloma de Juan",
"Joseph Jofish Kaye"
] | [] | [] | [] |
CHI | 2,016 | Gaze Augmentation in Egocentric Video Improves Awareness of Intention | 10.1145/2858036.2858127 | Video communication using head-mounted cameras could be useful to mediate shared activities and support collaboration. Growing popularity of wearable gaze trackers presents an opportunity to add gaze information on the egocentric video. We hypothesized three potential benefits of gaze-augmented egocentric video to support collaborative scenarios: support deictic referencing, enable grounding in communication, and enable better awareness of the collaborator's intentions. Previous research on using egocentric videos for real-world collaborative tasks has failed to show clear benefits of gaze point visualization. We designed a study, deconstructing a collaborative car navigation scenario, to specifically target the value of gaze-augmented video for intention prediction. Our results show that viewers of gaze-augmented video could predict the direction taken by a driver at a four-way intersection more accurately and more confidently than a viewer of the same video without the superimposed gaze point. Our study demonstrates that gaze augmentation can be useful and encourages further study in real-world collaborative scenarios. | false | false | [
"Deepak Akkil",
"Poika Isokoski"
] | [] | [] | [] |
CHI | 2,016 | Glowworms and Fireflies: Ambient Light on Large Interactive Surfaces | 10.1145/2858036.2858524 | Ambient light is starting to be commercially used to enhance the viewing experience for watching TV. We believe that ambient light can add value in meeting and control rooms that use large vertical interactive surfaces. Therefore, we equipped a large interactive whiteboard with a peripheral ambient light display and explored its utility for different scenarios by conducting two controlled experiments. In the first experiment, we investigated how ambient light can be used for peripheral notifications, and how perception is influenced by the user's position and the type of work they are engaged in. The second experiment investigated the utility of ambient light for off-screen visualization. We condense our findings into several design recommendations that we then applied to application scenarios to show the versatility and usefulness of ambient light for large surfaces. | false | false | [
"Florian Perteneder",
"Eva-Maria Beatrix Grossauer",
"Joanne Leong",
"Wolfgang Stuerzlinger",
"Michael Haller"
] | [] | [] | [] |
CHI | 2,016 | HapTurk: Crowdsourcing Affective Ratings of Vibrotactile Icons | 10.1145/2858036.2858279 | Vibrotactile (VT) display is becoming a standard component of informative user experience, where notifications and feedback must convey information eyes-free. However, effective design is hindered by incomplete understanding of relevant perceptual qualities, together with the need for user feedback to be accessed in-situ. To access evaluation streamlining now common in visual design, we introduce proxy modalities as a way to crowdsource VT sensations by reliably communicating high-level features through a crowd-accessible channel. We investigate two proxy modalities to represent a high-fidelity tactor: a new VT visualization, and low-fidelity vibratory translations playable on commodity smartphones. We translated 10 high-fidelity vibrations into both modalities, and in two user studies found that both proxy modalities can communicate affective features, and are consistent when deployed remotely over Mechanical Turk. We analyze fit of features to modalities, and suggest future improvements. | false | false | [
"Oliver S. Schneider",
"Hasti Seifi",
"Salma Kashani",
"Matthew Chun",
"Karon E. MacLean"
] | [] | [] | [] |
CHI | 2,016 | I Know Where You Live: Inferring Details of People's Lives by Visualizing Publicly Shared Location Data | 10.1145/2858036.2858272 | This research measures human performance in inferring the functional types (i.e., home, work, leisure and transport) of locations in geo-location data using different visual representations of the data (textual, static and animated visualizations) along with different amounts of data (1, 3 or 5 day(s)). We first collected real life geo-location data from tweets. We then asked the data owners to tag their location points, resulting in ground truth data. Using this dataset we conducted an empirical study involving 45 participants to analyze how accurately they could infer the functional location of the original data owners under different conditions, i.e., three data representations, three data densities and four location types. The study results indicate that while visual techniques perform better than textual ones, the functional locations of human activities can be inferred with a relatively high accuracy even using only textual representations and a low density of location points. Workplace was more easily inferred than home while transport was the functional location with the highest accuracy. Our results also showed that it was easier to infer functional locations from data exhibiting more stable and consistent mobility patterns, which are thus more vulnerable to privacy disclosures. We discuss the implications of our findings in the context of privacy preservation and provide guidelines to users and companies to help preserve and safeguard people's privacy. | false | false | [
"Ilaria Liccardi",
"Alfie Abdul-Rahman",
"Min Chen 0001"
] | [] | [] | [] |
CHI | 2,016 | Infrastructure in the Wild: What Mapping in Post-Earthquake Nepal Reveals about Infrastructural Emergence | 10.1145/2858036.2858545 | Disasters and their impacts have unavoidable spatial characteristics. As such, maps are necessary and omnipresent features of the information landscapes that surround and support disaster response. Professional and volunteer GIS services are increasingly in demand to support map-based information visualization during crises. This paper investigates the work of mapmakers working on the response to the 2015 Nepal earthquakes. In comparison to prior events, we found significantly more collaboration and spatial data sharing took place between map producers working across humanitarian organizations and parts of the Nepal government. Collaboration between mapping practitioners was supported by a complex and emergent information infrastructure composed of social and technical elements, some of which were brought through experience with prior disaster events, and some which were shaped anew by the availability and acceptance of open data sources. Our research investigates these elements of the spatial information infrastructure in post-earthquake Nepal to consider infrastructural emergence. | false | false | [
"Robert Soden",
"Leysia Palen"
] | [] | [] | [] |
CHI | 2,016 | Integrating the Smart Home into the Digital Calendar | 10.1145/2858036.2858168 | With the growing adoption of smart home technologies, inhabitants are faced with the challenge of making sense of the data that their homes can collect to configure automated behaviors that benefit their routines. Current commercial smart home interfaces usually provide information on individual devices instead of a more comprehensive overview of a home's behavior. To reduce the complexity of smart home data and integrate it better into inhabitants' lives, we turned to the familiar metaphor of a calendar and developed our smart home interface Casalendar. In order to investigate the concept and evaluate our goals to facilitate the understanding of smart home data, we created a prototype that we installed in two commercial smart homes for a month. The results we present in this paper are based on our analysis of user data from questionnaires, semi-structured interviews, participant-driven audio and screenshot feedback as well as logged interactions with our system. Our findings exposed advantages and disadvantages of this metaphor, emerging usage patterns, privacy concerns and challenges for information visualization. We further report on implications for design and open challenges we revealed through this work. | false | false | [
"Sarah Mennicken",
"David Kim",
"Elaine May Huang"
] | [] | [] | [] |
CHI | 2,016 | Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models | 10.1145/2858036.2858529 | Understanding predictive models, in terms of interpreting and identifying actionable insights, is a challenging task. Often the importance of a feature in a model is only a rough estimate condensed into one number. However, our research goes beyond these naïve estimates through the design and implementation of an interactive visual analytics system, Prospector. By providing interactive partial dependence diagnostics, data scientists can understand how features affect the prediction overall. In addition, our support for localized inspection allows data scientists to understand how and why specific datapoints are predicted as they are, as well as support for tweaking feature values and seeing how the prediction responds. Our system is then evaluated using a case study involving a team of data scientists improving predictive models for detecting the onset of diabetes from electronic medical records. | false | false | [
"Josua Krause",
"Adam Perer",
"Kenney Ng"
] | [] | [] | [] |
CHI | 2,016 | Investigating Effects of Post-Selection Feedback for Acquiring Ultra-Small Targets on Touchscreen | 10.1145/2858036.2858593 | In this paper, we investigate the effects of post-selection feedback for acquiring ultra-small (2-4mm) targets on touchscreens. Post-selection feedback shows the contact point on touchscreen after a user lifts his/her fingers to increase users' awareness of touching. Three experiments are conducted progressively using a single crosshair target, two reciprocally acquired targets and 2D random targets. Results show that in average post-selection feedback can reduce touch error rates by 78.4%, with a compromise of target acquisition time no more than 10%. In addition, we investigate participants' adjustment behavior based on correlation between successive trials. We conclude that the benefit of post-selection feedback is the outcome of both improved understanding about finger/point mapping and the dynamic adjustment of finger movement enabled by the visualization of the touch point. | false | false | [
"Chun Yu",
"Hongyi Wen",
"Wei Xiong",
"Xiaojun Bi 0001",
"Yuanchun Shi"
] | [] | [] | [] |
CHI | 2,016 | Investigating Time Series Visualisations to Improve the User Experience | 10.1145/2858036.2858300 | Research on graphical perception of time series visualisations has focused on visual representation, and not on interaction. Even for visual representation, there has been limited study of the impact on users of visual encodings and the strengths and weaknesses of Cartesian and Polar coordinate systems. In order to address this research gap, we performed a comprehensive graphical perception study that measured the effectiveness of time series visualisations with different interactions, visual encodings and coordinate systems for several tasks. Our results show that, while positional and colour visual encodings were better for most tasks, area visual encoding performed better for data comparison. Most importantly, we identified that introducing interactivity within time series visualisations considerably enhances the user experience, without any loss of efficiency or accuracy. We believe that our findings can greatly improve the development of visual analytics tools using time series visualisations in a variety of domains. | false | false | [
"Muhammad Adnan 0001",
"Mike Just",
"Lynne Baillie"
] | [] | [] | [] |
