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 |
|---|---|---|---|---|---|---|---|---|---|---|
VAST | 2,014 | An Insight- and Task-based Methodology for Evaluating Spatiotemporal Visual Analytics | 10.1109/VAST.2014.7042482 | We present a method for evaluating visualizations using both tasks and exploration, and demonstrate this method in a study of spatiotemporal network designs for a visual analytics system. The method is well suited for studying visual analytics applications in which users perform both targeted data searches and analyses of broader patterns. In such applications, an effective visualization design is one that helps users complete tasks accurately and efficiently, and supports hypothesis generation during open-ended exploration. To evaluate both of these aims in a single study, we developed an approach called layered insight- and task-based evaluation (LITE) that interposes several prompts for observations about the data model between sequences of predefined search tasks. We demonstrate the evaluation method in a user study of four network visualizations for spatiotemporal data in a visual analytics application. Results include findings that might have been difficult to obtain in a single experiment using a different methodology. For example, with one dataset we studied, we found that on average participants were faster on search tasks using a force-directed layout than using our other designs; at the same time, participants found this design least helpful in understanding the data. Our contributions include a novel evaluation method that combines well-defined tasks with exploration and observation, an evaluation of network visualization designs for spatiotemporal visual analytics, and guidelines for using this evaluation method. | false | false | [
"Steven R. Gomez",
"Hua Guo",
"Caroline Ziemkiewicz",
"David H. Laidlaw"
] | [] | [] | [] |
VAST | 2,014 | An Integrated Visual Analysis System for Fusing MR Spectroscopy and Multi-Modal Radiology Imaging | 10.1109/VAST.2014.7042481 | For cancers such as glioblastoma multiforme, there is an increasing interest in defining "biological target volumes" (BTV), high tumour-burden regions which may be targeted with dose boosts in radiotherapy. The definition of a BTV requires insight into tumour characteristics going beyond conventionally defined radiological abnormalities and anatomical features. Molecular and biochemical imaging techniques, like positron emission tomography, the use of Magnetic Resonance (MR) Imaging contrast agents or MR Spectroscopy deliver this information and support BTV delineation. MR Spectroscopy Imaging (MRSI) is the only non-invasive technique in this list. Studies with MRSI have shown that voxels with certain metabolic signatures are more susceptible to predict the site of relapse. Nevertheless, the discovery of complex relationships between a high number of different metabolites, anatomical, molecular and functional features is an ongoing topic of research - still lacking appropriate tools supporting a smooth workflow by providing data integration and fusion of MRSI data with other imaging modalities. We present a solution bridging this gap which gives fast and flexible access to all data at once. By integrating a customized visualization of the multi-modal and multi-variate image data with a highly flexible visual analytics (VA) framework, it is for the first time possible to interactively fuse, visualize and explore user defined metabolite relations derived from MRSI in combination with markers delivered by other imaging modalities. Real-world medical cases demonstrate the utility of our solution. By making MRSI data available both in a VA tool and in a multi-modal visualization renderer we can combine insights from each side to arrive at a superior BTV delineation. We also report feedback from domain experts indicating significant positive impact in how this work can improve the understanding of MRSI data and its integration into radiotherapy planning. | false | false | [
"Miguel Nunes",
"Benjamin Rowland",
"Matthias Schlachter",
"Soléakhéna Ken",
"Kresimir Matkovic",
"Anne Laprie",
"Katja Bühler"
] | [] | [] | [] |
VAST | 2,014 | Analyzing High-dimensional Multivariate Network Links with Integrated Anomaly Detection, Highlighting and Exploration | 10.1109/VAST.2014.7042484 | This paper focuses on the integration of a family of visual analytics techniques for analyzing high-dimensional, multivariate network data that features spatial and temporal information, network connections, and a variety of other categorical and numerical data types. Such data types are commonly encountered in transportation, shipping, and logistics industries. Due to the scale and complexity of the data, it is essential to integrate techniques for data analysis, visualization, and exploration. We present new visual representations, Petal and Thread, to effectively present many-to-many network data including multi-attribute vectors. In addition, we deploy an information-theoretic model for anomaly detection across varying dimensions, displaying highlighted anomalies in a visually consistent manner, as well as supporting a managed process of exploration. Lastly, we evaluate the proposed methodology through data exploration and an empirical study. | false | false | [
"Sungahn Ko",
"Shehzad Afzal",
"Simon J. Walton",
"Yang Yang",
"Junghoon Chae",
"Abish Malik",
"Yun Jang",
"Min Chen 0001",
"David S. Ebert"
] | [] | [] | [] |
VAST | 2,014 | Baseball4D: A Tool for Baseball Game Reconstruction & Visualization | 10.1109/VAST.2014.7042478 | While many sports use statistics and video to analyze and improve game play, baseball has led the charge throughout its history. With the advent of new technologies that allow all players and the ball to be tracked across the entire field, it is now possible to bring this understanding to another level. From discrete positions across time, we present techniques to reconstruct entire baseball games and visually explore each play. This provides opportunities to not only derive new metrics for the game, but also allow us to investigate existing measures with targeted visualizations. In addition, our techniques allow users to filter on demand so specific situations can be analyzed both in general and according to those situations. We show that gameplay can be accurately reconstructed from the raw position data and discuss how visualization and statistical methods can combine to better inform baseball analyses. | false | false | [
"Carlos A. Dietrich",
"David Koop",
"Huy T. Vo",
"Cláudio T. Silva"
] | [] | [] | [] |
VAST | 2,014 | BoundarySeer: Visual Analysis of 2D Boundary Changes | 10.1109/VAST.2014.7042490 | Boundary changes exist ubiquitously in our daily life. From the Antarctic ozone hole to the land desertification, and from the territory of a country to the area within one-hour reach from a downtown location, boundaries change over time. With a large number of time-varying boundaries recorded, people often need to analyze the changes, detect their similarities or differences, and find out spatial and temporal patterns of the evolution for various applications. In this paper, we present a comprehensive visual analytics system, BoundarySeer, to help users gain insight into the changes of boundaries. Our system consists of four major viewers: 1) a global viewer to show boundary groups based on their similarity and the distribution of boundary attributes such as smoothness and perimeter; 2) a region viewer to display the regions encircled by the boundaries and how they are affected by boundary changes; 3) a trend viewer to reveal the temporal patterns in the boundary evolution and potential spatio-temporal correlations; 4) a directional change viewer to encode movements of boundary segments in different directions. Quantitative analyses of boundaries (e.g., similarity measurement and adaptive clustering) and intuitive visualizations (e.g., density map and ThemeRiver) are integrated into these viewers, which enable users to explore boundary changes from different aspects and at different scales. Case studies with two real-world datasets have been carried out to demonstrate the effectiveness of our system. | false | false | [
"Wenchao Wu",
"Yixian Zheng",
"Huamin Qu",
"Wei Chen 0001",
"M. Eduard Gröller",
"Lionel M. Ni"
] | [] | [] | [] |
VAST | 2,014 | ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery | 10.1109/TVCG.2014.2346752 | Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms. | false | false | [
"Christian Partl",
"Alexander Lex",
"Marc Streit",
"Hendrik Strobelt",
"Anne Mai Wassermann",
"Hanspeter Pfister",
"Dieter Schmalstieg"
] | [] | [] | [] |
VAST | 2,014 | Cupid: Cluster-Based Exploration of Geometry Generators with Parallel Coordinates and Radial Trees | 10.1109/TVCG.2014.2346626 | Geometry generators are commonly used in video games and evaluation systems for computer vision to create geometric shapes such as terrains, vegetation or airplanes. The parameters of the generator are often sampled automatically which can lead to many similar or unwanted geometric shapes. In this paper, we propose a novel visual exploration approach that combines the abstract parameter space of the geometry generator with the resulting 3D shapes in a composite visualization. Similar geometric shapes are first grouped using hierarchical clustering and then nested within an illustrative parallel coordinates visualization. This helps the user to study the sensitivity of the generator with respect to its parameter space and to identify invalid parameter settings. Starting from a compact overview representation, the user can iteratively drill-down into local shape differences by clicking on the respective clusters. Additionally, a linked radial tree gives an overview of the cluster hierarchy and enables the user to manually split or merge clusters. We evaluate our approach by exploring the parameter space of a cup generator and provide feedback from domain experts. | false | false | [
"Michael Beham",
"Wolfgang Herzner",
"M. Eduard Gröller",
"Johannes Kehrer"
] | [] | [] | [] |
VAST | 2,014 | DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data | 10.1109/TVCG.2014.2346682 | Temporal event sequence data is increasingly commonplace, with applications ranging from electronic medical records to financial transactions to social media activity. Previously developed techniques have focused on low-dimensional datasets (e.g., with less than 20 distinct event types). Real-world datasets are often far more complex. This paper describes DecisionFlow, a visual analysis technique designed to support the analysis of high-dimensional temporal event sequence data (e.g., thousands of event types). DecisionFlow combines a scalable and dynamic temporal event data structure with interactive multi-view visualizations and ad hoc statistical analytics. We provide a detailed review of our methods, and present the results from a 12-person user study. The study results demonstrate that DecisionFlow enables the quick and accurate completion of a range of sequence analysis tasks for datasets containing thousands of event types and millions of individual events. | false | false | [
"David Gotz",
"Harry Stavropoulos"
] | [] | [] | [] |
VAST | 2,014 | DIA2: Web-based Cyberinfrastructure for Visual Analysis of Funding Portfolios | 10.1109/TVCG.2014.2346747 | We present a design study of the Deep Insights Anywhere, Anytime (DIA2) platform, a web-based visual analytics system that allows program managers and academic staff at the U.S. National Science Foundation to search, view, and analyze their research funding portfolio. The goal of this system is to facilitate users' understanding of both past and currently active research awards in order to make more informed decisions of their future funding. This user group is characterized by high domain expertise yet not necessarily high literacy in visualization and visual analytics-they are essentially casual experts-and thus require careful visual and information design, including adhering to user experience standards, providing a self-instructive interface, and progressively refining visualizations to minimize complexity. We discuss the challenges of designing a system for casual experts and highlight how we addressed this issue by modeling the organizational structure and workflows of the NSF within our system. We discuss each stage of the design process, starting with formative interviews, prototypes, and finally live deployments and evaluation with stakeholders. | false | false | [
"Krishna P. C. Madhavan",
"Niklas Elmqvist",
"Mihaela Vorvoreanu",
"Xin Chen",
"Yuet Ling Wong",
"Hanjun Xian",
"Zhihua Dong",
"Aditya Johri"
] | [] | [] | [] |
VAST | 2,014 | EvoRiver: Visual Analysis of Topic Coopetition on Social Media | 10.1109/TVCG.2014.2346919 | Cooperation and competition (jointly called “coopetition”) are two modes of interactions among a set of concurrent topics on social media. How do topics cooperate or compete with each other to gain public attention? Which topics tend to cooperate or compete with one another? Who plays the key role in coopetition-related interactions? We answer these intricate questions by proposing a visual analytics system that facilitates the in-depth analysis of topic coopetition on social media. We model the complex interactions among topics as a combination of carry-over, coopetition recruitment, and coopetition distraction effects. This model provides a close functional approximation of the coopetition process by depicting how different groups of influential users (i.e., “topic leaders”) affect coopetition. We also design EvoRiver, a time-based visualization, that allows users to explore coopetition-related interactions and to detect dynamically evolving patterns, as well as their major causes. We test our model and demonstrate the usefulness of our system based on two Twitter data sets (social topics data and business topics data). | false | false | [
"Guodao Sun",
"Yingcai Wu",
"Shixia Liu",
"Tai-Quan Peng",
"Jonathan J. H. Zhu",
"Ronghua Liang"
] | [] | [] | [] |
VAST | 2,014 | Feature-Driven Visual Analytics of Soccer Data | 10.1109/VAST.2014.7042477 | Soccer is one the most popular sports today and also very interesting from an scientific point of view. We present a system for analyzing high-frequency position-based soccer data at various levels of detail, allowing to interactively explore and analyze for movement features and game events. Our Visual Analytics method covers single-player, multi-player and event-based analytical views. Depending on the task the most promising features are semi-automatically selected, processed, and visualized. Our aim is to help soccer analysts in finding the most important and interesting events in a match. We present a flexible, modular, and expandable layer-based system allowing in-depth analysis. The integration of Visual Analytics techniques into the analysis process enables the analyst to find interesting events based on classification and allows, by a set of custom views, to communicate the found results. The feedback loop in the Visual Analytics pipeline helps to further improve the classification results. We evaluate our approach by investigating real-world soccer matches and collecting additional expert feedback. Several use cases and findings illustrate the capabilities of our approach. | false | false | [