CHI | 2,016 | iVoLVER: Interactive Visual Language for Visualization Extraction and Reconstruction | 10.1145/2858036.2858435 | We present the design and implementation of iVoLVER, a tool that allows users to create visualizations without textual programming. iVoLVER is designed to enable flexible acquisition of many types of data (text, colors, shapes, quantities, dates) from multiple source types (bitmap charts, webpages, photographs, SVGs, CSV files) and, within the same canvas, supports transformation of that data through simple widgets to construct interactive animated visuals. Aside from the tool, which is web-based and designed for pen and touch, we contribute the design of the interactive visual language and widgets for extraction, transformation, and representation of data. We demonstrate the flexibility and expressive power of the tool through a set of scenarios, and discuss some of the challenges encountered and how the tool fits within the current infovis tool landscape. | false | false | [
"Gonzalo Gabriel Méndez",
"Miguel A. Nacenta",
"Sebastien Vandenheste"
] | [] | [] | [] |
CHI | 2,016 | Look Before You Leap: Improving the Users' Ability to Detect Fraud in Electronic Marketplaces | 10.1145/2858036.2858555 | Reputation systems in current electronic marketplaces can easily be manipulated by malicious sellers in order to appear more reputable than appropriate. We conducted a controlled experiment with 40 UK and 41 German participants on their ability to detect malicious behavior by means of an eBay-like feedback profile versus a novel interface involving an interactive visualization of reputation data. The results show that participants using the new interface could better detect and understand malicious behavior in three out of four attacks (the overall detection accuracy 77% in the new vs. 56% in the old interface). Moreover, with the new interface, only 7% of the users decided to buy from the malicious seller (the options being to buy from one of the available sellers or to abstain from buying), as opposed to 30% in the old interface condition. | false | false | [
"Johannes Sänger",
"Norman Hänsch",
"Brian Glass",
"Zinaida Benenson",
"Robert Landwirth",
"M. Angela Sasse"
] | [] | [] | [] |
CHI | 2,016 | Making Sense of Temporal Queries with Interactive Visualization | 10.1145/2858036.2858408 | As real-time monitoring and analysis become increasingly important, researchers and developers turn to data stream management systems (DSMS's) for fast, efficient ways to pose temporal queries over their datasets. However, these systems are inherently complex, and even database experts find it difficult to understand the behavior of DSMS queries. To help analysts better understand these temporal queries, we developed StreamTrace, an interactive visualization tool that breaks down how a temporal query processes a given dataset, step-by-step. The design of StreamTrace is based on input from expert DSMS users; we evaluated the system with a lab study of programmers who were new to streaming queries. Results from the study demonstrate that StreamTrace can help users to verify that queries behave as expected and to isolate the regions of a query that may be causing unexpected results. | false | false | [
"Leilani Battle",
"Danyel Fisher",
"Robert DeLine",
"Mike Barnett 0001",
"Badrish Chandramouli",
"Jonathan Goldstein"
] | [] | [] | [] |
CHI | 2,016 | MyPart: Personal, Portable, Accurate, Airborne Particle Counting | 10.1145/2858036.2858571 | In 2012, air pollution in both cities and rural areas was estimated to have caused 3.7 million premature deaths, 88% of those in at risk communities. The primary pollutant was small airborne particulate matter of 10 microns or less in diameter which led to the development of cardiovascular and respiratory diseases. In response, we developed MyPart, the first personal, portable, and accurate particle sensor under $50 capable of distinguishing and counting differently sized particles. We demonstrate how MyPart offers substantial enhancements over most existing air particle sensors by simultaneously improving accessibility, flexibility, portability, and accuracy. We describe the evolution and implementation of the sensor design, demonstrate its performance across twenty everyday urban environments versus a calibrated instrument, and conduct a preliminary user study to report on the overall user experience of MyPart. We also present a novel smart-phone visualization interface and a series of simple form factor adaptations of our design. | false | false | [
"Rundong Tian",
"Christine Dierk",
"Christopher Myers",
"Eric Paulos"
] | [] | [] | [] |
CHI | 2,016 | Personalized Compass: A Compact Visualization for Direction and Location | 10.1145/2858036.2858068 | Maps on mobile/wearable devices often make it difficult to determine the location of a point of interest (POI). For example, a POI may exist outside the map or on a background with no meaningful cues. To address this issue, we present Personalized Compass, a self-contained compact graphical location indicator. Personalized Compass uses personal a priori POIs to establish a reference frame, within which a POI in question can then be localized. Graphically, a personalized compass combines a multi-needle compass with an abstract overview map. We analyze the characteristics of Personalized Compass and the existing Wedge technique, and report on a user study comparing them. Personalized Compass performs better for four inference tasks, while Wedge is better for a locating task. Based on our analysis and study results, we suggest the two techniques are complementary and offer design recommendations. | false | false | [
"Daniel Miau",
"Steven Feiner"
] | [] | [] | [] |
CHI | 2,016 | Physikit: Data Engagement Through Physical Ambient Visualizations in the Home | 10.1145/2858036.2858059 | Internet of things (IoT) devices and sensor kits have the potential to democratize the access, use, and appropriation of data. Despite the increased availability of low cost sensors, most of the produced data is "black box" in nature: users often do not know how to access or interpret data. We propose a "human-data design" approach in which end-users are given tools to create, share, and use data through tangible and physical visualizations. This paper introduces Physikit, a system designed to allow users to explore and engage with environmental data through physical ambient visualizations. We report on the design and implementation of Physikit, and present a two-week field study which showed that participants got an increased sense of the meaning of data, embellished and appropriated the basic visualizations to make them blend into their homes, and used the visualizations as a probe for community engagement and social behavior. | false | false | [
"Steven Houben",
"Connie Golsteijn",
"Sarah Gallacher",
"Rose Johnson",
"Saskia Bakker",
"Nicolai Marquardt",
"Licia Capra",
"Yvonne Rogers"
] | [] | [] | [] |
CHI | 2,016 | Programming, Problem Solving, and Self-Awareness: Effects of Explicit Guidance | 10.1145/2858036.2858252 | More people are learning to code than ever, but most learning opportunities do not explicitly teach the problem solving skills necessary to succeed at open-ended programming problems. In this paper, we present a new approach to impart these skills, consisting of: 1) explicit instruction on programming problem solving, which frames coding as a process of translating mental representations of problems and solutions into source code, 2) a method of visualizing and monitoring progression through six problem solving stages, 3) explicit, on-demand prompts for learners to reflect on their strategies when seeking help from instructors, and 4) context-sensitive help embedded in a code editor that reinforces the problem solving instruction. We experimentally evaluated the effects of our intervention across two 2-week web development summer camps with 48 high school students, finding that the intervention increased productivity, independence, programming self-efficacy, metacognitive awareness, and growth mindset. We discuss the implications of these results on learning technologies and classroom instruction. | false | false | [
"Dastyni Loksa",
"Amy J. Ko",
"Will Jernigan",
"Alannah Oleson",
"Christopher J. Mendez",
"Margaret M. Burnett"
] | [] | [] | [] |
CHI | 2,016 | ResViz: Politics and Design Issues in Visualizing Academic Metrics | 10.1145/2858036.2858181 | The use of data and metrics on a professional and personal level has led to considerable discourse around the performative power and politics of 'big data' and data visualization, with academia being no exception. We have developed a university system, ResViz, which publicly visualizes the externally funded research projects of academics, and their internal collaborations. We present an interview study that engages 20 key stakeholders, academics and administrators who are part of the pilot release for the first version of this system. In doing so, we describe and problematize our design space, considering the implications of making metrics visible and their social use within a large organization. Our findings cut across the way people communicate, review and manage performance with metrics. We raise seven design issues in this space -- practical considerations that expose the tensions in making metrics available for public contestation. | false | false | [
"Chris Elsden",
"Sebastian Mellor",
"Patrick Olivier",
"Pete Wheldon",
"David S. Kirk",
"Rob Comber"
] | [] | [] | [] |
CHI | 2,016 | SnapToReality: Aligning Augmented Reality to the Real World | 10.1145/2858036.2858250 | Augmented Reality (AR) applications may require the precise alignment of virtual objects to the real world. We propose automatic alignment of virtual objects to physical constraints calculated from the real world in real time ("snapping to reality"). We demonstrate SnapToReality alignment techniques that allow users to position, rotate, and scale virtual content to dynamic, real world scenes. Our proof-of-concept prototype extracts 3D edge and planar surface constraints. We furthermore discuss the unique design challenges of snapping in AR, including the user's limited field of view, noise in constraint extraction, issues with changing the view in AR, visualizing constraints, and more. We also report the results of a user study evaluating SnapToReality, confirming that aligning objects to the real world is significantly faster when assisted by snapping to dynamically extracted constraints. Perhaps more importantly, we also found that snapping in AR enables a fresh and expressive form of AR content creation. | false | false | [
"Benjamin Nuernberger",
"Eyal Ofek",
"Hrvoje Benko",
"Andrew D. Wilson"
] | [] | [] | [] |
CHI | 2,016 | Supporting Comment Moderators in Identifying High Quality Online News Comments | 10.1145/2858036.2858389 | Online comments submitted by readers of news articles can provide valuable feedback and critique, personal views and perspectives, and opportunities for discussion. The varying quality of these comments necessitates that publishers remove the low quality ones, but there is also a growing awareness that by identifying and highlighting high quality contributions this can promote the general quality of the community. In this paper we take a user-centered design approach towards developing a system, CommentIQ, which supports comment moderators in interactively identifying high quality comments using a combination of comment analytic scores as well as visualizations and flexible UI components. We evaluated this system with professional comment moderators working at local and national news outlets and provide insights into the utility and appropriateness of features for journalistic tasks, as well as how the system may enable or transform journalistic practices around online comments. | false | false | [
"Deokgun Park 0001",
"Simranjit Singh Sachar",
"Nicholas Diakopoulos",
"Niklas Elmqvist"
] | [] | [] | [] |
CHI | 2,016 | Taking 5: Work-Breaks, Productivity, and Opportunities for Personal Informatics for Knowledge Workers | 10.1145/2858036.2858066 | Taking breaks from work is an essential and universal practice. In this paper, we extend current research on productivity in the workplace to consider the break habits of knowledge workers and explore opportunities of break logging for personal informatics. We report on three studies. Through a survey of 147 U.S.-based knowledge workers, we investigate what activities respondents consider to be breaks from work, and offer an understanding of the benefit workers desire when they take breaks. We then present results from a two-week in-situ diary study with 28 participants in the U.S. who logged 800 breaks, offering insights into the effect of work breaks on productivity. We finally explore the space of information visualization of work breaks and productivity in a third study. We conclude with a discussion of implications for break recommendation systems, availability and interuptibility research, and the quantified workplace. | false | false | [
"Daniel A. Epstein",
"Daniel Avrahami",
"Jacob T. Biehl"
] | [] | [] | [] |
CHI | 2,016 | Telling Stories about Dynamic Networks with Graph Comics | 10.1145/2858036.2858387 | In this paper, we explore graph comics as a medium to communicate changes in dynamic networks. While previous re- search has focused on visualizing dynamic networks for data exploration, we want to see if we can take advantage of the visual expressiveness and familiarity of comics to present and explain temporal changes in networks to an audience. To understand the potential of comics as a storytelling medium, we first created a variety of comics during a 3 month structured design process, involving domain experts from public education and neuroscience. This process led to the definition of 8 design factors for creating graph comics and propose design solutions for each. Results from a qualitative study suggest that a general audience is quickly able understand complex temporal changes through graph comics, provided with minimal textual annotations and no training. | false | false | [
"Benjamin Bach",
"Natalie Kerracher",
"Kyle Wm. Hall",
"Sheelagh Carpendale",
"Jessie Kennedy",
"Nathalie Henry Riche"
] | [] | [] | [] |
CHI | 2,016 | The Effect of Richer Visualizations on Code Comprehension | 10.1145/2858036.2858372 | Researchers often introduce visual tools to programming environments in order to facilitate program comprehension, reduce navigation times, and help developers answer difficult questions. Syntax highlighting is the main visual lens through which developers perceive their code, and yet its effects and the effects of richer code presentations on code comprehension have not been evaluated systematically. We present a rigorous user study comparing mainstream syntax highlighting to two visually-enhanced presentations of code. Our results show that: (1) richer code visualizations reduce the time necessary to answer questions about code features, and (2) contrary to the subjective perception of developers, richer code visualizations do not lead to visual overload. Based on our results we outline practical recommendations for tool designers. | false | false | [
"Dimitar Asenov",
"Otmar Hilliges",
"Peter Müller 0001"
] | [] | [] | [] |
CHI | 2,016 | Unsupervised Clickstream Clustering for User Behavior Analysis | 10.1145/2858036.2858107 | Online services are increasingly dependent on user participation. Whether it's online social networks or crowdsourcing services, understanding user behavior is important yet challenging. In this paper, we build an unsupervised system to capture dominating user behaviors from clickstream data (traces of users' click events), and visualize the detected behaviors in an intuitive manner. Our system identifies "clusters" of similar users by partitioning a similarity graph (nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviors. For evaluation, we present case studies on two large-scale clickstream traces (142 million events) from real social networks. Our system effectively identifies previously unknown behaviors, e.g., dormant users, hostile chatters. Also, our user study shows people can easily interpret identified behaviors using our visualization tool. | false | false | [
"Gang Wang 0011",
"Xinyi Zhang",
"Shiliang Tang",
"Haitao Zheng 0001",
"Ben Y. Zhao"
] | [] | [] | [] |
CHI | 2,016 | UX Heatmaps: Mapping User Experience on Visual Interfaces | 10.1145/2858036.2858271 | In this paper, we present an off-the-shelf UX evaluation tool which contextualizes users' physiological and behavioral signals while interacting with a system. The proposed tool triangulates users' gaze data with inferred users' cognitive and emotional states to produce user experience (UX) heatmaps, which show where users were looking when they experienced specific cognitive and emotional states. Results show that for a given cognitive state (i.e., cognitive load), the proposed UX heatmap was able to effectively highlight the areas where users experienced different levels of cognitive load on an interface. The proposed tool enables the visual analysis of users' various emotional and cognitive states for specific areas on a given interface, and also to compare users' states across multiple interfaces, which should be useful for both UX researchers and practitioners. | false | false | [
"Vanessa Georges",
"François Courtemanche",
"Sylvain Senecal",
"Thierry Baccino",
"Marc Fredette",
"Pierre-Majorique Léger"
] | [] | [] | [] |
CHI | 2,016 | When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems | 10.1145/2858036.2858558 | Users often rely on realtime predictions in everyday contexts like riding the bus, but may not grasp that such predictions are subject to uncertainty. Existing uncertainty visualizations may not align with user needs or how they naturally reason about probability. We present a novel mobile interface design and visualization of uncertainty for transit predictions on mobile phones based on discrete outcomes. To develop it, we identified domain specific design requirements for visualizing uncertainty in transit prediction through: 1) a literature review, 2) a large survey of users of a popular realtime transit application, and 3) an iterative design process. We present several candidate visualizations of uncertainty for realtime transit predictions in a mobile context, and we propose a novel discrete representation of continuous outcomes designed for small screens, quantile dotplots. In a controlled experiment we find that quantile dotplots reduce the variance of probabilistic estimates by ~1.15 times compared to density plots and facilitate more confident estimation by end-users in the context of realtime transit prediction scenarios. | false | false | [
"Matthew Kay 0001",
"Tara Kola",
"Jessica R. Hullman",
"Sean A. Munson"
] | [] | [] | [] |
VAST | 2,015 | 3D Regression Heat Map Analysis of Population Study Data | 10.1109/TVCG.2015.2468291 | Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease. | false | false | [
"Paul Klemm",
"Kai Lawonn",
"Sylvia Saalfeld",
"Uli Niemann",
"Katrin Hegenscheid",
"Henry Völzke",
"Bernhard Preim"
] | [] | [] | [] |
VAST | 2,015 | A Case Study Using Visualization Interaction Logs and Insight Metrics to Understand How Analysts Arrive at Insights | 10.1109/TVCG.2015.2467613 | We present results from an experiment aimed at using logs of interactions with a visual analytics application to better understand how interactions lead to insight generation. We performed an insight-based user study of a visual analytics application and ran post hoc quantitative analyses of participants' measured insight metrics and interaction logs. The quantitative analyses identified features of interaction that were correlated with insight characteristics, and we confirmed these findings using a qualitative analysis of video captured during the user study. Results of the experiment include design guidelines for the visual analytics application aimed at supporting insight generation. Furthermore, we demonstrated an analysis method using interaction logs that identified which interaction patterns led to insights, going beyond insight-based evaluations that only quantify insight characteristics. We also discuss choices and pitfalls encountered when applying this analysis method, such as the benefits and costs of applying an abstraction framework to application-specific actions before further analysis. Our method can be applied to evaluations of other visualization tools to inform the design of insight-promoting interactions and to better understand analyst behaviors. | false | false | [
"Hua Guo",
"Steven R. Gomez",
"Caroline Ziemkiewicz",
"David H. Laidlaw"
] | [
"HM"
] | [] | [] |
VAST | 2,015 | A software developer's guide to informal evaluation of Visual Analytics environments using VAST Challenge information | 10.1109/VAST.2015.7347674 | The VAST Challenge has been a popular venue for academic and industry participants for over ten years. Many participants comment that the majority of their time in preparing VAST Challenge entries is discovering elements in their software environments that need to be redesigned in order to solve the given task. Fortunately, there is no need to wait until the VAST Challenge is announced to test out software systems. The Visual Analytics Benchmark Repository contains all past VAST Challenge tasks, data, solutions and submissions. In this poster we describe how developers can perform informal evaluations of various aspects of their visual analytics environments using VAST Challenge information. | false | false | [
"Kristin A. Cook",
"Jean Scholtz",
"Mark A. Whiting"
] | [] | [] | [] |
VAST | 2,015 | A System for visual exploration of caution spots from vehicle recorder data | 10.1109/VAST.2015.7347677 | It is vital for the transportation industry, which performs most of its work by automobiles, to reduce its accident rate. This paper proposes a 3D visual interaction method for exploring caution areas from large-scale vehicle recorder data. Our method provides (i) a flexible filtering interface for driving operations such as braking or handling operations by various combinations of their attribute values such as velocity and acceleration, and (ii) a 3D visual environment for spatio-temporal exploration of caution areas. The proposed method was able to extract caution areas where some accidents have actually occurred or that are on very narrow roads with bad visibility by using real data given by one of the biggest transportation companies in Japan. | false | false | [
"Masahiko Itoh",
"Daisaku Yokoyama",
"Masashi Toyoda",
"Masaru Kitsuregawa"
] | [] | [] | [] |
VAST | 2,015 | An Uncertainty-Aware Approach for Exploratory Microblog Retrieval | 10.1109/TVCG.2015.2467554 | Although there has been a great deal of interest in analyzing customer opinions and breaking news in microblogs, progress has been hampered by the lack of an effective mechanism to discover and retrieve data of interest from microblogs. To address this problem, we have developed an uncertainty-aware visual analytics approach to retrieve salient posts, users, and hashtags. We extend an existing ranking technique to compute a multifaceted retrieval result: the mutual reinforcement rank of a graph node, the uncertainty of each rank, and the propagation of uncertainty among different graph nodes. To illustrate the three facets, we have also designed a composite visualization with three visual components: a graph visualization, an uncertainty glyph, and a flow map. The graph visualization with glyphs, the flow map, and the uncertainty analysis together enable analysts to effectively find the most uncertain results and interactively refine them. We have applied our approach to several Twitter datasets. Qualitative evaluation and two real-world case studies demonstrate the promise of our approach for retrieving high-quality microblog data. | false | false | [
"Mengchen Liu",
"Shixia Liu",
"Xizhou Zhu",
"Qinying Liao",
"Furu Wei",