"Halldór Janetzko",
"Dominik Sacha",
"Manuel Stein",
"Tobias Schreck",
"Daniel A. Keim",
"Oliver Deussen"
] | [] | [] | [] |
VAST | 2,014 | Feedback-Driven Interactive Exploration of Large Multidimensional Data Supported by Visual Classifier | 10.1109/VAST.2014.7042480 | The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user- and context-dependent visual exploration systems. | false | false | [
"Michael Behrisch 0001",
"Fatih Korkmaz",
"Lin Shao 0001",
"Tobias Schreck"
] | [] | [] | [] |
VAST | 2,014 | Finding Waldo: Learning about Users from their Interactions | 10.1109/TVCG.2014.2346575 | Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user's interactions with a system reflect a large amount of the user's reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user's task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, we conduct an experiment in which participants perform a visual search task, and apply well-known machine learning algorithms to three encodings of the users' interaction data. We achieve, depending on algorithm and encoding, between 62% and 83% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user's personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time: in one case 95% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed-initiative visual analytics systems. | false | false | [
"Eli T. Brown",
"Alvitta Ottley",
"Helen Zhao",
"Quan Lin",
"Richard Souvenir",
"Alex Endert",
"Remco Chang"
] | [] | [] | [] |
VAST | 2,014 | Footprints: A Visual Search Tool that Supports Discovery and Coverage Tracking | 10.1109/TVCG.2014.2346743 | Searching a large document collection to learn about a broad subject involves the iterative process of figuring out what to ask, filtering the results, identifying useful documents, and deciding when one has covered enough material to stop searching. We are calling this activity “discoverage,” discovery of relevant material and tracking coverage of that material. We built a visual analytic tool called Footprints that uses multiple coordinated visualizations to help users navigate through the discoverage process. To support discovery, Footprints displays topics extracted from documents that provide an overview of the search space and are used to construct searches visuospatially. Footprints allows users to triage their search results by assigning a status to each document (To Read, Read, Useful), and those status markings are shown on interactive histograms depicting the user's coverage through the documents across dates, sources, and topics. Coverage histograms help users notice biases in their search and fill any gaps in their analytic process. To create Footprints, we used a highly iterative, user-centered approach in which we conducted many evaluations during both the design and implementation stages and continually modified the design in response to feedback. | false | false | [
"Ellen Isaacs",
"Kelly Domico",
"Shane Ahern",
"Eugene Bart",
"Mudita Singhal"
] | [] | [] | [] |
VAST | 2,014 | Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks | 10.1109/TVCG.2014.2346753 | Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness). | false | false | [
"Bowen Yu 0004",
"Harish Doraiswamy",
"Xi Chen",
"Emily R. Miraldi",
"Mario Luis Arrieta-Ortiz",
"Christoph Hafemeister",
"Aviv Madar",
"Richard Bonneau",
"Cláudio T. Silva"
] | [] | [] | [] |
VAST | 2,014 | HydroQual: Visual Analysis of River Water Quality | 10.1109/VAST.2014.7042488 | Economic development based on industrialization, intensive agriculture expansion and population growth places greater pressure on water resources through increased water abstraction and water quality degradation [40], River pollution is now a visible issue, with emblematic ecological disasters following industrial accidents such as the pollution of the Rhine river in 1986 [31]. River water quality is a pivotal public health and environmental issue that has prompted governments to plan initiatives for preserving or restoring aquatic ecosystems and water resources [56], Water managers require operational tools to help interpret the complex range of information available on river water quality functioning. Tools based on statistical approaches often fail to resolve some tasks due to the sparse nature of the data. Here we describe HydroQual, a tool to facilitate visual analysis of river water quality. This tool combines spatiotemporal data mining and visualization techniques to perform tasks defined by water experts. We illustrate the approach with a case study that illustrates how the tool helps experts analyze water quality. We also perform a qualitative evaluation with these experts. | false | false | [
"Pierre Accorsi",
"Nathalie Lalande",
"Mickaël Fabrègue",
"Agnès Braud",
"Pascal Poncelet",
"Arnaud Sallaberry",
"Sandra Bringay",
"Maguelonne Teisseire",
"Flavie Cernesson",
"Florence Le Ber"
] | [] | [] | [] |
VAST | 2,014 | INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data | 10.1109/TVCG.2014.2346482 | Predictive modeling techniques are increasingly being used by data scientists to understand the probability of predicted outcomes. However, for data that is high-dimensional, a critical step in predictive modeling is determining which features should be included in the models. Feature selection algorithms are often used to remove non-informative features from models. However, there are many different classes of feature selection algorithms. Deciding which one to use is problematic as the algorithmic output is often not amenable to user interpretation. This limits the ability for users to utilize their domain expertise during the modeling process. To improve on this limitation, we developed INFUSE, a novel visual analytics system designed to help analysts understand how predictive features are being ranked across feature selection algorithms, cross-validation folds, and classifiers. We demonstrate how our system can lead to important insights in a case study involving clinical researchers predicting patient outcomes from electronic medical records. | false | false | [
"Josua Krause",
"Adam Perer",
"Enrico Bertini"
] | [] | [] | [] |
VAST | 2,014 | Integrating Predictive Analytics and Social Media | 10.1109/VAST.2014.7042495 | A key analytical task across many domains is model building and exploration for predictive analysis. Data is collected, parsed and analyzed for relationships, and features are selected and mapped to estimate the response of a system under exploration. As social media data has grown more abundant, data can be captured that may potentially represent behavioral patterns in society. In turn, this unstructured social media data can be parsed and integrated as a key factor for predictive intelligence. In this paper, we present a framework for the development of predictive models utilizing social media data. We combine feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. In order to explore how predictions might be performed in such a framework, we present results from a user study focusing on social media data as a predictor for movie box-office success. | false | false | [
"Yafeng Lu",
"Robert Krüger",
"Dennis Thom",
"Feng Wang 0012",
"Steffen Koch 0001",
"Thomas Ertl",
"Ross Maciejewski"
] | [] | [] | [] |
VAST | 2,014 | Interactive Visual Analysis of Image-Centric Cohort Study Data | 10.1109/TVCG.2014.2346591 | Epidemiological population studies impose information about a set of subjects (a cohort) to characterize disease-specific risk factors. Cohort studies comprise heterogenous variables describing the medical condition as well as demographic and lifestyle factors and, more recently, medical image data. We propose an Interactive Visual Analysis (IVA) approach that enables epidemiologists to rapidly investigate the entire data pool for hypothesis validation and generation. We incorporate image data, which involves shape-based object detection and the derivation of attributes describing the object shape. The concurrent investigation of image-based and non-image data is realized in a web-based multiple coordinated view system, comprising standard views from information visualization and epidemiological data representations such as pivot tables. The views are equipped with brushing facilities and augmented by 3D shape renderings of the segmented objects, e.g., each bar in a histogram is overlaid with a mean shape of the associated subgroup of the cohort. We integrate an overview visualization, clustering of variables and object shape for data-driven subgroup definition and statistical key figures for measuring the association between variables. We demonstrate the IVA approach by validating and generating hypotheses related to lower back pain as part of a qualitative evaluation. | false | false | [
"Paul Klemm",
"Steffen Oeltze-Jafra",
"Kai Lawonn",
"Katrin Hegenscheid",
"Henry Völzke",
"Bernhard Preim"
] | [] | [] | [] |
VAST | 2,014 | Knowledge Generation Model for Visual Analytics | 10.1109/TVCG.2014.2346481 | Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused so they do not encompass different perspectives at different levels. This paper proposes a knowledge generation model for visual analytics that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes. To test its utility, a real world visual analytics system is compared against the model, demonstrating that the knowledge generation process model provides a useful guideline when developing and evaluating such systems. The model is used to effectively compare different data analysis systems. Furthermore, the model provides a common language and description of visual analytic processes, which can be used for communication between researchers. At the end, our model reflects areas of research that future researchers can embark on. | false | false | [
"Dominik Sacha",
"Andreas Stoffel",
"Florian Stoffel",
"Bum Chul Kwon",
"Geoffrey P. Ellis",
"Daniel A. Keim"
] | [
"TT"
] | [] | [] |
VAST | 2,014 | LoyalTracker: Visualizing Loyalty Dynamics in Search Engines | 10.1109/TVCG.2014.2346912 | The huge amount of user log data collected by search engine providers creates new opportunities to understand user loyalty and defection behavior at an unprecedented scale. However, this also poses a great challenge to analyze the behavior and glean insights into the complex, large data. In this paper, we introduce LoyalTracker, a visual analytics system to track user loyalty and switching behavior towards multiple search engines from the vast amount of user log data. We propose a new interactive visualization technique (flow view) based on a flow metaphor, which conveys a proper visual summary of the dynamics of user loyalty of thousands of users over time. Two other visualization techniques, a density map and a word cloud, are integrated to enable analysts to gain further insights into the patterns identified by the flow view. Case studies and the interview with domain experts are conducted to demonstrate the usefulness of our technique in understanding user loyalty and switching behavior in search engines. | false | false | [
"Conglei Shi",
"Yingcai Wu",
"Shixia Liu",
"Hong Zhou 0004",
"Huamin Qu"
] | [
"HM"
] | [] | [] |
VAST | 2,014 | Multi-Model Semantic Interaction for Text Analytics | 10.1109/VAST.2014.7042492 | Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. However, this technique is not inherently scalable past hundreds of text documents. To remedy this, we present the concept of multi-model semantic interaction, where semantic interactions can be used to steer multiple models at multiple levels of data scale, enabling users to tackle larger data problems. We also present an updated visualization pipeline model for generalized multi-model semantic interaction. To demonstrate multi-model semantic interaction, we introduce StarSPIRE, a visual text analytics prototype that transforms user interactions on documents into both small-scale display layout updates as well as large-scale relevancy-based document selection. | false | false | [
"Lauren Bradel",
"Chris North 0001",
"Leanna House",
"Scotland Leman"
] | [] | [] | [] |
VAST | 2,014 | Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations | 10.1109/TVCG.2014.2346578 | An increasing number of interactive visualization tools stress the integration with computational software like MATLAB and R to access a variety of proven algorithms. In many cases, however, the algorithms are used as black boxes that run to completion in isolation which contradicts the needs of interactive data exploration. This paper structures, formalizes, and discusses possibilities to enable user involvement in ongoing computations. Based on a structured characterization of needs regarding intermediate feedback and control, the main contribution is a formalization and comparison of strategies for achieving user involvement for algorithms with different characteristics. In the context of integration, we describe considerations for implementing these strategies either as part of the visualization tool or as part of the algorithm, and we identify requirements and guidelines for the design of algorithmic APIs. To assess the practical applicability, we provide a survey of frequently used algorithm implementations within R regarding the fulfillment of these guidelines. While echoing previous calls for analysis modules which support data exploration more directly, we conclude that a range of pragmatic options for enabling user involvement in ongoing computations exists on both the visualization and algorithm side and should be used. | false | false | [
"Thomas Mühlbacher",
"Harald Piringer",
"Samuel Gratzl",
"Michael Sedlmair",
"Marc Streit"
] | [] | [] | [] |