"Shimei Pan"
] | [] | [
"P"
] | [
{
"name": "Paper Preprint",
"url": "http://arxiv.org/pdf/1512.04038v1",
"icon": "paper"
}
] |
VAST | 2,015 | BiSet: Semantic Edge Bundling with Biclusters for Sensemaking | 10.1109/TVCG.2015.2467813 | Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, “in-between”, to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics. | false | false | [
"Maoyuan Sun",
"Peng Mi",
"Chris North 0001",
"Naren Ramakrishnan"
] | [] | [] | [] |
VAST | 2,015 | Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes | 10.1109/TVCG.2015.2467551 | While the primary goal of visual analytics research is to improve the quality of insights and findings, a substantial amount of research in provenance has focused on the history of changes and advances throughout the analysis process. The term, provenance, has been used in a variety of ways to describe different types of records and histories related to visualization. The existing body of provenance research has grown to a point where the consolidation of design knowledge requires cross-referencing a variety of projects and studies spanning multiple domain areas. We present an organizational framework of the different types of provenance information and purposes for why they are desired in the field of visual analytics. Our organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects. We also discuss the relationships between these factors and the methods used to capture provenance information. In addition, our organization can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research. | false | false | [
"Eric D. Ragan",
"Alex Endert",
"Jibonananda Sanyal",
"Jian Chen 0006"
] | [] | [] | [] |
VAST | 2,015 | CiteRivers: Visual Analytics of Citation Patterns | 10.1109/TVCG.2015.2467621 | The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback. | false | false | [
"Florian Heimerl",
"Qi Han 0006",
"Steffen Koch 0001",
"Thomas Ertl"
] | [] | [] | [] |
VAST | 2,015 | Collaborative visual analysis with RCloud | 10.1109/VAST.2015.7347627 | Consider the emerging role of data science teams embedded in larger organizations. Individual analysts work on loosely related problems, and must share their findings with each other and the organization at large, moving results from exploratory data analyses (EDA) into automated visualizations, diagnostics and reports deployed for wider consumption. There are two problems with the current practice. First, there are gaps in this workflow: EDA is performed with one set of tools, and automated reports and deployments with another. Second, these environments often assume a single-developer perspective, while data scientist teams could get much benefit from easier sharing of scripts and data feeds, experiments, annotations, and automated recommendations, which are well beyond what traditional version control systems provide. We contribute and justify the following three requirements for systems built to support current data science teams and users: discoverability, technology transfer, and coexistence. In addition, we contribute the design and implementation of RCloud, a system that supports the requirements of collaborative data analysis, visualization and web deployment. About 100 people used RCloud for two years. We report on interviews with some of these users, and discuss design decisions, tradeoffs and limitations in comparison to other approaches. | false | false | [
"Stephen C. North",
"Carlos Scheidegger",
"Simon Urbanek",
"Gordon Woodhull"
] | [] | [] | [] |
VAST | 2,015 | Comparative visual analysis of vector field ensembles | 10.1109/VAST.2015.7347634 | We present a new visual analysis approach to support the comparative exploration of 2D vector-valued ensemble fields. Our approach enables the user to quickly identify the most similar groups of ensemble members, as well as the locations where the variation among the members is high. We further provide means to visualize the main features of the potentially multimodal directional distributions at user-selected locations. For this purpose, directional data is modelled using mixtures of probability density functions (pdfs), which allows us to characterize and classify complex distributions with relatively few parameters. The resulting mixture models are used to determine the degree of similarity between ensemble members, and to construct glyphs showing the direction, spread, and strength of the principal modes of the directional distributions. We also propose several similarity measures, based on which we compute pairwise member similarities in the spatial domain and form clusters of similar members. The hierarchical clustering is shown using dendrograms and similarity matrices, which can be used to select particular members and visualize their variations. A user interface providing multiple linked views enables the simultaneous visualization of aggregated global and detailed local variations, as well as the selection of members for a detailed comparison. | false | false | [
"Mihaela Jarema",
"Ismail Demir",
"Johannes Kehrer",
"Rüdiger Westermann"
] | [] | [] | [] |
VAST | 2,015 | DemographicVis: Analyzing demographic information based on user generated content | 10.1109/VAST.2015.7347631 | The wide-spread of social media provides unprecedented sources of written language that can be used to model and infer online demographics. In this paper, we introduce a novel visual text analytics system, DemographicVis, to aid interactive analysis of such demographic information based on user-generated content. Our approach connects categorical data (demographic information) with textual data, allowing users to understand the characteristics of different demographic groups in a transparent and exploratory manner. The modeling and visualization are based on ground truth demographic information collected via a survey conducted on Reddit.com. Detailed user information is taken into our modeling process that connects the demographic groups with features that best describe the distinguishing characteristics of each group. Features including topical and linguistic are generated from the user-generated contents. Such features are then analyzed and ranked based on their ability to predict the users' demographic information. To enable interactive demographic analysis, we introduce a web-based visual interface that presents the relationship of the demographic groups, their topic interests, as well as the predictive power of various features. We present multiple case studies to showcase the utility of our visual analytics approach in exploring and understanding the interests of different demographic groups. We also report results from a comparative evaluation, showing that the DemographicVis is quantitatively superior or competitive and subjectively preferred when compared to a commercial text analysis tool. | false | false | [
"Wenwen Dou",
"Isaac Cho",
"Omar ElTayeby",
"Jaegul Choo",
"Derek Xiaoyu Wang",
"William Ribarsky"
] | [] | [] | [] |
VAST | 2,015 | EgoNetCloud: Event-based egocentric dynamic network visualization | 10.1109/VAST.2015.7347632 | Event-based egocentric dynamic networks are an important class of networks widely seen in many domains. In this paper, we present a visual analytics approach for these networks by combining data-driven network simplifications with a novel visualization design - EgoNetCloud. In particular, an integrated data processing pipeline is proposed to prune, compress and filter the networks into smaller but salient abstractions. To accommodate the simplified network into the visual design, we introduce a constrained graph layout algorithm on the dynamic network. Through a real-life case study as well as conversations with the domain expert, we demonstrate the effectiveness of the EgoNetCloud design and system in completing analysis tasks on event-based dynamic networks. The user study comparing EgoNetCloud with a working system on academic search confirms the effectiveness and convenience of our visual analytics based approach. | false | false | [
"Qingsong Liu",
"Yifan Hu 0001",
"Lei Shi 0002",
"Xinzhu Mu",
"Yutao Zhang",
"Jie Tang 0001"
] | [] | [] | [] |
VAST | 2,015 | egoSlider: Visual Analysis of Egocentric Network Evolution | 10.1109/TVCG.2015.2468151 | Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks. | false | false | [
"Yanhong Wu",
"Naveen Pitipornvivat",
"Jian Zhao 0010",
"Sixiao Yang",
"Guowei Huang",
"Huamin Qu"
] | [] | [] | [] |
VAST | 2,015 | Evolution inspector: Interactive visual analysis for evolutionary molecular design | 10.1109/VAST.2015.7347687 | De novo design is a computational-chemistry method, where a computer program utilizes an optimization method, in our case an evolutionary algorithm, to design compounds with desired chemical properties. The optimization is performed with respect to a quantity called fitness, defined by the chemists. We present a tool that connects interactive visual analysis and evolutionary algorithm-based molecular design. We employ linked views to communicate different aspects of the data: the statistical distribution of molecule fitness, connections between individual molecules during the evolution and 3D molecular structure. The application is already used by chemists to explore and analyze the results of their evolution experiments and has proved to be highly useful. | false | false | [
"Veronika Soltészová",
"Marco Foscato",
"Sondre H. Eliasson",
"Vidar R. Jensen"
] | [] | [] | [] |
VAST | 2,015 | Exploring Evolving Media Discourse Through Event Cueing | 10.1109/TVCG.2015.2467991 | Online news, microblogs and other media documents all contain valuable insight regarding events and responses to events. Underlying these documents is the concept of framing, a process in which communicators act (consciously or unconsciously) to construct a point of view that encourages facts to be interpreted by others in a particular manner. As media discourse evolves, how topics and documents are framed can undergo change, shifting the discussion to different viewpoints or rhetoric. What causes these shifts can be difficult to determine directly; however, by linking secondary datasets and enabling visual exploration, we can enhance the hypothesis generation process. In this paper, we present a visual analytics framework for event cueing using media data. As discourse develops over time, our framework applies a time series intervention model which tests to see if the level of framing is different before or after a given date. If the model indicates that the times before and after are statistically significantly different, this cues an analyst to explore related datasets to help enhance their understanding of what (if any) events may have triggered these changes in discourse. Our framework consists of entity extraction and sentiment analysis as lenses for data exploration and uses two different models for intervention analysis. To demonstrate the usage of our framework, we present a case study on exploring potential relationships between climate change framing and conflicts in Africa. | false | false | [
"Yafeng Lu",
"Michael Steptoe",
"Sarah Burke",
"Hong Wang",
"Jiun-Yi Tsai",
"Hasan Davulcu",
"Douglas C. Montgomery",
"Steven R. Corman",
"Ross Maciejewski"
] | [] | [] | [] |
VAST | 2,015 | FeatureInsight: Visual support for error-driven feature ideation in text classification | 10.1109/VAST.2015.7347637 | Machine learning requires an effective combination of data, features, and algorithms. While many tools exist for working with machine learning data and algorithms, support for thinking of new features, or feature ideation, remains poor. In this paper, we investigate two general approaches to support feature ideation: visual summaries and sets of errors. We present FeatureInsight, an interactive visual analytics tool for building new dictionary features (semantically related groups of words) for text classification problems. FeatureInsight supports an error-driven feature ideation process and provides interactive visual summaries of sets of misclassified documents. We conducted a controlled experiment evaluating both visual summaries and sets of errors in FeatureInsight. Our results show that visual summaries significantly improve feature ideation, especially in combination with sets of errors. Users preferred visual summaries over viewing raw data, and only preferred examining sets when visual summaries were provided. We discuss extensions of both approaches to data types other than text, and point to areas for future research. | false | false | [