VAST | 2,014 | OpinionFlow: Visual Analysis of Opinion Diffusion on Social Media | 10.1109/TVCG.2014.2346920 | It is important for many different applications such as government and business intelligence to analyze and explore the diffusion of public opinions on social media. However, the rapid propagation and great diversity of public opinions on social media pose great challenges to effective analysis of opinion diffusion. In this paper, we introduce a visual analysis system called OpinionFlow to empower analysts to detect opinion propagation patterns and glean insights. Inspired by the information diffusion model and the theory of selective exposure, we develop an opinion diffusion model to approximate opinion propagation among Twitter users. Accordingly, we design an opinion flow visualization that combines a Sankey graph with a tailored density map in one view to visually convey diffusion of opinions among many users. A stacked tree is used to allow analysts to select topics of interest at different levels. The stacked tree is synchronized with the opinion flow visualization to help users examine and compare diffusion patterns across topics. Experiments and case studies on Twitter data demonstrate the effectiveness and usability of OpinionFlow. | false | false | [
"Yingcai Wu",
"Shixia Liu",
"Kai Yan",
"Mengchen Liu",
"Fangzhao Wu"
] | [] | [] | [] |
VAST | 2,014 | PEARL: An Interactive Visual Analytic Tool for Understanding Personal Emotion Style Derived from Social Media | 10.1109/VAST.2014.7042496 | Hundreds of millions of people leave digital footprints on social media (e.g., Twitter and Facebook). Such data not only disclose a person's demographics and opinions, but also reveal one's emotional style. Emotional style captures a person's patterns of emotions over time, including his overall emotional volatility and resilience. Understanding one's emotional style can provide great benefits for both individuals and businesses alike, including the support of self-reflection and delivery of individualized customer care. We present PEARL, a timeline-based visual analytic tool that allows users to interactively discover and examine a person's emotional style derived from this person's social media text. Compared to other visual text analytic systems, our work offers three unique contributions. First, it supports multi-dimensional emotion analysis from social media text to automatically detect a person's expressed emotions at different time points and summarize those emotions to reveal the person's emotional style. Second, it effectively visualizes complex, multi-dimensional emotion analysis results to create a visual emotional profile of an individual, which helps users browse and interpret one's emotional style. Third, it supports rich visual interactions that allow users to interactively explore and validate emotion analysis results. We have evaluated our work extensively through a series of studies. The results demonstrate the effectiveness of our tool both in emotion analysis from social media and in support of interactive visualization of the emotion analysis results. | false | false | [
"Jian Zhao 0010",
"Liang Gou",
"Fei Wang",
"Michelle X. Zhou"
] | [] | [] | [] |
VAST | 2,014 | Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement | 10.1109/TVCG.2014.2346926 | In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets. | false | false | [
"Abish Malik",
"Ross Maciejewski",
"Sherry Towers",
"Sean McCullough",
"David S. Ebert"
] | [] | [] | [] |
VAST | 2,014 | Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics | 10.1109/TVCG.2014.2346574 | As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records. | false | false | [
"Charles D. Stolper",
"Adam Perer",
"David Gotz"
] | [] | [] | [] |
VAST | 2,014 | Run Watchers: Automatic Simulation-Based Decision Support in Flood Management | 10.1109/TVCG.2014.2346930 | In this paper, we introduce a simulation-based approach to design protection plans for flood events. Existing solutions require a lot of computation time for an exhaustive search, or demand for a time-consuming expert supervision and steering. We present a faster alternative based on the automated control of multiple parallel simulation runs. Run Watchers are dedicated system components authorized to monitor simulation runs, terminate them, and start new runs originating from existing ones according to domain-specific rules. This approach allows for a more efficient traversal of the search space and overall performance improvements due to a re-use of simulated states and early termination of failed runs. In the course of search, Run Watchers generate large and complex decision trees. We visualize the entire set of decisions made by Run Watchers using interactive, clustered timelines. In addition, we present visualizations to explain the resulting response plans. Run Watchers automatically generate storyboards to convey plan details and to justify the underlying decisions, including those which leave particular buildings unprotected. We evaluate our solution with domain experts. | false | false | [
"Artem Konev",
"Jürgen Waser",
"Bernhard Sadransky",
"Daniel Cornel",
"Rui A. P. Perdigão",
"Zsolt Horváth",
"M. Eduard Gröller"
] | [] | [] | [] |
VAST | 2,014 | Serendip: Topic Model-Driven Visual Exploration of Text Corpora | 10.1109/VAST.2014.7042493 | Exploration and discovery in a large text corpus requires investigation at multiple levels of abstraction, from a zoomed-out view of the entire corpus down to close-ups of individual passages and words. At each of these levels, there is a wealth of information that can inform inquiry - from statistical models, to metadata, to the researcher's own knowledge and expertise. Joining all this information together can be a challenge, and there are issues of scale to be combatted along the way. In this paper, we describe an approach to text analysis that addresses these challenges of scale and multiple information sources, using probabilistic topic models to structure exploration through multiple levels of inquiry in a way that fosters serendipitous discovery. In implementing this approach into a tool called Serendip, we incorporate topic model data and metadata into a highly reorderable matrix to expose corpus level trends; extend encodings of tagged text to illustrate probabilistic information at a passage level; and introduce a technique for visualizing individual word rankings, along with interaction techniques and new statistical methods to create links between different levels and information types. We describe example uses from both the humanities and visualization research that illustrate the benefits of our approach. | false | false | [
"Eric C. Alexander",
"Joe Kohlmann",
"Robin Valenza",
"Michael Witmore",
"Michael Gleicher"
] | [] | [] | [] |
VAST | 2,014 | Supporting Communication and Coordination in Collaborative Sensemaking | 10.1109/TVCG.2014.2346573 | When people work together to analyze a data set, they need to organize their findings, hypotheses, and evidence, share that information with their collaborators, and coordinate activities amongst team members. Sharing externalizations (recorded information such as notes) could increase awareness and assist with team communication and coordination. However, we currently know little about how to provide tool support for this sort of sharing. We explore how linked common work (LCW) can be employed within a `collaborative thinking space', to facilitate synchronous collaborative sensemaking activities in Visual Analytics (VA). Collaborative thinking spaces provide an environment for analysts to record, organize, share and connect externalizations. Our tool, CLIP, extends earlier thinking spaces by integrating LCW features that reveal relationships between collaborators' findings. We conducted a user study comparing CLIP to a baseline version without LCW. Results demonstrated that LCW significantly improved analytic outcomes at a collaborative intelligence task. Groups using CLIP were also able to more effectively coordinate their work, and held more discussion of their findings and hypotheses. LCW enabled them to maintain awareness of each other's activities and findings and link those findings to their own work, preventing disruptive oral awareness notifications. | false | false | [
"Narges Mahyar",
"Melanie Tory"
] | [
"BP"
] | [] | [] |
VAST | 2,014 | The Spinel Explorer - Interactive Visual Analysis of Spinel Group Minerals | 10.1109/TVCG.2014.2346754 | Geologists usually deal with rocks that are up to several thousand million years old. They try to reconstruct the tectonic settings where these rocks were formed and the history of events that affected them through the geological time. The spinel group minerals provide useful information regarding the geological environment in which the host rocks were formed. They constitute excellent indicators of geological environments (tectonic settings) and are of invaluable help in the search for mineral deposits of economic interest. The current workflow requires the scientists to work with different applications to analyze spine data. They do use specific diagrams, but these are usually not interactive. The current workflow hinders domain experts to fully exploit the potentials of tediously and expensively collected data. In this paper, we introduce the Spinel Explorer-an interactive visual analysis application for spinel group minerals. The design of the Spinel Explorer and of the newly introduced interactions is a result of a careful study of geologists' tasks. The Spinel Explorer includes most of the diagrams commonly used for analyzing spinel group minerals, including 2D binary plots, ternary plots, and 3D Spinel prism plots. Besides specific plots, conventional information visualization views are also integrated in the Spinel Explorer. All views are interactive and linked. The Spinel Explorer supports conventional statistics commonly used in spinel minerals exploration. The statistics views and different data derivation techniques are fully integrated in the system. Besides the Spinel Explorer as newly proposed interactive exploration system, we also describe the identified analysis tasks, and propose a new workflow. We evaluate the Spinel Explorer using real-life data from two locations in Argentina: the Frontal Cordillera in Central Andes and Patagonia. We describe the new findings of the geologists which would have been much more difficult to achieve using the current workflow only. Very positive feedback from geologists confirms the usefulness of the Spinel Explorer. | false | false | [
"Maria Luján Ganuza",
"Gabriela Ferracutti",
"Maria Florencia Gargiulo",
"Silvia Mabel Castro",
"Ernesto A. Bjerg",
"M. Eduard Gröller",
"Kresimir Matkovic"
] | [] | [] | [] |
VAST | 2,014 | TopicPanorama: A Full Picture of Relevant Topics | 10.1109/VAST.2014.7042494 | We present a visual analytics approach to developing a full picture of relevant topics discussed in multiple sources such as news, blogs, or micro-blogs. The full picture consists of a number of common topics among multiple sources as well as distinctive topics. The key idea behind our approach is to jointly match the topics extracted from each source together in order to interactively and effectively analyze common and distinctive topics. We start by modeling each textual corpus as a topic graph. These graphs are then matched together with a consistent graph matching method. Next, we develop an LOD-based visualization for better understanding and analysis of the matched graph. The major feature of this visualization is that it combines a radially stacked tree visualization with a density-based graph visualization to facilitate the examination of the matched topic graph from multiple perspectives. To compensate for the deficiency of the graph matching algorithm and meet different users' needs, we allow users to interactively modify the graph matching result. We have applied our approach to various data including news, tweets, and blog data. Qualitative evaluation and a real-world case study with domain experts demonstrate the promise of our approach, especially in support of analyzing a topic-graph-based full picture at different levels of detail. | false | false | [
"Shixia Liu",
"Xiting Wang",
"Jianfei Chen 0001",
"Jim Zhu",
"Baining Guo"
] | [] | [] | [] |
VAST | 2,014 | Towards Interactive, Intelligent, and Integrated Multimedia Analytics | 10.1109/VAST.2014.7042476 | The size and importance of visual multimedia collections grew rapidly over the last years, creating a need for sophisticated multimedia analytics systems enabling large-scale, interactive, and insightful analysis. These systems need to integrate the human's natural expertise in analyzing multimedia with the machine's ability to process large-scale data. The paper starts off with a comprehensive overview of representation, learning, and interaction techniques from both the human's and the machine's point of view. To this end, hundreds of references from the related disciplines (visual analytics, information visualization, computer vision, multimedia information retrieval) have been surveyed. Based on the survey, a novel general multimedia analytics model is synthesized. In the model, the need for semantic navigation of the collection is emphasized and multimedia analytics tasks are placed on the exploration-search axis. The axis is composed of both exploration and search in a certain proportion which changes as the analyst progresses towards insight. Categorization is proposed as a suitable umbrella task realizing the exploration-search axis in the model. Finally, the pragmatic gap, defined as the difference between the tight machine categorization model and the flexible human categorization model is identified as a crucial multimedia analytics topic. | false | false | [
"Jan Zahálka",
"Marcel Worring"
] | [] | [] | [] |
VAST | 2,014 | Transforming Scagnostics to Reveal Hidden Features | 10.1109/TVCG.2014.2346572 | Scagnostics (Scatterplot Diagnostics) were developed by Wilkinson et al. based on an idea of Paul and John Tukey, in order to discern meaningful patterns in large collections of scatterplots. The Tukeys' original idea was intended to overcome the impediments involved in examining large scatterplot matrices (multiplicity of plots and lack of detail). Wilkinson's implementation enabled for the first time scagnostics computations on many points as well as many plots. Unfortunately, scagnostics are sensitive to scale transformations. We illustrate the extent of this sensitivity and show how it is possible to pair statistical transformations with scagnostics to enable discovery of hidden structures in data that are not discernible in untransformed visualizations. | false | false | [
"Tommy Dang",
"Leland Wilkinson"
] | [] | [] | [] |
VAST | 2,014 | Using Visualizations to Monitor Changes and Harvest Insights from a Global-Scale Logging Infrastructure at Twitter | 10.1109/VAST.2014.7042487 | Logging user activities is essential to data analysis for internet products and services. Twitter has built a unified logging infrastructure that captures user activities across all clients it owns, making it one of the largest datasets in the organization. This paper describes challenges and opportunities in applying information visualization to log analysis at this massive scale, and shows how various visualization techniques can be adapted to help data scientists extract insights. In particular, we focus on two scenarios: (1) monitoring and exploring a large collection of log events, and (2) performing visual funnel analysis on log data with tens of thousands of event types. Two interactive visualizations were developed for these purposes: we discuss design choices and the implementation of these systems, along with case studies of how they are being used in day-to-day operations at Twitter. | false | false | [
"Krist Wongsuphasawat",
"Jimmy Lin"
] | [] | [] | [] |
VAST | 2,014 | VAET: A Visual Analytics Approach for E-Transactions Time-Series | 10.1109/TVCG.2014.2346913 | Previous studies on E-transaction time-series have mainly focused on finding temporal trends of transaction behavior. Interesting transactions that are time-stamped and situation-relevant may easily be obscured in a large amount of information. This paper proposes a visual analytics system, Visual Analysis of E-transaction Time-Series (VAET), that allows the analysts to interactively explore large transaction datasets for insights about time-varying transactions. With a set of analyst-determined training samples, VAET automatically estimates the saliency of each transaction in a large time-series using a probabilistic decision tree learner. It provides an effective time-of-saliency (TOS) map where the analysts can explore a large number of transactions at different time granularities. Interesting transactions are further encoded with KnotLines, a compact visual representation that captures both the temporal variations and the contextual connection of transactions. The analysts can thus explore, select, and investigate knotlines of interest. A case study and user study with a real E-transactions dataset (26 million records) demonstrate the effectiveness of VAET. | false | false | [
"Cong Xie",
"Wei Chen 0001",
"Xinxin Huang",
"Yueqi Hu",
"Scott Barlowe",
"Jing Yang 0001"
] | [] | [] | [] |
VAST | 2,014 | VarifocalReader -- In-Depth Visual Analysis of Large Text Documents | 10.1109/TVCG.2014.2346677 | Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies. | false | false | [
"Steffen Koch 0001",
"Markus John",
"Michael Wörner 0001",
"Andreas Müller 0012",
"Thomas Ertl"
] | [] | [] | [] |
VAST | 2,014 | VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure | 10.1109/TVCG.2014.2346911 | We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain. | false | false | [
"Sungahn Ko",
"Jieqiong Zhao",
"Jing Xia",
"Shehzad Afzal",
"Derek Xiaoyu Wang",
"Greg Abram",
"Niklas Elmqvist",
"Len Kne",
"David Van Riper",
"Kelly P. Gaither",
"Shaun Kennedy",
"William J. Tolone",
"William Ribarsky",
"David S. Ebert"
] | [] | [] | [] |
VAST | 2,014 | Vismate: Interactive Visual Analysis of Station-Based Observation Data on Climate Changes | 10.1109/VAST.2014.7042489 | We present a new approach to visualizing the climate data of multi-dimensional, time-series, and geo-related characteristics. Our approach integrates three new highly interrelated visualization techniques, and uses the same input data types as in the traditional model-based analysis methods. As the main visualization view, Global Radial Map is used to identify the overall state of climate changes and provide users with a compact and intuitive view for analyzing spatial and temporal patterns at the same time. Other two visualization techniques, providing complementary views, are specialized in analysing time trend and detecting abnormal cases, which are two important analysis tasks in any climate change study. Case studies and expert reviews have been conducted, through which the effectiveness and scalability of the proposed approach has been confirmed. | false | false | [
"Jie Li 0006",
"Kang Zhang 0001",
"Zhaopeng Meng"
] | [] | [] | [] |
VAST | 2,014 | Visual Abstraction and Exploration of Multi-class Scatterplots | 10.1109/TVCG.2014.2346594 | Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach. | false | false | [
"Haidong Chen",
"Wei Chen 0001",
"Honghui Mei",
"Zhiqi Liu",
"Kun Zhou",
"Weifeng Chen 0002",
"Wentao Gu",
"Kwan-Liu Ma"
] | [] | [] | [] |
VAST | 2,014 | Visual Analysis of Patterns in Multiple Amino Acid Mutation Graphs | 10.1109/VAST.2014.7042485 | Proteins are essential parts in all living organisms. They consist of sequences of amino acids. An interaction with reactive agent can stimulate a mutation at a specific position in the sequence. This mutation may set off a chain reaction, which effects other amino acids in the protein. Chain reactions need to be analyzed, as they may invoke unwanted side effects in drug treatment. A mutation chain is represented by a directed acyclic graph, where amino acids are connected by their mutation dependencies. As each amino acid may mutate individually, many mutation graphs exist. To determine important impacts of mutations, experts need to analyze and compare common patterns in these mutations graphs. Experts, however, lack suitable tools for this purpose. We present a new system for the search and the exploration of frequent patterns (i.e., motifs) in mutation graphs. We present a fast pattern search algorithm specifically developed for finding biologically relevant patterns in many mutation graphs (i.e., many labeled acyclic directed graphs). Our visualization system allows an interactive exploration and comparison of the found patterns. It enables locating the found patterns in the mutation graphs and in the 3D protein structures. In this way, potentially interesting patterns can be discovered. These patterns serve as starting point for a further biological analysis. In cooperation with biologists, we use our approach for analyzing a real world data set based on multiple HIV protease sequences. | false | false | [
"Olav Lenz",
"Frank Keul",
"Sebastian Bremm",
"Kay Hamacher",
"Tatiana von Landesberger"
] | [] | [] | [] |
VAST | 2,014 | Visual Analysis of Public Utility Service Problems in a Metropolis | 10.1109/TVCG.2014.2346898 | Issues about city utility services reported by citizens can provide unprecedented insights into the various aspects of such services. Analysis of these issues can improve living quality through evidence-based decision making. However, these issues are complex, because of the involvement of spatial and temporal components, in addition to having multi-dimensional and multivariate natures. Consequently, exploring utility service problems and creating visual representations are difficult. To analyze these issues, we propose a visual analytics process based on the main tasks of utility service management. We also propose an aggregate method that transforms numerous issues into legible events and provide visualizations for events. In addition, we provide a set of tools and interaction techniques to explore such issues. Our approach enables administrators to make more informed decisions. | false | false | [
"Jiawan Zhang",
"E. Yanli",
"Jing Ma",
"Yahui Zhao",
"Binghan Xu",
"Liting Sun",
"Jinyan Chen",
"Xiaoru Yuan"
] | [] | [] | [] |
VAST | 2,014 | Visual Analytics for Comparison of Ocean Model Output with Reference Data: Detecting and Analyzing Geophysical Processes Using Clustering Ensembles | 10.1109/TVCG.2014.2346751 | Researchers assess the quality of an ocean model by comparing its output to that of a previous model version or to observations. One objective of the comparison is to detect and to analyze differences and similarities between both data sets regarding geophysical processes, such as particular ocean currents. This task involves the analysis of thousands or hundreds of thousands of geographically referenced temporal profiles in the data. To cope with the amount of data, modelers combine aggregation of temporal profiles to single statistical values with visual comparison. Although this strategy is based on experience and a well-grounded body of expert knowledge, our discussions with domain experts have shown that it has two limitations: (1) using a single statistical measure results in a rather limited scope of the comparison and in significant loss of information, and (2) the decisions modelers have to make in the process may lead to important aspects being overlooked. | false | false | [
"Patrick Köthur",
"Mike Sips",
"Henryk Dobslaw",
"Doris Dransch"
] | [] | [] | [] |
VAST | 2,014 | Visual Analytics for Complex Engineering Systems: Hybrid Visual Steering of Simulation Ensembles | 10.1109/TVCG.2014.2346744 | In this paper we propose a novel approach to hybrid visual steering of simulation ensembles. A simulation ensemble is a collection of simulation runs of the same simulation model using different sets of control parameters. Complex engineering systems have very large parameter spaces so a naïve sampling can result in prohibitively large simulation ensembles. Interactive steering of simulation ensembles provides the means to select relevant points in a multi-dimensional parameter space (design of experiment). Interactive steering efficiently reduces the number of simulation runs needed by coupling simulation and visualization and allowing a user to request new simulations on the fly. As system complexity grows, a pure interactive solution is not always sufficient. The new approach of hybrid steering combines interactive visual steering with automatic optimization. Hybrid steering allows a domain expert to interactively (in a visualization) select data points in an iterative manner, approximate the values in a continuous region of the simulation space (by regression) and automatically find the “best” points in this continuous region based on the specified constraints and objectives (by optimization). We argue that with the full spectrum of optimization options, the steering process can be improved substantially. We describe an integrated system consisting of a simulation, a visualization, and an optimization component. We also describe typical tasks and propose an interactive analysis workflow for complex engineering systems. We demonstrate our approach on a case study from automotive industry, the optimization of a hydraulic circuit in a high pressure common rail Diesel injection system. | false | false | [
"Kresimir Matkovic",
"Denis Gracanin",
"Rainer Splechtna",
"Mario Jelovic",
"Benedikt Stehno",
"Helwig Hauser",
"Werner Purgathofer"
] | [] | [] | [] |
VAST | 2,014 | Visual Exploration of Sparse Traffic Trajectory Data | 10.1109/TVCG.2014.2346746 | In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macro-traffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system. | false | false | [
"Zuchao Wang",
"Tangzhi Ye",
"Min Lu 0002",
"Xiaoru Yuan",
"Huamin Qu",
"Jacky Yuan",
"Qianliang Wu"
] | [] | [] | [] |
VAST | 2,014 | Visual Methods for Analyzing Probabilistic Classification Data | 10.1109/TVCG.2014.2346660 | Multi-class classifiers often compute scores for the classification samples describing probabilities to belong to different classes. In order to improve the performance of such classifiers, machine learning experts need to analyze classification results for a large number of labeled samples to find possible reasons for incorrect classification. Confusion matrices are widely used for this purpose. However, they provide no information about classification scores and features computed for the samples. We propose a set of integrated visual methods for analyzing the performance of probabilistic classifiers. Our methods provide insight into different aspects of the classification results for a large number of samples. One visualization emphasizes at which probabilities these samples were classified and how these probabilities correlate with classification error in terms of false positives and false negatives. Another view emphasizes the features of these samples and ranks them by their separation power between selected true and false classifications. We demonstrate the insight gained using our technique in a benchmarking classification dataset, and show how it enables improving classification performance by interactively defining and evaluating post-classification rules. | false | false | [
"Bilal Alsallakh",
"Allan Hanbury",
"Helwig Hauser",
"Silvia Miksch",
"Andreas Rauber"
] | [] | [] | [] |
VAST | 2,014 | Visual Reconciliation of Alternative Similarity Spaces in Climate Modeling | 10.1109/TVCG.2014.2346755 | Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses additional challenges. In this paper we define visual reconciliation as the problem of reconciling multiple alternative similarity spaces through visualization and interaction. We derive this problem from our work on model comparison in climate science where climate modelers are faced with the challenge of making sense of alternative ways to describe their models: one through the output they generate, another through the large set of properties that describe them. Ideally, they want to understand whether groups of models with similar spatio-temporal behaviors share similar sets of criteria or, conversely, whether similar criteria lead to similar behaviors. We propose a visual analytics solution based on linked views, that addresses this problem by allowing the user to dynamically create, modify and observe the interaction among groupings, thereby making the potential explanations apparent. We present case studies that demonstrate the usefulness of our technique in the area of climate science. | false | false | [
"Jorge Poco",
"Aritra Dasgupta",
"Yaxing Wei",
"William W. Hargrove",
"Christopher R. Schwalm",
"Deborah N. Huntzinger",
"Robert B. Cook",
"Enrico Bertini",
"Cláudio T. Silva"
] | [] | [] | [] |
VAST | 2,014 | Visualizing Mobility of Public Transportation System | 10.1109/TVCG.2014.2346893 | Public transportation systems (PTSs) play an important role in modern cities, providing shared/massive transportation services that are essential for the general public. However, due to their increasing complexity, designing effective methods to visualize and explore PTS is highly challenging. Most existing techniques employ network visualization methods and focus on showing the network topology across stops while ignoring various mobility-related factors such as riding time, transfer time, waiting time, and round-the-clock patterns. This work aims to visualize and explore passenger mobility in a PTS with a family of analytical tasks based on inputs from transportation researchers. After exploring different design alternatives, we come up with an integrated solution with three visualization modules: isochrone map view for geographical information, isotime flow map view for effective temporal information comparison and manipulation, and OD-pair journey view for detailed visual analysis of mobility factors along routes between specific origin-destination pairs. The isotime flow map linearizes a flow map into a parallel isoline representation, maximizing the visualization of mobility information along the horizontal time axis while presenting clear and smooth pathways from origin to destinations. Moreover, we devise several interactive visual query methods for users to easily explore the dynamics of PTS mobility over space and time. Lastly, we also construct a PTS mobility model from millions of real passenger trajectories, and evaluate our visualization techniques with assorted case studies with the transportation researchers. | false | false | [