"Michael Brooks",
"Saleema Amershi",
"Bongshin Lee",
"Steven Mark Drucker",
"Ashish Kapoor",
"Patrice Y. Simard"
] | [] | [] | [] |
VAST | 2,015 | Four considerations for supporting visual analysis in display ecologies | 10.1109/VAST.2015.7347628 | The current proliferation of large displays and mobile devices presents a number of exciting opportunities for visual analytics and information visualization. The display ecology enables multiple displays to function in concert within a broader technological environment to accomplish visual analysis tasks. Based on a comprehensive survey of multi-display systems from a variety of fields, we propose four key considerations for visual analysis in display ecologies: 1) Display Composition, 2) Information Coordination/Transfer, 3) Information Connection, and 4) Display Membership. Different aspects of display ecologies stemming from these design considerations will enable users to transform and empower multiple displays as a display ecology for visual analysis. | false | false | [
"Haeyong Chung",
"Chris North 0001",
"Sarang Joshi",
"Jian Chen 0006"
] | [] | [] | [] |
VAST | 2,015 | FPSSeer: Visual analysis of game frame rate data | 10.1109/VAST.2015.7347633 | The rate at which frames are rendered in a computer game directly influences both game playability and enjoyability. Players frequently have to deal with the trade-off between high frame rates and good resolution. Analyzing patterns in frame rate data and their correlation with the overall game performance is important in designing games (e.g., graphic card/display setting suggestion and game performance measurement). However, this task is challenging because game frame rates vary both temporally and spatially. In addition, players may adjust their display settings based on their gaming experience and hardware conditions, which further contributes to the unpredictability of frame rates. In this paper, we present a comprehensive visual analytics system FPSSeer, to help game designers gain insight into frame rate data. Our system consists of four major views: 1) a frame rate view to show the overall distribution in a geographic scale, 2) a grid view to show the frame rate distribution and grid element clusters based on their similarity, 3) a FootRiver view to reveal the temporal patterns in game condition changes and potential spatiotemporal correlations, and 4) a comparison view to evaluate game performance discrepancy under different game tests. The real-world case studies demonstrate the effectiveness of our system. The system has been applied to an online commercial game to monitor its performance and to provide feedbacks to designers and developers. | false | false | [
"Quan Li",
"Peng Xu",
"Huamin Qu"
] | [] | [] | [] |
VAST | 2,015 | HTMVS: Visualizing hierarchical topics and their evolution | 10.1109/VAST.2015.7347675 | Topic model has been an active research area for many years, it can be used for discovering latent semantics and finding hidden knowledge in unstructured data corpus. In this paper, we investigated the problems in visualizing hierarchical topic and their evolution. The contribution of this paper is threefold, first we explore the static visualization of hierarchical topics using the `nested circle' layout, and then in order to present the topic evolution over time, we extended a hierarchical topic model and employ topic transformation visualizations to track the arising, splitting and disappearing of certain topics under the dynamic topical hierarchy. Finally, a Hierarchical Topic Model Visualization System (HTMVS) is designed to take advantage of both static and dynamic hierarchical topic visualization. | false | false | [
"Haoling Dong",
"Siliang Tang",
"Si Li",
"Fei Wu 0001",
"Yueting Zhuang"
] | [] | [] | [] |
VAST | 2,015 | Integrating predictive analytics into a spatiotemporal epidemic simulation | 10.1109/VAST.2015.7347626 | The Epidemic Simulation System (EpiSimS) is a scalable, complex modeling tool for analyzing disease within the United States. Due to its high input dimensionality, time requirements, and resource constraints, simulating over the entire parameter space is unfeasible. One solution is to take a granular sampling of the input space and use simpler predictive models (emulators) in between. The quality of the implemented emulator depends on many factors: robustness, sophistication, configuration, and suitability to the input data. Visual analytics can be leveraged to provide guidance and understanding of these things to the user. In this paper, we have implemented a novel interface and workflow for emulator building and use. We introduce a workflow to build emulators, make predictions, and then analyze the results. Our prediction process first predicts temporal time series, and uses these to derive predicted spatial densities. Integrated into the EpiSimS framework, we target users who are non-experts at statistical modeling. This approach allows for a high level of analysis into the state of the built emulators and their resultant predictions. We present our workflow, models, the associated system, and evaluate the overall utility with feedback from EpiSimS scientists. | false | false | [
"Chris Bryan",
"Xue Wu",
"Susan M. Mniszewski",
"Kwan-Liu Ma"
] | [] | [] | [] |
VAST | 2,015 | Interactive semi-automatic categorization for spinel group minerals | 10.1109/VAST.2015.7347676 | Spinel group minerals are excellent indicators of geological environments (tectonic settings). In 2001, Barnes and Roeder defined a set of contours corresponding to compositional fields for spinel group minerals. Geologists typically use this contours to estimate the tectonic environment where a particular spinel composition could have been formed. This task is prone to errors and requires tedious manual comparison of overlapping diagrams. We introduce a semi-automatic, interactive detection of tectonic settings for an arbitrary dataset based on the Barnes and Roeder contours. The new approach integrates the mentioned contours and includes a novel interaction called contour brush. The new methodology is integrated in the Spinel Explorer system and it improves the scientist's workflow significantly. | false | false | [
"Maria Luján Ganuza",
"Maria Florencia Gargiulo",
"Gabriela Ferracutti",
"Silvia Mabel Castro",
"Ernesto A. Bjerg",
"M. Eduard Gröller",
"Kresimir Matkovic"
] | [] | [] | [] |
VAST | 2,015 | Interactive Visual Discovering of Movement Patterns from Sparsely Sampled Geo-tagged Social Media Data | 10.1109/TVCG.2015.2467619 | Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns. | false | false | [
"Siming Chen 0001",
"Xiaoru Yuan",
"Zhenhuang Wang",
"Cong Guo 0004",
"Christy Jie Liang",
"Zuchao Wang",
"Xiaolong Zhang 0001",
"Jiawan Zhang"
] | [] | [] | [] |
VAST | 2,015 | Interactive Visual Profiling of Musicians | 10.1109/TVCG.2015.2467620 | Determining similar objects based upon the features of an object of interest is a common task for visual analytics systems. This process is called profiling, if the object of interest is a person with individual attributes. The profiling of musicians similar to a musician of interest with the aid of visual means became an interesting research question for musicologists working with the Bavarian Musicians Encyclopedia Online. This paper illustrates the development of a visual analytics profiling system that is used to address such research questions. Taking musicological knowledge into account, we outline various steps of our collaborative digital humanities project, priority (1) the definition of various measures to determine the similarity of musicians' attributes, and (2) the design of an interactive profiling system that supports musicologists in iteratively determining similar musicians. The utility of the profiling system is emphasized by various usage scenarios illustrating current research questions in musicology. | false | false | [
"Stefan Jänicke",
"Josef Focht",
"Gerik Scheuermann"
] | [] | [] | [] |
VAST | 2,015 | Interactive visual steering of hierarchical simulation ensembles | 10.1109/VAST.2015.7347635 | Multi-level simulation models, i.e., models where different components are simulated using sub-models of varying levels of complexity, belong to the current state-of-the-art in simulation. The existing analysis practice for multi-level simulation results is to manually compare results from different levels of complexity, amounting to a very tedious and error-prone, trial-and-error exploration process. In this paper, we introduce hierarchical visual steering, a new approach to the exploration and design of complex systems. Hierarchical visual steering makes it possible to explore and analyze hierarchical simulation ensembles at different levels of complexity. At each level, we deal with a dynamic simulation ensemble - the ensemble grows during the exploration process. There is at least one such ensemble per simulation level, resulting in a collection of dynamic ensembles, analyzed simultaneously. The key challenge is to map the multi-dimensional parameter space of one ensemble to the multi-dimensional parameter space of another ensemble (from another level). In order to support the interactive visual analysis of such complex data we propose a novel approach to interactive and semi-automatic parameter space segmentation and comparison. The approach combines a novel interaction technique and automatic, computational methods - clustering, concave hull computation, and concave polygon overlapping - to support the analysts in the cross-ensemble parameter space mapping. In addition to the novel parameter space segmentation we also deploy coordinated multiple views with standard plots. We describe the abstract analysis tasks, identified during a case study, i.e., the design of a variable valve actuation system of a car engine. The study is conducted in cooperation with experts from the automotive industry. Very positive feedback indicates the usefulness and efficiency of the newly proposed approach. | false | false | [
"Rainer Splechtna",
"Kresimir Matkovic",
"Denis Gracanin",
"Mario Jelovic",
"Helwig Hauser"
] | [] | [] | [] |
VAST | 2,015 | InterAxis: Steering Scatterplot Axes via Observation-Level Interaction | 10.1109/TVCG.2015.2467615 | Scatterplots are effective visualization techniques for multidimensional data that use two (or three) axes to visualize data items as a point at its corresponding x and y Cartesian coordinates. Typically, each axis is bound to a single data attribute. Interactive exploration occurs by changing the data attributes bound to each of these axes. In the case of using scatterplots to visualize the outputs of dimension reduction techniques, the x and y axes are combinations of the true, high-dimensional data. For these spatializations, the axes present usability challenges in terms of interpretability and interactivity. That is, understanding the axes and interacting with them to make adjustments can be challenging. In this paper, we present InterAxis, a visual analytics technique to properly interpret, define, and change an axis in a user-driven manner. Users are given the ability to define and modify axes by dragging data items to either side of the x or y axes, from which the system computes a linear combination of data attributes and binds it to the axis. Further, users can directly tune the positive and negative contribution to these complex axes by using the visualization of data attributes that correspond to each axis. We describe the details of our technique and demonstrate the intended usage through two scenarios. | false | false | [