"Wei Zeng 0004",
"Chi-Wing Fu",
"Stefan Müller Arisona",
"Alexander Erath",
"Huamin Qu"
] | [] | [] | [] |
VAST | 2,014 | Weaving a Carpet from Log Entries: A Network Security Visualization Built with Co-Creation | 10.1109/VAST.2014.7042483 | We created a pixel map for multivariate data based on an analysis of the needs of network security engineers. Parameters of a log record are shown as pixels and these pixels are stacked to represent a record. This allows a broad view of a data set on one screen while staying very close to the raw data and to expose common and rare patterns of user behavior through the visualization itself (the "Carpet"). Visualizations that immediately point to areas of suspicious activity without requiring extensive filtering, help network engineers investigating unknown computer security incidents. Most of them, however, have limited knowledge of advanced visualization techniques, while many designers and data scientists are unfamiliar with computer security topics. To bridge this gap, we developed visualizations together with engineers, following a co-creative process. We will show how we explored the scope of the engineers' tasks and how we jointly developed ideas and designs. Our expert evaluation indicates that this visualization helps to scan large parts of log files quickly and to define areas of interest for closer inspection. | false | false | [
"Johannes Landstorfer",
"Ivo Herrmann",
"Jan-Erik Stange",
"Marian Dörk",
"Reto Wettach"
] | [] | [] | [] |
VAST | 2,014 | YMCA - Your Mesh Comparison Application | 10.1109/VAST.2014.7042491 | Polygonal meshes can be created in several different ways. In this paper we focus on the reconstruction of meshes from point clouds, which are sets of points in 3D. Several algorithms that tackle this task already exist, but they have different benefits and drawbacks, which leads to a large number of possible reconstruction results (i.e., meshes). The evaluation of those techniques requires extensive comparisons between different meshes which is up to now done by either placing images of rendered meshes side-by-side, or by encoding differences by heat maps. A major drawback of both approaches is that they do not scale well with the number of meshes. This paper introduces a new comparative visual analysis technique for 3D meshes which enables the simultaneous comparison of several meshes and allows for the interactive exploration of their differences. Our approach gives an overview of the differences of the input meshes in a 2D view. By selecting certain areas of interest, the user can switch to a 3D representation and explore the spatial differences in detail. To inspect local variations, we provide a magic lens tool in 3D. The location and size of the lens provide further information on the variations of the reconstructions in the selected area. With our comparative visualization approach, differences between several mesh reconstruction algorithms can be easily localized and inspected. | false | false | [
"Johanna Schmidt",
"Reinhold Preiner",
"Thomas Auzinger",
"Michael Wimmer 0001",
"M. Eduard Gröller",
"Stefan Bruckner"
] | [] | [] | [] |
SciVis | 2,014 | A Robust Parity Test for Extracting Parallel Vectors in 3D | 10.1109/TVCG.2014.2346412 | Parallel vectors (PV), the loci where two vector fields are parallel, are commonly used to represent curvilinear features in 3D for data visualization. Methods for extracting PV usually operate on a 3D grid and start with detecting seed points on a cell face. We propose, to the best of our knowledge, the first provably correct test that determines the parity of the number of PV points on a cell face. The test only needs to sample along the face boundary and works for any choice of the two vector fields. A discretization of the test is described, validated, and compared with existing tests that are also based on boundary sampling. The test can guide PV-extraction algorithms to ensure closed curves wherever the input fields are continuous, which we exemplify in extracting ridges and valleys of scalar functions. | false | false | [
"Tao Ju",
"Minxin Cheng",
"Xu Wang",
"Ye Duan"
] | [
"HM"
] | [] | [] |
SciVis | 2,014 | ADR - Anatomy-Driven Reformation | 10.1109/TVCG.2014.2346405 | Dedicated visualization methods are among the most important tools of modern computer-aided medical applications. Reformation methods such as Multiplanar Reformation or Curved Planar Reformation have evolved as useful tools that facilitate diagnostic and therapeutic work. In this paper, we present a novel approach that can be seen as a generalization of Multiplanar Reformation to curved surfaces. The main concept is to generate reformatted medical volumes driven by the individual anatomical geometry of a specific patient. This process generates flat views of anatomical structures that facilitate many tasks such as diagnosis, navigation and annotation. Our reformation framework is based on a non-linear as-rigid-as-possible volumetric deformation scheme that uses generic triangular surface meshes as input. To manage inevitable distortions during reformation, we introduce importance maps which allow controlling the error distribution and improving the overall visual quality in areas of elevated interest. Our method seamlessly integrates with well-established concepts such as the slice-based inspection of medical datasets and we believe it can improve the overall efficiency of many medical workflows. To demonstrate this, we additionally present an integrated visualization system and discuss several use cases that substantiate its benefits. | false | false | [
"Jan Kretschmer",
"Grzegorz Soza",
"Christian Tietjen",
"Michael Sühling",
"Bernhard Preim",
"Marc Stamminger"
] | [] | [] | [] |
SciVis | 2,014 | Advection-Based Sparse Data Management for Visualizing Unsteady Flow | 10.1109/TVCG.2014.2346418 | When computing integral curves and integral surfaces for large-scale unsteady flow fields, a major bottleneck is the widening gap between data access demands and the available bandwidth (both I/O and in-memory). In this work, we explore a novel advection-based scheme to manage flow field data for both efficiency and scalability. The key is to first partition flow field into blocklets (e.g. cells or very fine-grained blocks of cells), and then (pre)fetch and manage blocklets on-demand using a parallel key-value store. The benefits are (1) greatly increasing the scale of local-range analysis (e.g. source-destination queries, streak surface generation) that can fit within any given limit of hardware resources; (2) improving memory and I/O bandwidth-efficiencies as well as the scalability of naive task-parallel particle advection. We demonstrate our method using a prototype system that works on workstation and also in supercomputing environments. Results show significantly reduced I/O overhead compared to accessing raw flow data, and also high scalability on a supercomputer for a variety of applications. | false | false | [
"Hanqi Guo 0001",
"Jiang Zhang 0002",
"Richen Liu",
"Lu Liu 0017",
"Xiaoru Yuan",
"Jian Huang 0007",
"Xiangfei Meng",
"Jingshan Pan"
] | [] | [] | [] |
SciVis | 2,014 | Attractive Flicker: Guiding Attention in Dynamic Narrative Visualizations | 10.1109/TVCG.2014.2346352 | Focus-context techniques provide visual guidance in visualizations by giving strong visual prominence to elements of interest while the context is suppressed. However, finding a visual feature to enhance for the focus to pop out from its context in a large dynamic scene, while leading to minimal visual deformation and subjective disturbance, is challenging. This paper proposes Attractive Flicker, a novel technique for visual guidance in dynamic narrative visualizations. We first show that flicker is a strong visual attractor in the entire visual field, without distorting, suppressing, or adding any scene elements. The novel aspect of our Attractive Flicker technique is that it consists of two signal stages: The first “orientation stage” is a short but intensive flicker stimulus to attract the attention to elements of interest. Subsequently, the intensive flicker is reduced to a minimally disturbing luminance oscillation (“engagement stage”) as visual support to keep track of the focus elements. To find a good trade-off between attraction effectiveness and subjective annoyance caused by flicker, we conducted two perceptual studies to find suitable signal parameters. We showcase Attractive Flicker with the parameters obtained from the perceptual statistics in a study of molecular interactions. With Attractive Flicker, users were able to easily follow the narrative of the visualization on a large display, while the flickering of focus elements was not disturbing when observing the context. | false | false | [
"Manuela Waldner",
"Mathieu Le Muzic",
"Matthias Bernhard",
"Werner Purgathofer",
"Ivan Viola"
] | [] | [] | [] |
SciVis | 2,014 | Boundary Aware Reconstruction of Scalar Fields | 10.1109/TVCG.2014.2346351 | In visualization, the combined role of data reconstruction and its classification plays a crucial role. In this paper we propose a novel approach that improves classification of different materials and their boundaries by combining information from the classifiers at the reconstruction stage. Our approach estimates the targeted materials' local support before performing multiple material-specific reconstructions that prevent much of the misclassification traditionally associated with transitional regions and transfer function (TF) design. With respect to previously published methods our approach offers a number of improvements and advantages. For one, it does not rely on TFs acting on derivative expressions, therefore it is less sensitive to noisy data and the classification of a single material does not depend on specialized TF widgets or specifying regions in a multidimensional TF. Additionally, improved classification is attained without increasing TF dimensionality, which promotes scalability to multivariate data. These aspects are also key in maintaining low interaction complexity. The results are simple-to-achieve visualizations that better comply with the user's understanding of discrete features within the studied object. | false | false | [
"Stefan Lindholm",
"Daniel Jönsson",
"Charles D. Hansen",
"Anders Ynnerman"
] | [] | [] | [] |
SciVis | 2,014 | Characterizing Molecular Interactions in Chemical Systems | 10.1109/TVCG.2014.2346403 | Interactions between atoms have a major influence on the chemical properties of molecular systems. While covalent interactions impose the structural integrity of molecules, noncovalent interactions govern more subtle phenomena such as protein folding, bonding or self assembly. The understanding of these types of interactions is necessary for the interpretation of many biological processes and chemical design tasks. While traditionally the electron density is analyzed to interpret the quantum chemistry of a molecular system, noncovalent interactions are characterized by low electron densities and only slight variations of them - challenging their extraction and characterization. Recently, the signed electron density and the reduced gradient, two scalar fields derived from the electron density, have drawn much attention in quantum chemistry since they enable a qualitative visualization of these interactions even in complex molecular systems and experimental measurements. In this work, we present the first combinatorial algorithm for the automated extraction and characterization of covalent and noncovalent interactions in molecular systems. The proposed algorithm is based on a joint topological analysis of the signed electron density and the reduced gradient. Combining the connectivity information of the critical points of these two scalar fields enables to visualize, enumerate, classify and investigate molecular interactions in a robust manner. Experiments on a variety of molecular systems, from simple dimers to proteins or DNA, demonstrate the ability of our technique to robustly extract these interactions and to reveal their structural relations to the atoms and bonds forming the molecules. For simple systems, our analysis corroborates the observations made by the chemists while it provides new visual and quantitative insights on chemical interactions for larger molecular systems. | false | false | [
"David Günther",
"Roberto Álvarez Boto",
"Juila Contreras-Garcia",
"Jean-Philip Piquemal",
"Julien Tierny"
] | [] | [] | [] |
SciVis | 2,014 | City Forensics: Using Visual Elements to Predict Non-Visual City Attributes | 10.1109/TVCG.2014.2346446 | We present a method for automatically identifying and validating predictive relationships between the visual appearance of a city and its non-visual attributes (e.g. crime statistics, housing prices, population density etc.). Given a set of street-level images and (location, city-attribute-value) pairs of measurements, we first identify visual elements in the images that are discriminative of the attribute. We then train a predictor by learning a set of weights over these elements using non-linear Support Vector Regression. To perform these operations efficiently, we implement a scalable distributed processing framework that speeds up the main computational bottleneck (extracting visual elements) by an order of magnitude. This speedup allows us to investigate a variety of city attributes across 6 different American cities. We find that indeed there is a predictive relationship between visual elements and a number of city attributes including violent crime rates, theft rates, housing prices, population density, tree presence, graffiti presence, and the perception of danger. We also test human performance for predicting theft based on street-level images and show that our predictor outperforms this baseline with 33% higher accuracy on average. Finally, we present three prototype applications that use our system to (1) define the visual boundary of city neighborhoods, (2) generate walking directions that avoid or seek out exposure to city attributes, and (3) validate user-specified visual elements for prediction. | false | false | [
"Sean M. Arietta",
"Alexei A. Efros",
"Ravi Ramamoorthi",
"Maneesh Agrawala"
] | [
"HM"
] | [] | [] |
SciVis | 2,014 | Combined Visualization of Wall Thickness and Wall Shear Stress for the Evaluation of Aneurysms | 10.1109/TVCG.2014.2346406 | For an individual rupture risk assessment of aneurysms, the aneurysm's wall morphology and hemodynamics provide valuable information. Hemodynamic information is usually extracted via computational fluid dynamic (CFD) simulation on a previously extracted 3D aneurysm surface mesh or directly measured with 4D phase-contrast magnetic resonance imaging. In contrast, a noninvasive imaging technique that depicts the aneurysm wall in vivo is still not available. Our approach comprises an experiment, where intravascular ultrasound (IVUS) is employed to probe a dissected saccular aneurysm phantom, which we modeled from a porcine kidney artery. Then, we extracted a 3D surface mesh to gain the vessel wall thickness and hemodynamic information from a CFD simulation. Building on this, we developed a framework that depicts the inner and outer aneurysm wall with dedicated information about local thickness via distance ribbons. For both walls, a shading is adapted such that the inner wall as well as its distance to the outer wall is always perceivable. The exploration of the wall is further improved by combining it with hemodynamic information from the CFD simulation. Hence, the visual analysis comprises a brushing and linking concept for individual highlighting of pathologic areas. Also, a surface clustering is integrated to provide an automatic division of different aneurysm parts combined with a risk score depending on wall thickness and hemodynamic information. In general, our approach can be employed for vessel visualization purposes where an inner and outer wall has to be adequately represented. | false | false | [
"Sylvia Saalfeld",
"Kai Lawonn",
"Thomas Hoffmann 0002",
"Martin Skalej",
"Bernhard Preim"
] | [] | [] | [] |
SciVis | 2,014 | Conforming Morse-Smale Complexes | 10.1109/TVCG.2014.2346434 | Morse-Smale (MS) complexes have been gaining popularity as a tool for feature-driven data analysis and visualization. However, the quality of their geometric embedding and the sole dependence on the input scalar field data can limit their applicability when expressing application-dependent features. In this paper we introduce a new combinatorial technique to compute an MS complex that conforms to both an input scalar field and an additional, prior segmentation of the domain. The segmentation constrains the MS complex computation guaranteeing that boundaries in the segmentation are captured as separatrices of the MS complex. We demonstrate the utility and versatility of our approach with two applications. First, we use streamline integration to determine numerically computed basins/mountains and use the resulting segmentation as an input to our algorithm. This strategy enables the incorporation of prior flow path knowledge, effectively resulting in an MS complex that is as geometrically accurate as the employed numerical integration. Our second use case is motivated by the observation that often the data itself does not explicitly contain features known to be present by a domain expert. We introduce edit operations for MS complexes so that a user can directly modify their features while maintaining all the advantages of a robust topology-based representation. | false | false | [
"Attila Gyulassy",
"David Günther",
"Joshua A. Levine",
"Julien Tierny",
"Valerio Pascucci"
] | [] | [] | [] |
SciVis | 2,014 | Curve Boxplot: Generalization of Boxplot for Ensembles of Curves | 10.1109/TVCG.2014.2346455 | In simulation science, computational scientists often study the behavior of their simulations by repeated solutions with variations in parameters and/or boundary values or initial conditions. Through such simulation ensembles, one can try to understand or quantify the variability or uncertainty in a solution as a function of the various inputs or model assumptions. In response to a growing interest in simulation ensembles, the visualization community has developed a suite of methods for allowing users to observe and understand the properties of these ensembles in an efficient and effective manner. An important aspect of visualizing simulations is the analysis of derived features, often represented as points, surfaces, or curves. In this paper, we present a novel, nonparametric method for summarizing ensembles of 2D and 3D curves. We propose an extension of a method from descriptive statistics, data depth, to curves. We also demonstrate a set of rendering and visualization strategies for showing rank statistics of an ensemble of curves, which is a generalization of traditional whisker plots or boxplots to multidimensional curves. Results are presented for applications in neuroimaging, hurricane forecasting and fluid dynamics. | false | false | [
"Mahsa Mirzargar",
"Ross T. Whitaker",
"Robert M. Kirby"
] | [] | [] | [] |
SciVis | 2,014 | Decomposition and Simplification of Multivariate Data using Pareto Sets | 10.1109/TVCG.2014.2346447 | Topological and structural analysis of multivariate data is aimed at improving the understanding and usage of such data through identification of intrinsic features and structural relationships among multiple variables. We present two novel methods for simplifying so-called Pareto sets that describe such structural relationships. Such simplification is a precondition for meaningful visualization of structurally rich or noisy data. As a framework for simplification operations, we introduce a decomposition of the data domain into regions of equivalent structural behavior and the reachability graph that describes global connectivity of Pareto extrema. Simplification is then performed as a sequence of edge collapses in this graph; to determine a suitable sequence of such operations, we describe and utilize a comparison measure that reflects the changes to the data that each operation represents. We demonstrate and evaluate our methods on synthetic and real-world examples. | false | false | [
"Lars Huettenberger",
"Christian Heine 0002",
"Christoph Garth"
] | [] | [] | [] |
SciVis | 2,014 | Design and Evaluation of Interactive Proofreading Tools for Connectomics | 10.1109/TVCG.2014.2346371 | Proofreading refers to the manual correction of automatic segmentations of image data. In connectomics, electron microscopy data is acquired at nanometer-scale resolution and results in very large image volumes of brain tissue that require fully automatic segmentation algorithms to identify cell boundaries. However, these algorithms require hundreds of corrections per cubic micron of tissue. Even though this task is time consuming, it is fairly easy for humans to perform corrections through splitting, merging, and adjusting segments during proofreading. In this paper we present the design and implementation of Mojo, a fully-featured single-user desktop application for proofreading, and Dojo, a multi-user web-based application for collaborative proofreading. We evaluate the accuracy and speed of Mojo, Dojo, and Raveler, a proofreading tool from Janelia Farm, through a quantitative user study. We designed a between-subjects experiment and asked non-experts to proofread neurons in a publicly available connectomics dataset. Our results show a significant improvement of corrections using web-based Dojo, when given the same amount of time. In addition, all participants using Dojo reported better usability. We discuss our findings and provide an analysis of requirements for designing visual proofreading software. | false | false | [
"Daniel Haehn",
"Seymour Knowles-Barley",
"Mike Roberts 0001",
"Johanna Beyer",
"Narayanan Kasthuri",
"Jeff Lichtman",
"Hanspeter Pfister"
] | [] | [] | [] |
SciVis | 2,014 | Escape Maps | 10.1109/TVCG.2014.2346442 | We present a technique to visualize the streamline-based mapping between the boundary of a simply-connected subregion of arbitrary 3D vector fields. While the streamlines are seeded on one part of the boundary, the remaining part serves as escape border. Hence, the seeding part of the boundary represents a map of streamline behavior, indicating if streamlines reach the escape border or not. Since the resulting maps typically exhibit a very fine and complex structure and are thus not amenable to direct sampling, our approach instead aims at topologically consistent extraction of their boundary. We show that isocline surfaces of the projected vector field provide a robust basis for stream-surface-based extraction of these boundaries. The utility of our technique is demonstrated in the context of transport processes using vector field data from different domains. | false | false | [
"Gustavo Mello Machado",
"Filip Sadlo",
"Thomas Müller 0005",
"Thomas Ertl"
] | [] | [] | [] |
SciVis | 2,014 | Fast and Memory-Efficienty Topological Denoising of 2D and 3D Scalar Fields | 10.1109/TVCG.2014.2346432 | Data acquisition, numerical inaccuracies, and sampling often introduce noise in measurements and simulations. Removing this noise is often necessary for efficient analysis and visualization of this data, yet many denoising techniques change the minima and maxima of a scalar field. For example, the extrema can appear or disappear, spatially move, and change their value. This can lead to wrong interpretations of the data, e.g., when the maximum temperature over an area is falsely reported being a few degrees cooler because the denoising method is unaware of these features. Recently, a topological denoising technique based on a global energy optimization was proposed, which allows the topology-controlled denoising of 2D scalar fields. While this method preserves the minima and maxima, it is constrained by the size of the data. We extend this work to large 2D data and medium-sized 3D data by introducing a novel domain decomposition approach. It allows processing small patches of the domain independently while still avoiding the introduction of new critical points. Furthermore, we propose an iterative refinement of the solution, which decreases the optimization energy compared to the previous approach and therefore gives smoother results that are closer to the input. We illustrate our technique on synthetic and real-world 2D and 3D data sets that highlight potential applications. | false | false | [
"David Günther",
"Alec Jacobson",
"Jan Reininghaus",
"Hans-Peter Seidel",
"Olga Sorkine-Hornung",
"Tino Weinkauf"
] | [] | [] | [] |
SciVis | 2,014 | Fixed-Rate Compressed Floating-Point Arrays | 10.1109/TVCG.2014.2346458 | Current compression schemes for floating-point data commonly take fixed-precision values and compress them to a variable-length bit stream, complicating memory management and random access. We present a fixed-rate, near-lossless compression scheme that maps small blocks of 4<sup>d</sup> values in d dimensions to a fixed, user-specified number of bits per block, thereby allowing read and write random access to compressed floating-point data at block granularity. Our approach is inspired by fixed-rate texture compression methods widely adopted in graphics hardware, but has been tailored to the high dynamic range and precision demands of scientific applications. Our compressor is based on a new, lifted, orthogonal block transform and embedded coding, allowing each per-block bit stream to be truncated at any point if desired, thus facilitating bit rate selection using a single compression scheme. To avoid compression or decompression upon every data access, we employ a software write-back cache of uncompressed blocks. Our compressor has been designed with computational simplicity and speed in mind to allow for the possibility of a hardware implementation, and uses only a small number of fixed-point arithmetic operations per compressed value. We demonstrate the viability and benefits of lossy compression in several applications, including visualization, quantitative data analysis, and numerical simulation. | false | false | [
"Peter Lindstrom 0001"
] | [
"TT"
] | [] | [] |
SciVis | 2,014 | FLDA: Latent Dirichlet Allocation Based Unsteady Flow Analysis | 10.1109/TVCG.2014.2346416 | In this paper, we present a novel feature extraction approach called FLDA for unsteady flow fields based on Latent Dirichlet allocation (LDA) model. Analogous to topic modeling in text analysis, in our approach, pathlines and features in a given flow field are defined as documents and words respectively. Flow topics are then extracted based on Latent Dirichlet allocation. Different from other feature extraction methods, our approach clusters pathlines with probabilistic assignment, and aggregates features to meaningful topics at the same time. We build a prototype system to support exploration of unsteady flow field with our proposed LDA-based method. Interactive techniques are also developed to explore the extracted topics and to gain insight from the data. We conduct case studies to demonstrate the effectiveness of our proposed approach. | false | false | [
"Fan Hong",
"Chufan Lai",
"Hanqi Guo 0001",
"Enya Shen",
"Xiaoru Yuan",
"Sikun Li"
] | [] | [] | [] |
SciVis | 2,014 | Interactive Progressive Visualization with Space-Time Error Control | 10.1109/TVCG.2014.2346319 | We present a novel scheme for progressive rendering in interactive visualization. Static settings with respect to a certain image quality or frame rate are inherently incapable of delivering both high frame rates for rapid changes and high image quality for detailed investigation. Our novel technique flexibly adapts by steering the visualization process in three major degrees of freedom: when to terminate the refinement of a frame in the background and start a new one, when to display a frame currently computed, and how much resources to consume. We base these decisions on the correlation of the errors due to insufficient sampling and response delay, which we estimate separately using fast yet expressive heuristics. To automate the configuration of the steering behavior, we employ offline video quality analysis. We provide an efficient implementation of our scheme for the application of volume raycasting, featuring integrated GPU-accelerated image reconstruction and error estimation. Our implementation performs an integral handling of the changes due to camera transforms, transfer function adaptations, as well as the progression of the data to in time. Finally, the overall technique is evaluated with an expert study. | false | false | [
"Steffen Frey",
"Filip Sadlo",
"Kwan-Liu Ma",
"Thomas Ertl"
] | [] | [] | [] |