"Hannah Kim",
"Jaegul Choo",
"Haesun Park",
"Alex Endert"
] | [] | [] | [] |
VAST | 2,015 | iVizTRANS: Interactive visual learning for home and work place detection from massive public transportation data | 10.1109/VAST.2015.7347630 | Using transport smart card transaction data to understand the homework dynamics of a city for urban planning is emerging as an alternative to traditional surveys which may be conducted every few years are no longer effective and efficient for the rapidly transforming modern cities. As commuters travel patterns are highly diverse, existing rule-based methods are not fully adequate. In this paper, we present iVizTRANS - a tool which combines an interactive visual analytics (VA) component to aid urban planners to analyse complex travel patterns and decipher activity locations for single public transport commuters. It is coupled with a machine learning component that iteratively learns from the planners classifications to train a classifier. The classifier is then applied to the city-wide smart card data to derive the dynamics for all public transport commuters. Our evaluation shows it outperforms the rule-based methods in previous work. | false | false | [
"Liang Yu",
"Wei Wu 0020",
"Xiaohui Li 0002",
"Guangxia Li",
"Wee Siong Ng",
"See-Kiong Ng",
"Zhongwen Huang",
"Anushiya Arunan",
"Hui Min Watt"
] | [] | [] | [] |
VAST | 2,015 | LiteVis: Integrated Visualization for Simulation-Based Decision Support in Lighting Design | 10.1109/TVCG.2015.2468011 | State-of-the-art lighting design is based on physically accurate lighting simulations of scenes such as offices. The simulation results support lighting designers in the creation of lighting configurations, which must meet contradicting customer objectives regarding quality and price while conforming to industry standards. However, current tools for lighting design impede rapid feedback cycles. On the one side, they decouple analysis and simulation specification. On the other side, they lack capabilities for a detailed comparison of multiple configurations. The primary contribution of this paper is a design study of LiteVis, a system for efficient decision support in lighting design. LiteVis tightly integrates global illumination-based lighting simulation, a spatial representation of the scene, and non-spatial visualizations of parameters and result indicators. This enables an efficient iterative cycle of simulation parametrization and analysis. Specifically, a novel visualization supports decision making by ranking simulated lighting configurations with regard to a weight-based prioritization of objectives that considers both spatial and non-spatial characteristics. In the spatial domain, novel concepts support a detailed comparison of illumination scenarios. We demonstrate LiteVis using a real-world use case and report qualitative feedback of lighting designers. This feedback indicates that LiteVis successfully supports lighting designers to achieve key tasks more efficiently and with greater certainty. | false | false | [
"Johannes Sorger",
"Thomas Ortner",
"Christian Luksch",
"Michael Schwärzler",
"M. Eduard Gröller",
"Harald Piringer"
] | [] | [] | [] |
VAST | 2,015 | Mixed-initiative visual analytics using task-driven recommendations | 10.1109/VAST.2015.7347625 | Visual data analysis is composed of a collection of cognitive actions and tasks to decompose, internalize, and recombine data to produce knowledge and insight. Visual analytic tools provide interactive visual interfaces to data to support discovery and sensemaking tasks, including forming hypotheses, asking questions, and evaluating and organizing evidence. Myriad analytic models can be incorporated into visual analytic systems at the cost of increasing complexity in the analytic discourse between user and system. Techniques exist to increase the usability of interacting with analytic models, such as inferring data models from user interactions to steer the underlying models of the system via semantic interaction, shielding users from having to do so explicitly. Such approaches are often also referred to as mixed-initiative systems. Sensemaking researchers have called for development of tools that facilitate analytic sensemaking through a combination of human and automated activities. However, design guidelines do not exist for mixed-initiative visual analytic systems to support iterative sensemaking. In this paper, we present candidate design guidelines and introduce the Active Data Environment (ADE) prototype, a spatial workspace supporting the analytic process via task recommendations invoked by inferences about user interactions within the workspace. ADE recommends data and relationships based on a task model, enabling users to co-reason with the system about their data in a single, spatial workspace. This paper provides an illustrative use case, a technical description of ADE, and a discussion of the strengths and limitations of the approach. | false | false | [
"Kristin A. Cook",
"Nick Cramer",
"David J. Israel",
"Michael Wolverton",
"Joe Bruce",
"Russ Burtner",
"Alex Endert"
] | [] | [] | [] |
VAST | 2,015 | MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering | 10.1109/TVCG.2015.2468111 | Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called MobilityGraphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that MobilityGraphs support the identification of regular daily and weekly movement patterns of resident population. | false | false | [
"Tatiana von Landesberger",
"Felix Brodkorb",
"Philipp Roskosch",
"Natalia V. Andrienko",
"Gennady L. Andrienko",
"Andreas Kerren"
] | [] | [] | [] |
VAST | 2,015 | MotionFlow: Visual Abstraction and Aggregation of Sequential Patterns in Human Motion Tracking Data | 10.1109/TVCG.2015.2468292 | Pattern analysis of human motions, which is useful in many research areas, requires understanding and comparison of different styles of motion patterns. However, working with human motion tracking data to support such analysis poses great challenges. In this paper, we propose MotionFlow, a visual analytics system that provides an effective overview of various motion patterns based on an interactive flow visualization. This visualization formulates a motion sequence as transitions between static poses, and aggregates these sequences into a tree diagram to construct a set of motion patterns. The system also allows the users to directly reflect the context of data and their perception of pose similarities in generating representative pose states. We provide local and global controls over the partition-based clustering process. To support the users in organizing unstructured motion data into pattern groups, we designed a set of interactions that enables searching for similar motion sequences from the data, detailed exploration of data subsets, and creating and modifying the group of motion patterns. To evaluate the usability of MotionFlow, we conducted a user study with six researchers with expertise in gesture-based interaction design. They used MotionFlow to explore and organize unstructured motion tracking data. Results show that the researchers were able to easily learn how to use MotionFlow, and the system effectively supported their pattern analysis activities, including leveraging their perception and domain knowledge. | false | false | [
"Sujin Jang",
"Niklas Elmqvist",
"Karthik Ramani"
] | [] | [] | [] |
VAST | 2,015 | PhenoBlocks: Phenotype Comparison Visualizations | 10.1109/TVCG.2015.2467733 | The differential diagnosis of hereditary disorders is a challenging task for clinicians due to the heterogeneity of phenotypes that can be observed in patients. Existing clinical tools are often text-based and do not emphasize consistency, completeness, or granularity of phenotype reporting. This can impede clinical diagnosis and limit their utility to genetics researchers. Herein, we present PhenoBlocks, a novel visual analytics tool that supports the comparison of phenotypes between patients, or between a patient and the hallmark features of a disorder. An informal evaluation of PhenoBlocks with expert clinicians suggested that the visualization effectively guides the process of differential diagnosis and could reinforce the importance of complete, granular phenotypic reporting. | false | false | [
"Michael Glueck",
"Peter Hamilton",
"Fanny Chevalier",
"Simon Breslav",
"Azam Khan",
"Daniel J. Wigdor",
"Michael Brudno"
] | [] | [] | [] |
VAST | 2,015 | Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration | 10.1109/TVCG.2015.2468078 | We propose a visual analytics approach for the exploration and analysis of dynamic networks. We consider snapshots of the network as points in high-dimensional space and project these to two dimensions for visualization and interaction using two juxtaposed views: one for showing a snapshot and one for showing the evolution of the network. With this approach users are enabled to detect stable states, recurring states, outlier topologies, and gain knowledge about the transitions between states and the network evolution in general. The components of our approach are discretization, vectorization and normalization, dimensionality reduction, and visualization and interaction, which are discussed in detail. The effectiveness of the approach is shown by applying it to artificial and real-world dynamic networks. | false | false | [
"Stef van den Elzen",
"Danny Holten",
"Jorik Blaas",
"Jarke J. van Wijk"
] | [
"BP",
"TT"
] | [] | [] |
VAST | 2,015 | SensePath: Understanding the Sensemaking Process Through Analytic Provenance | 10.1109/TVCG.2015.2467611 | Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user's sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process. | false | false | [
"Phong Hai Nguyen",
"Kai Xu 0003",
"Ashley Wheat",
"B. L. William Wong",
"Simon Attfield",
"Bob Fields"
] | [] | [] | [] |
VAST | 2,015 | Sequencing of categorical time series | 10.1109/VAST.2015.7347684 | Exploring and comparing categorical time series and finding temporal patterns are complex tasks in the field of time series data mining. Although different analysis approaches exist, these tasks remain challenging, especially when numerous time series are considered at once. We propose a visual analysis approach that supports exploring such data by ordering time series in meaningful ways. We provide interaction techniques to steer the automated arrangement and to allow users to investigate patterns in detail. | false | false | [
"Christian Richter",
"Martin Luboschik",
"Martin Röhlig",
"Heidrun Schumann"
] | [] | [] | [] |
VAST | 2,015 | StreamVisND: Visualizing relationships in streaming multivariate data | 10.1109/VAST.2015.7347673 | In streaming acquisitions the data changes over time. ThemeRiver and line charts are common methods to display data over time. However, these methods can only show the values of the variables (or attributes) but not the relationships among them over time. We propose a framework we call StreamVis<sup>ND</sup> that can display these types of streaming data relations. It first slices the data stream into different time slices, then it visualizes each slice with a sequence of multivariate 2D data layouts, and finally it flattens this series of displays into a parallel coordinate type display. Our framework is fully interactive and lends itself well to real-time displays. | false | false | [
"Shenghui Cheng",
"Yue Wang",
"Dan Zhang",
"Zhifang Jiang",
"Klaus Mueller 0001"
] | [] | [] | [] |
VAST | 2,015 | Supporting activity recognition by visual analytics | 10.1109/VAST.2015.7347629 | Recognizing activities has become increasingly relevant in many application domains, such as security or ambient assisted living. To handle different scenarios, the underlying automated algorithms are configured using multiple input parameters. However, the influence and interplay of these parameters is often not clear, making exhaustive evaluations necessary. On this account, we propose a visual analytics approach to supporting users in understanding the complex relationships among parameters, recognized activities, and associated accuracies. First, representative parameter settings are determined. Then, the respective output is computed and statistically analyzed to assess parameters' influence in general. Finally, visualizing the parameter settings along with the activities provides overview and allows to investigate the computed results in detail. Coordinated interaction helps to explore dependencies, compare different settings, and examine individual activities. By integrating automated, visual, and interactive means users can select parameter values that meet desired quality criteria. We demonstrate the application of our solution in a use case with realistic complexity, involving a study of human protagonists in daily living with respect to hundreds of parameter settings. | false | false | [
"Martin Röhlig",
"Martin Luboschik",
"Frank Krüger 0001",
"Thomas Kirste",
"Heidrun Schumann",
"Markus Bögl",
"Bilal Alsallakh",
"Silvia Miksch"
] | [] | [] | [] |
VAST | 2,015 | Supporting Iterative Cohort Construction with Visual Temporal Queries | 10.1109/TVCG.2015.2467622 | Many researchers across diverse disciplines aim to analyze the behavior of cohorts whose behaviors are recorded in large event databases. However, extracting cohorts from databases is a difficult yet important step, often overlooked in many analytical solutions. This is especially true when researchers wish to restrict their cohorts to exhibit a particular temporal pattern of interest. In order to fill this gap, we designed COQUITO, a visual interface that assists users defining cohorts with temporal constraints. COQUITO was designed to be comprehensible to domain experts with no preknowledge of database queries and also to encourage exploration. We then demonstrate the utility of COQUITO via two case studies, involving medical and social media researchers. | false | false | [
"Josua Krause",
"Adam Perer",
"Harry Stavropoulos"
] | [] | [] | [] |
VAST | 2,015 | TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems | 10.1109/TVCG.2015.2467196 | Users with anomalous behaviors in online communication systems (e.g. email and social medial platforms) are potential threats to society. Automated anomaly detection based on advanced machine learning techniques has been developed to combat this issue; challenges remain, though, due to the difficulty of obtaining proper ground truth for model training and evaluation. Therefore, substantial human judgment on the automated analysis results is often required to better adjust the performance of anomaly detection. Unfortunately, techniques that allow users to understand the analysis results more efficiently, to make a confident judgment about anomalies, and to explore data in their context, are still lacking. In this paper, we propose a novel visual analysis system, TargetVue, which detects anomalous users via an unsupervised learning model and visualizes the behaviors of suspicious users in behavior-rich context through novel visualization designs and multiple coordinated contextual views. Particularly, TargetVue incorporates three new ego-centric glyphs to visually summarize a user's behaviors which effectively present the user's communication activities, features, and social interactions. An efficient layout method is proposed to place these glyphs on a triangle grid, which captures similarities among users and facilitates comparisons of behaviors of different users. We demonstrate the power of TargetVue through its application in a social bot detection challenge using Twitter data, a case study based on email records, and an interview with expert users. Our evaluation shows that TargetVue is beneficial to the detection of users with anomalous communication behaviors. | false | false | [
"Nan Cao",
"Conglei Shi",
"Wan-Yi Sabrina Lin",
"Jie Lu",
"Yu-Ru Lin",
"Ching-Yung Lin"
] | [] | [] | [] |
VAST | 2,015 | Task-Driven Comparison of Topic Models | 10.1109/TVCG.2015.2467618 | Topic modeling, a method of statistically extracting thematic content from a large collection of texts, is used for a wide variety of tasks within text analysis. Though there are a growing number of tools and techniques for exploring single models, comparisons between models are generally reduced to a small set of numerical metrics. These metrics may or may not reflect a model's performance on the analyst's intended task, and can therefore be insufficient to diagnose what causes differences between models. In this paper, we explore task-centric topic model comparison, considering how we can both provide detail for a more nuanced understanding of differences and address the wealth of tasks for which topic models are used. We derive comparison tasks from single-model uses of topic models, which predominantly fall into the categories of understanding topics, understanding similarity, and understanding change. Finally, we provide several visualization techniques that facilitate these tasks, including buddy plots, which combine color and position encodings to allow analysts to readily view changes in document similarity. | false | false | [
"Eric C. Alexander",
"Michael Gleicher"
] | [] | [] | [] |
VAST | 2,015 | Tell me what do you see: Detecting perceptually-separable visual patterns via clustering of image-space features in visualizations | 10.1109/VAST.2015.7347683 | Visualization helps users infer structures and relationships in the data by encoding information as visual features that can be processed by the human visual-perceptual system. However, users would typically need to expend significant effort to scan and analyze a large number of views before they can begin to recognize relationships in a visualization. We propose a technique to partially automate the process of analyzing visualizations. By deriving and analyzing image-space features from visualizations, we can detect perceptually-separable patterns in the information space. We summarize these patterns with a tree-based meta-visualization and present it to the user to aid exploration. We illustrate this technique with an example scenario involving the analysis of census data. | false | false | [
"Khairi Reda",
"Alberto Gonzalez",
"Jason Leigh",
"Michael E. Papka"
] | [] | [] | [] |
VAST | 2,015 | Temporal MDS Plots for Analysis of Multivariate Data | 10.1109/TVCG.2015.2467553 | Multivariate time series data can be found in many application domains. Examples include data from computer networks, healthcare, social networks, or financial markets. Often, patterns in such data evolve over time among multiple dimensions and are hard to detect. Dimensionality reduction methods such as PCA and MDS allow analysis and visualization of multivariate data, but per se do not provide means to explore multivariate patterns over time. We propose Temporal Multidimensional Scaling (TMDS), a novel visualization technique that computes temporal one-dimensional MDS plots for multivariate data which evolve over time. Using a sliding window approach, MDS is computed for each data window separately, and the results are plotted sequentially along the time axis, taking care of plot alignment. Our TMDS plots enable visual identification of patterns based on multidimensional similarity of the data evolving over time. We demonstrate the usefulness of our approach in the field of network security and show in two case studies how users can iteratively explore the data to identify previously unknown, temporally evolving patterns. | false | false | [
"Dominik Jäckle",
"Fabian Fischer 0001",
"Tobias Schreck",
"Daniel A. Keim"
] | [] | [] | [] |
VAST | 2,015 | The Data Context Map: Fusing Data and Attributes into a Unified Display | 10.1109/TVCG.2015.2467552 | Numerous methods have been described that allow the visualization of the data matrix. But all suffer from a common problem - observing the data points in the context of the attributes is either impossible or inaccurate. We describe a method that allows these types of comprehensive layouts. We achieve it by combining two similarity matrices typically used in isolation - the matrix encoding the similarity of the attributes and the matrix encoding the similarity of the data points. This combined matrix yields two of the four submatrices needed for a full multi-dimensional scaling type layout. The remaining two submatrices are obtained by creating a fused similarity matrix - one that measures the similarity of the data points with respect to the attributes, and vice versa. The resulting layout places the data objects in direct context of the attributes and hence we call it the data context map. It allows users to simultaneously appreciate (1) the similarity of data objects, (2) the similarity of attributes in the specific scope of the collection of data objects, and (3) the relationships of data objects with attributes and vice versa. The contextual layout also allows data regions to be segmented and labeled based on the locations of the attributes. This enables, for example, the map's application in selection tasks where users seek to identify one or more data objects that best fit a certain configuration of factors, using the map to visually balance the tradeoffs. | false | false | [
"Shenghui Cheng",
"Klaus Mueller 0001"
] | [] | [] | [] |
VAST | 2,015 | The Role of Uncertainty, Awareness, and Trust in Visual Analytics | 10.1109/TVCG.2015.2467591 | Visual analytics supports humans in generating knowledge from large and often complex datasets. Evidence is collected, collated and cross-linked with our existing knowledge. In the process, a myriad of analytical and visualisation techniques are employed to generate a visual representation of the data. These often introduce their own uncertainties, in addition to the ones inherent in the data, and these propagated and compounded uncertainties can result in impaired decision making. The user's confidence or trust in the results depends on the extent of user's awareness of the underlying uncertainties generated on the system side. This paper unpacks the uncertainties that propagate through visual analytics systems, illustrates how human's perceptual and cognitive biases influence the user's awareness of such uncertainties, and how this affects the user's trust building. The knowledge generation model for visual analytics is used to provide a terminology and framework to discuss the consequences of these aspects in knowledge construction and though examples, machine uncertainty is compared to human trust measures with provenance. Furthermore, guidelines for the design of uncertainty-aware systems are presented that can aid the user in better decision making. | false | false | [
"Dominik Sacha",
"Hansi Senaratne",
"Bum Chul Kwon",
"Geoffrey P. Ellis",
"Daniel A. Keim"
] | [] | [] | [] |