SciVis | 2,014 | Ligand Excluded Surface: A New Type of Molecular Surface | 10.1109/TVCG.2014.2346404 | The most popular molecular surface in molecular visualization is the solvent excluded surface (SES). It provides information about the accessibility of a biomolecule for a solvent molecule that is geometrically approximated by a sphere. During a period of almost four decades, the SES has served for many purposes - including visualization, analysis of molecular interactions and the study of cavities in molecular structures. However, if one is interested in the surface that is accessible to a molecule whose shape differs significantly from a sphere, a different concept is necessary. To address this problem, we generalize the definition of the SES by replacing the probe sphere with the full geometry of the ligand defined by the arrangement of its van der Waals spheres. We call the new surface ligand excluded surface (LES) and present an efficient, grid-based algorithm for its computation. Furthermore, we show that this algorithm can also be used to compute molecular cavities that could host the ligand molecule. We provide a detailed description of its implementation on CPU and GPU. Furthermore, we present a performance and convergence analysis and compare the LES for several molecules, using as ligands either water or small organic molecules. | false | false | [
"Norbert Lindow",
"Daniel Baum",
"Hans-Christian Hege"
] | [] | [] | [] |
SciVis | 2,014 | Low-Pass Filtered Volumetric Shadows | 10.1109/TVCG.2014.2346333 | We present a novel and efficient method to compute volumetric soft shadows for interactive direct volume visualization to improve the perception of spatial depth. By direct control of the softness of volumetric shadows, disturbing visual patterns due to hard shadows can be avoided and users can adapt the illumination to their personal and application-specific requirements. We compute the shadowing of a point in the data set by employing spatial filtering of the optical depth over a finite area patch pointing toward each light source. Conceptually, the area patch spans a volumetric region that is sampled with shadow rays; afterward, the resulting optical depth values are convolved with a low-pass filter on the patch. In the numerical computation, however, to avoid expensive shadow ray marching, we show how to align and set up summed area tables for both directional and point light sources. Once computed, the summed area tables enable efficient evaluation of soft shadows for each point in constant time without shadow ray marching and the softness of the shadows can be controlled interactively. We integrated our method in a GPU-based volume renderer with ray casting from the camera, which offers interactive control of the transfer function, light source positions, and viewpoint, for both static and time-dependent data sets. Our results demonstrate the benefit of soft shadows for visualization to achieve user-controlled illumination with many-point lighting setups for improved perception combined with high rendering speed. | false | false | [
"Marco Ament",
"Filip Sadlo",
"Carsten Dachsbacher",
"Daniel Weiskopf"
] | [] | [] | [] |
SciVis | 2,014 | Multi-Charts for Comparative 3D Ensemble Visualization | 10.1109/TVCG.2014.2346448 | A comparative visualization of multiple volume data sets is challenging due to the inherent occlusion effects, yet it is important to effectively reveal uncertainties, correlations and reliable trends in 3D ensemble fields. In this paper we present bidirectional linking of multi-charts and volume visualization as a means to analyze visually 3D scalar ensemble fields at the data level. Multi-charts are an extension of conventional bar and line charts: They linearize the 3D data points along a space-filling curve and draw them as multiple charts in the same plot area. The bar charts encode statistical information on ensemble members, such as histograms and probability densities, and line charts are overlayed to allow comparing members against the ensemble. Alternative linearizations based on histogram similarities or ensemble variation allow clustering of spatial locations depending on data distribution. Multi-charts organize the data at multiple scales to quickly provide overviews and enable users to select regions exhibiting interesting behavior interactively. They are further put into a spatial context by allowing the user to brush or query value intervals and specific distributions, and to simultaneously visualize the corresponding spatial points via volume rendering. By providing a picking mechanism in 3D and instantly highlighting the corresponding data points in the chart, the user can go back and forth between the abstract and the 3D view to focus the analysis. | false | false | [
"Ismail Demir",
"Christian Dick",
"Rüdiger Westermann"
] | [] | [] | [] |
SciVis | 2,014 | Multiscale Symmetry Detection in Scalar Fields by Clustering Contours | 10.1109/TVCG.2014.2346332 | The complexity in visualizing volumetric data often limits the scope of direct exploration of scalar fields. Isocontour extraction is a popular method for exploring scalar fields because of its simplicity in presenting features in the data. In this paper, we present a novel representation of contours with the aim of studying the similarity relationship between the contours. The representation maps contours to points in a high-dimensional transformation-invariant descriptor space. We leverage the power of this representation to design a clustering based algorithm for detecting symmetric regions in a scalar field. Symmetry detection is a challenging problem because it demands both segmentation of the data and identification of transformation invariant segments. While the former task can be addressed using topological analysis of scalar fields, the latter requires geometry based solutions. Our approach combines the two by utilizing the contour tree for segmenting the data and the descriptor space for determining transformation invariance. We discuss two applications, query driven exploration and asymmetry visualization, that demonstrate the effectiveness of the approach. | false | false | [
"Dilip Mathew Thomas",
"Vijay Natarajan"
] | [] | [] | [] |
SciVis | 2,014 | Predicate-Based Focus-and-Context Visualization for 3D Ultrasound | 10.1109/TVCG.2014.2346317 | Direct volume visualization techniques offer powerful insight into volumetric medical images and are part of the clinical routine for many applications. Up to now, however, their use is mostly limited to tomographic imaging modalities such as CT or MRI. With very few exceptions, such as fetal ultrasound, classic volume rendering using one-dimensional intensity-based transfer functions fails to yield satisfying results in case of ultrasound volumes. This is particularly due its gradient-like nature, a high amount of noise and speckle, and the fact that individual tissue types are rather characterized by a similar texture than by similar intensity values. Therefore, clinicians still prefer to look at 2D slices extracted from the ultrasound volume. In this work, we present an entirely novel approach to the classification and compositing stage of the volume rendering pipeline, specifically designed for use with ultrasonic images. We introduce point predicates as a generic formulation for integrating the evaluation of not only low-level information like local intensity or gradient, but also of high-level information, such as non-local image features or even anatomical models. Thus, we can successfully filter clinically relevant from non-relevant information. In order to effectively reduce the potentially high dimensionality of the predicate configuration space, we propose the predicate histogram as an intuitive user interface. This is augmented by a scribble technique to provide a comfortable metaphor for selecting predicates of interest. Assigning importance factors to the predicates allows for focus-and-context visualization that ensures to always show important (focus) regions of the data while maintaining as much context information as possible. Our method naturally integrates into standard ray casting algorithms and yields superior results in comparison to traditional methods in terms of visualizing a specific target anatomy in ultrasound volumes. | false | false | [
"Christian Schulte zu Berge",
"Maximilian Baust",
"Ankur Kapoor",
"Nassir Navab"
] | [] | [] | [] |
SciVis | 2,014 | Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering | 10.1109/TVCG.2014.2346324 | This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs. | false | false | [
"Ronell Sicat",
"Jens H. Krüger",
"Torsten Möller",
"Markus Hadwiger"
] | [] | [] | [] |
SciVis | 2,014 | Stent Maps - Comparative Visualization for the Prediction of Adverse Events of Transcatheter Aortic Valve Implantations | 10.1109/TVCG.2014.2346459 | Transcatheter aortic valve implantation (TAVI) is a minimally-invasive method for the treatment of aortic valve stenosis in patients with high surgical risk. Despite the success of TAVI, side effects such as paravalvular leakages can occur postoperatively. The goal of this project is to quantitatively analyze the co-occurrence of this complication and several potential risk factors such as stent shape after implantation, implantation height, amount and distribution of calcifications, and contact forces between stent and surrounding structure. In this paper, we present a two-dimensional visualization (stent maps), which allows (1) to comprehensively display all these aspects from CT data and mechanical simulation results and (2) to compare different datasets to identify patterns that are typical for adverse effects. The area of a stent map represents the surface area of the implanted stent - virtually straightened and uncoiled. Several properties of interest, like radial forces or stent compression, are displayed in this stent map in a heatmap-like fashion. Important anatomical landmarks and calcifications are plotted to show their spatial relation to the stent and possible correlations with the color-coded parameters. To provide comparability, the maps of different patient datasets are spatially adjusted according to a corresponding anatomical landmark. Also, stent maps summarizing the characteristics of different populations (e.g. with or without side effects) can be generated. Up to this point several interesting patterns have been observed with our technique, which remained hidden when examining the raw CT data or 3D visualizations of the same data. One example are obvious radial force maxima between the right and non-coronary valve leaflet occurring mainly in cases without leakages. These observations confirm the usefulness of our approach and give starting points for new hypotheses and further analyses. Because of its reduced dimensionality, the stent map data is an appropriate input for statistical group evaluation and machine learning methods. | false | false | [
"Silvia Born",
"Simon H. Sündermann",
"Christoph Russ",
"Raoul Hopf",
"Carlos E. Ruiz",
"Volkmar Falk",
"Michael Gessat"
] | [] | [] | [] |
SciVis | 2,014 | Trajectory-Based Flow Feature Tracking in Joint Particle/Volume Datasets | 10.1109/TVCG.2014.2346423 | Studying the dynamic evolution of time-varying volumetric data is essential in countless scientific endeavors. The ability to isolate and track features of interest allows domain scientists to better manage large complex datasets both in terms of visual understanding and computational efficiency. This work presents a new trajectory-based feature tracking technique for use in joint particle/volume datasets. While traditional feature tracking approaches generally require a high temporal resolution, this method utilizes the indexed trajectories of corresponding Lagrangian particle data to efficiently track features over large jumps in time. Such a technique is especially useful for situations where the volume dataset is either temporally sparse or too large to efficiently track a feature through all intermediate timesteps. In addition, this paper presents a few other applications of this approach, such as the ability to efficiently track the internal properties of volumetric features using variables from the particle data. We demonstrate the effectiveness of this technique using real world combustion and atmospheric datasets and compare it to existing tracking methods to justify its advantages and accuracy. | false | false | [
"Franz Sauer",
"Hongfeng Yu 0001",
"Kwan-Liu Ma"
] | [] | [] | [] |
SciVis | 2,014 | Trend-Centric Motion Visualization: Designing and Applying a New Strategy for Analyzing Scientific Motion Collections | 10.1109/TVCG.2014.2346451 | In biomechanics studies, researchers collect, via experiments or simulations, datasets with hundreds or thousands of trials, each describing the same type of motion (e.g., a neck flexion-extension exercise) but under different conditions (e.g., different patients, different disease states, pre- and post-treatment). Analyzing similarities and differences across all of the trials in these collections is a major challenge. Visualizing a single trial at a time does not work, and the typical alternative of juxtaposing multiple trials in a single visual display leads to complex, difficult-to-interpret visualizations. We address this problem via a new strategy that organizes the analysis around motion trends rather than trials. This new strategy matches the cognitive approach that scientists would like to take when analyzing motion collections. We introduce several technical innovations making trend-centric motion visualization possible. First, an algorithm detects a motion collection's trends via time-dependent clustering. Second, a 2D graphical technique visualizes how trials leave and join trends. Third, a 3D graphical technique, using a median 3D motion plus a visual variance indicator, visualizes the biomechanics of the set of trials within each trend. These innovations are combined to create an interactive exploratory visualization tool, which we designed through an iterative process in collaboration with both domain scientists and a traditionally-trained graphic designer. We report on insights generated during this design process and demonstrate the tool's effectiveness via a validation study with synthetic data and feedback from expert musculoskeletal biomechanics researchers who used the tool to analyze the effects of disc degeneration on human spinal kinematics. | false | false | [
"David Schroeder",
"Fedor Korsakov",
"Carissa Mai-Ping Knipe",
"Lauren Thorson",
"Arin M. Ellingson",
"David J. Nuckley",
"John V. Carlis",
"Daniel F. Keefe"
] | [] | [] | [] |