VAST | 2,015 | The Visual Causality Analyst: An Interactive Interface for Causal Reasoning | 10.1109/TVCG.2015.2467931 | Uncovering the causal relations that exist among variables in multivariate datasets is one of the ultimate goals in data analytics. Causation is related to correlation but correlation does not imply causation. While a number of casual discovery algorithms have been devised that eliminate spurious correlations from a network, there are no guarantees that all of the inferred causations are indeed true. Hence, bringing a domain expert into the casual reasoning loop can be of great benefit in identifying erroneous casual relationships suggested by the discovery algorithm. To address this need we present the Visual Causal Analyst - a novel visual causal reasoning framework that allows users to apply their expertise, verify and edit causal links, and collaborate with the causal discovery algorithm to identify a valid causal network. Its interface consists of both an interactive 2D graph view and a numerical presentation of salient statistical parameters, such as regression coefficients, p-values, and others. Both help users in gaining a good understanding of the landscape of causal structures particularly when the number of variables is large. Our framework is also novel in that it can handle both numerical and categorical variables within one unified model and return plausible results. We demonstrate its use via a set of case studies using multiple practical datasets. | false | false | [
"Jun Wang",
"Klaus Mueller 0001"
] | [] | [] | [] |
VAST | 2,015 | TimeLineCurator: Interactive Authoring of Visual Timelines from Unstructured Text | 10.1109/TVCG.2015.2467531 | We present TimeLineCurator, a browser-based authoring tool that automatically extracts event data from temporal references in unstructured text documents using natural language processing and encodes them along a visual timeline. Our goal is to facilitate the timeline creation process for journalists and others who tell temporal stories online. Current solutions involve manually extracting and formatting event data from source documents, a process that tends to be tedious and error prone. With TimeLineCurator, a prospective timeline author can quickly identify the extent of time encompassed by a document, as well as the distribution of events occurring along this timeline. Authors can speculatively browse possible documents to quickly determine whether they are appropriate sources of timeline material. TimeLineCurator provides controls for curating and editing events on a timeline, the ability to combine timelines from multiple source documents, and export curated timelines for online deployment. We evaluate TimeLineCurator through a benchmark comparison of entity extraction error against a manual timeline curation process, a preliminary evaluation of the user experience of timeline authoring, a brief qualitative analysis of its visual output, and a discussion of prospective use cases suggested by members of the target author communities following its deployment. | false | false | [
"Johanna Fulda",
"Matthew Brehmer",
"Tamara Munzner"
] | [] | [] | [] |
VAST | 2,015 | TimeStitch: Interactive multi-focus cohort discovery and comparison | 10.1109/VAST.2015.7347682 | Whereas event-based timelines for healthcare enable users to visualize the chronology of events surrounding events of interest, they are often not designed to aid the discovery, construction, or comparison of associated cohorts. We present TimeStitch, a system that helps health researchers discover and understand events that may cause abstinent smokers to lapse. TimeStitch extracts common sequences of events performed by abstinent smokers from large amounts of mobile health sensor data, and offers a suite of interactive and visualization techniques to enable cohort discovery, construction, and comparison, using extracted sequences as interactive elements. We are extending TimeStitch to support more complex health conditions with high mortality risk, such as reducing hospital readmission in congestive heart failure. | false | false | [
"Peter J. Polack Jr.",
"Shang-Tse Chen",
"Minsuk Kahng",
"Moushumi Sharmin",
"Polo Chau"
] | [] | [] | [] |
VAST | 2,015 | Topicks: Visualizing complex topic models for user comprehension | 10.1109/VAST.2015.7347681 | The interactive visualization of topic models is a promising approach to summarizing large sets of textual data. Topicks is the working title for a means to visualize topic modelling outputs. Incorporating a radial layout, users can view the relationships between topics, terms and the corpus as a whole. Interacting with topic and term nodes, as well as a related bar chart, provides the user with various ways to manipulate the visualization and explore the data. We describe the visualization and potential user interactions before discussing future work. | false | false | [
"Jessica Peter",
"Steve James Szigeti",
"Ana Jofre",
"Sara Diamond"
] | [] | [] | [] |
VAST | 2,015 | TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data | 10.1109/TVCG.2015.2467771 | We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis. | false | false | [
"Xiaoke Huang",
"Ye Zhao 0003",
"Chao Ma 0023",
"Jing Yang 0001",
"Xinyue Ye",
"Chong Zhang"
] | [] | [] | [] |
VAST | 2,015 | Trending pool: Visual analytics for trending event compositions for time-series categorical log data | 10.1109/VAST.2015.7347688 | Although many visualization tools provide us plenty of ways to view the data, users can not easily find the trending events and their explanation from the data. In this work, we address the issue by leveraging the real music streaming log data as an example to better understand a million-scale dataset. Trending event explanation turns out to be challenging when it comes to categorical log data. Therefore, we propose to use a learning-based method with an interface design to uncover the trending event compositions for time-series categorical log data, which can be extend to other datasets, e.g., the hashtags in social media. First, we perform “trending pool” operation to save the memory and time cost. Second, we apply sparse coding to learn important trending candidate combination sets instead of traditional brute-force way or manual investigation for generating combinations. Besides the contributions above, we also observe some interesting user behaviors by exploring detected trending candidate combinations visually through our interface. | false | false | [
"Yi-Chih Tsai",
"Liang-Chi Hsieh",
"Wen-Feng Cheng",
"Yin-Hsi Kuo",
"Winston H. Hsu",
"Wen-Chin Chen"
] | [] | [] | [] |
VAST | 2,015 | uRank: Visual analytics approach for search result exploration | 10.1109/VAST.2015.7347686 | uRank is a Web-based tool combining lightweight text analytics and visual methods for topic-wise exploration of document sets. It includes a view summarizing the content of the document set in meaningful terms, a dynamic document ranking view and a detailed view for further inspection of individual documents. Its major strength lies in how it supports users in reorganizing documents on-the-fly as their information interests change. We present a preliminary evaluation showing that uRank helps to reduce cognitive load compared to a traditional list-based representation. | false | false | [
"Cecilia di Sciascio",
"Vedran Sabol",
"Eduardo E. Veas"
] | [] | [] | [] |
VAST | 2,015 | Urbane: A 3D framework to support data driven decision making in urban development | 10.1109/VAST.2015.7347636 | Architects working with developers and city planners typically rely on experience, precedent and data analyzed in isolation when making decisions that impact the character of a city. These decisions are critical in enabling vibrant, sustainable environments but must also negotiate a range of complex political and social forces. This requires those shaping the built environment to balance maximizing the value of a new development with its impact on the character of a neighborhood. As a result architects are focused on two issues throughout the decision making process: a) what defines the character of a neighborhood? and b) how will a new development change its neighborhood? In the first, character can be influenced by a variety of factors and understanding the interplay between diverse data sets is crucial; including safety, transportation access, school quality and access to entertainment. In the second, the impact of a new development is measured, for example, by how it impacts the view from the buildings that surround it. In this paper, we work in collaboration with architects to design Urbane, a 3-dimensional multi-resolution framework that enables a data-driven approach for decision making in the design of new urban development. This is accomplished by integrating multiple data layers and impact analysis techniques facilitating architects to explore and assess the effect of these attributes on the character and value of a neighborhood. Several of these data layers, as well as impact analysis, involve working in 3-dimensions and operating in real time. Efficient computation and visualization is accomplished through the use of techniques from computer graphics. We demonstrate the effectiveness of Urbane through a case study of development in Manhattan depicting how a data-driven understanding of the value and impact of speculative buildings can benefit the design-development process between architects, planners and developers. | false | false | [
"Nivan Ferreira",
"Marcos Lage",
"Harish Doraiswamy",
"Huy T. Vo",
"Luc Wilson",
"Heidi Werner",
"Muchan Park",
"Cláudio T. Silva"
] | [] | [] | [] |
VAST | 2,015 | Using visualization and analysis with efficient dimension Reduction to determine underlying factors in hospital inpatient procedure costs | 10.1109/VAST.2015.7347680 | The Centers for Medicare and Medicaid Services (CMS) has made public a data set showing what hospitals charged and what Medicare paid for the one hundred most common inpatient stays. Here we present the application of Reduced Basis Decomposition (RBD), an efficient novel dimension reduction algorithm for data processing, to the CMS data. This was paired with a comparative visual exploration of the results when put into context with characteristics of the hospitals and marketplaces in which they operate. We used Weave Analyst, a new web-based analysis and visualization environment, to visualize the relationship between the hospital groups, their charge levels, and distinguishing indicator variables. Particular insights to the relatively small number of underlying factors that exert greatest influence on hospital pricing surfaced thanks to the combined synergetic integration of the modeling, reduction, and visualization techniques. | false | false | [
"Miriam Perkins",
"Yanlai Chen"
] | [] | [] | [] |
VAST | 2,015 | VA2: A Visual Analytics Approach for Evaluating Visual Analytics Applications | 10.1109/TVCG.2015.2467871 | Evaluation has become a fundamental part of visualization research and researchers have employed many approaches from the field of human-computer interaction like measures of task performance, thinking aloud protocols, and analysis of interaction logs. Recently, eye tracking has also become popular to analyze visual strategies of users in this context. This has added another modality and more data, which requires special visualization techniques to analyze this data. However, only few approaches exist that aim at an integrated analysis of multiple concurrent evaluation procedures. The variety, complexity, and sheer amount of such coupled multi-source data streams require a visual analytics approach. Our approach provides a highly interactive visualization environment to display and analyze thinking aloud, interaction, and eye movement data in close relation. Automatic pattern finding algorithms allow an efficient exploratory search and support the reasoning process to derive common eye-interaction-thinking patterns between participants. In addition, our tool equips researchers with mechanisms for searching and verifying expected usage patterns. We apply our approach to a user study involving a visual analytics application and we discuss insights gained from this joint analysis. We anticipate our approach to be applicable to other combinations of evaluation techniques and a broad class of visualization applications. | false | false | [
"Tanja Blascheck",
"Markus John",
"Kuno Kurzhals",
"Steffen Koch 0001",
"Thomas Ertl"
] | [
"HM"
] | [] | [] |
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