SciVis | 2,014 | Using Topological Analysis to Support Event-Guided Exploration in Urban Data | 10.1109/TVCG.2014.2346449 | The explosion in the volume of data about urban environments has opened up opportunities to inform both policy and administration and thereby help governments improve the lives of their citizens, increase the efficiency of public services, and reduce the environmental harms of development. However, cities are complex systems and exploring the data they generate is challenging. The interaction between the various components in a city creates complex dynamics where interesting facts occur at multiple scales, requiring users to inspect a large number of data slices over time and space. Manual exploration of these slices is ineffective, time consuming, and in many cases impractical. In this paper, we propose a technique that supports event-guided exploration of large, spatio-temporal urban data. We model the data as time-varying scalar functions and use computational topology to automatically identify events in different data slices. To handle a potentially large number of events, we develop an algorithm to group and index them, thus allowing users to interactively explore and query event patterns on the fly. A visual exploration interface helps guide users towards data slices that display interesting events and trends. We demonstrate the effectiveness of our technique on two different data sets from New York City (NYC): data about taxi trips and subway service. We also report on the feedback we received from analysts at different NYC agencies. | false | false | [
"Harish Doraiswamy",
"Nivan Ferreira",
"Theodoros Damoulas",
"Juliana Freire",
"Cláudio T. Silva"
] | [] | [] | [] |
SciVis | 2,014 | ViSlang: A System for Interpreted Domain-Specific Languages for Scientific Visualization | 10.1109/TVCG.2014.2346318 | Researchers from many domains use scientific visualization in their daily practice. Existing implementations of algorithms usually come with a graphical user interface (high-level interface), or as software library or source code (low-level interface). In this paper we present a system that integrates domain-specific languages (DSLs) and facilitates the creation of new DSLs. DSLs provide an effective interface for domain scientists avoiding the difficulties involved with low-level interfaces and at the same time offering more flexibility than high-level interfaces. We describe the design and implementation of ViSlang, an interpreted language specifically tailored for scientific visualization. A major contribution of our design is the extensibility of the ViSlang language. Novel DSLs that are tailored to the problems of the domain can be created and integrated into ViSlang. We show that our approach can be added to existing user interfaces to increase the flexibility for expert users on demand, but at the same time does not interfere with the user experience of novice users. To demonstrate the flexibility of our approach we present new DSLs for volume processing, querying and visualization. We report the implementation effort for new DSLs and compare our approach with Matlab and Python implementations in terms of run-time performance. | false | false | [
"Peter Rautek",
"Stefan Bruckner",
"M. Eduard Gröller",
"Markus Hadwiger"
] | [] | [] | [] |
SciVis | 2,014 | Visualization of Brain Microstructure Through Spherical Harmonics Illumination of High Fidelity Spatio-Angular Fields | 10.1109/TVCG.2014.2346411 | Diffusion kurtosis imaging (DKI) is gaining rapid adoption in the medical imaging community due to its ability to measure the non-Gaussian property of water diffusion in biological tissues. Compared to traditional diffusion tensor imaging (DTI), DKI can provide additional details about the underlying microstructural characteristics of the neural tissues. It has shown promising results in studies on changes in gray matter and mild traumatic brain injury where DTI is often found to be inadequate. The DKI dataset, which has high-fidelity spatio-angular fields, is difficult to visualize. Glyph-based visualization techniques are commonly used for visualization of DTI datasets; however, due to the rapid changes in orientation, lighting, and occlusion, visually analyzing the much more higher fidelity DKI data is a challenge. In this paper, we provide a systematic way to manage, analyze, and visualize high-fidelity spatio-angular fields from DKI datasets, by using spherical harmonics lighting functions to facilitate insights into the brain microstructure. | false | false | [
"Sujal Bista",
"Jiachen Zhuo",
"Rao P. Gullapalli",
"Amitabh Varshney"
] | [
"BP"
] | [] | [] |
SciVis | 2,014 | Visualization of Regular Maps: The Chase Continues | 10.1109/TVCG.2014.2352952 | A regular map is a symmetric tiling of a closed surface, in the sense that all faces, vertices, and edges are topologically indistinguishable. Platonic solids are prime examples, but also for surfaces with higher genus such regular maps exist. We present a new method to visualize regular maps. Space models are produced by matching regular maps with target shapes in the hyperbolic plane. The approach is an extension of our earlier work. Here a wider variety of target shapes is considered, obtained by duplicating spherical and toroidal regular maps, merging triangles, punching holes, and gluing the edges. The method produces about 45 new examples, including the genus 7 Hurwitz surface. | false | false | [
"Jarke J. van Wijk"
] | [] | [] | [] |
SciVis | 2,014 | Visualizing 2-dimensional Manifolds with Curve Handles in 4D | 10.1109/TVCG.2014.2346425 | In this paper, we present a mathematical visualization paradigm for exploring curves embedded in 3D and surfaces in 4D mathematical world. The basic problem is that, 3D figures of 4D mathematical entities often twist, turn, and fold back on themselves, leaving important properties behind the surface sheets. We propose an interactive system to visualize the topological features of the original 4D surface by slicing its 3D figure into a series of feature diagram. A novel 4D visualization interface is designed to allow users to control 4D topological shapes via the collection of diagram handles using the established curve manipulation mechanism. Our system can support rich mathematical interaction of 4D mathematical objects which is very difficult with any existing approach. We further demonstrate the effectiveness of the proposed visualization tool using various experimental results and cases studies. | false | false | [
"Hui Zhang 0006",
"Jianguang Weng",
"Guangchen Ruan"
] | [] | [] | [] |
SciVis | 2,014 | Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems | 10.1109/TVCG.2014.2346322 | As the size of image data from microscopes and telescopes increases, the need for high-throughput processing and visualization of large volumetric data has become more pressing. At the same time, many-core processors and GPU accelerators are commonplace, making high-performance distributed heterogeneous computing systems affordable. However, effectively utilizing GPU clusters is difficult for novice programmers, and even experienced programmers often fail to fully leverage the computing power of new parallel architectures due to their steep learning curve and programming complexity. In this paper, we propose Vivaldi, a new domain-specific language for volume processing and visualization on distributed heterogeneous computing systems. Vivaldi's Python-like grammar and parallel processing abstractions provide flexible programming tools for non-experts to easily write high-performance parallel computing code. Vivaldi provides commonly used functions and numerical operators for customized visualization and high-throughput image processing applications. We demonstrate the performance and usability of Vivaldi on several examples ranging from volume rendering to image segmentation. | false | false | [
"Hyungsuk Choi",
"Woohyuk Choi",
"Tran Minh Quan",
"David G. C. Hildebrand",
"Hanspeter Pfister",
"Won-Ki Jeong"
] | [] | [] | [] |
SciVis | 2,014 | Volume-Preserving Mapping and Registration for Collective Data Visualization | 10.1109/TVCG.2014.2346457 | In order to visualize and analyze complex collective data, complicated geometric structure of each data is desired to be mapped onto a canonical domain to enable map-based visual exploration. This paper proposes a novel volume-preserving mapping and registration method which facilitates effective collective data visualization. Given two 3-manifolds with the same topology, there exists a mapping between them to preserve each local volume element. Starting from an initial mapping, a volume restoring diffeomorphic flow is constructed as a compressible flow based on the volume forms at the manifold. Such a flow yields equality of each local volume element between the original manifold and the target at its final state. Furthermore, the salient features can be used to register the manifold to a reference template by an incompressible flow guided by a divergence-free vector field within the manifold. The process can retain the equality of local volume elements while registering the manifold to a template at the same time. An efficient and practical algorithm is also presented to generate a volume-preserving mapping and a salient feature registration on discrete 3D volumes which are represented with tetrahedral meshes embedded in 3D space. This method can be applied to comparative analysis and visualization of volumetric medical imaging data across subjects. We demonstrate an example application in multimodal neuroimaging data analysis and collective data visualization. | false | false | [
"Jiaxi Hu",
"Guangyu Zou",
"Jing Hua 0001"
] | [] | [] | [] |
SciVis | 2,014 | Vortex Cores of Inertial Particles | 10.1109/TVCG.2014.2346415 | The cores of massless, swirling particle motion are an indicator for vortex-like behavior in vector fields and to this end, a number of coreline extractors have been proposed in the literature. Though, many practical applications go beyond the study of the vector field. Instead, engineers seek to understand the behavior of inertial particles moving therein, for instance in sediment transport, helicopter brownout and pulverized coal combustion. In this paper, we present two strategies for the extraction of the corelines that inertial particles swirl around, which depend on particle density, particle diameter, fluid viscosity and gravity. The first is to deduce the local swirling behavior from the autonomous inertial motion ODE, which eventually reduces to a parallel vectors operation. For the second strategy, we use a particle density estimation to locate inertial attractors. With this, we are able to extract the cores of swirling inertial particle motion for both steady and unsteady 3D vector fields. We demonstrate our techniques in a number of benchmark data sets, and elaborate on the relation to traditional massless corelines. | false | false | [
"Tobias Günther",
"Holger Theisel"
] | [] | [] | [] |
InfoVis | 2,014 | A Principled Way of Assessing Visualization Literacy | 10.1109/TVCG.2014.2346984 | We describe a method for assessing the visualization literacy (VL) of a user. Assessing how well people understand visualizations has great value for research (e. g., to avoid confounds), for design (e. g., to best determine the capabilities of an audience), for teaching (e. g., to assess the level of new students), and for recruiting (e. g., to assess the level of interviewees). This paper proposes a method for assessing VL based on Item Response Theory. It describes the design and evaluation of two VL tests for line graphs, and presents the extension of the method to bar charts and scatterplots. Finally, it discusses the reimplementation of these tests for fast, effective, and scalable web-based use. | false | false | [
"Jeremy Boy",
"Ronald A. Rensink",
"Enrico Bertini",
"Jean-Daniel Fekete"
] | [] | [] | [] |
InfoVis | 2,014 | Activity Sculptures: Exploring the Impact of Physical Visualizations on Running Activity | 10.1109/TVCG.2014.2352953 | Data sculptures are a promising type of visualizations in which data is given a physical form. In the past, they have mostly been used for artistic, communicative or educational purposes, and designers of data sculptures argue that in such situations, physical visualizations can be more enriching than pixel-based visualizations. We present the design of Activity Sculptures: data sculptures of running activity. In a three-week field study we investigated the impact of the sculptures on 14 participants' running activity, the personal and social behaviors generated by the sculptures, as well as participants' experiences when receiving these individual physical tokens generated from the specific data of their runs. The physical rewards generated curiosity and personal experimentation but also social dynamics such as discussion on runs or envy/competition. We argue that such passive (or calm) visualizations can complement nudging and other mechanisms of persuasion with a more playful and reflective look at ones' activity. | false | false | [
"Simon Stusak",
"Aurélien Tabard",
"Franziska Sauka",
"Rohit Ashok Khot",
"Andreas Butz"
] | [] | [] | [] |
InfoVis | 2,014 | An Algebraic Process for Visualization Design | 10.1109/TVCG.2014.2346325 | We present a model of visualization design based on algebraic considerations of the visualization process. The model helps characterize visual encodings, guide their design, evaluate their effectiveness, and highlight their shortcomings. The model has three components: the underlying mathematical structure of the data or object being visualized, the concrete representation of the data in a computer, and (to the extent possible) a mathematical description of how humans perceive the visualization. Because we believe the value of our model lies in its practical application, we propose three general principles for good visualization design. We work through a collection of examples where our model helps explain the known properties of existing visualizations methods, both good and not-so-good, as well as suggesting some novel methods. We describe how to use the model alongside experimental user studies, since it can help frame experiment outcomes in an actionable manner. Exploring the implications and applications of our model and its design principles should provide many directions for future visualization research. | false | false | [
"Gordon L. Kindlmann",
"Carlos Scheidegger"
] | [
"HM"
] | [] | [] |
InfoVis | 2,014 | Attribute Signatures: Dynamic Visual Summaries for Analyzing Multivariate Geographical Data | 10.1109/TVCG.2014.2346265 | The visual analysis of geographically referenced datasets with a large number of attributes is challenging due to the fact that the characteristics of the attributes are highly dependent upon the locations at which they are focussed, and the scale and time at which they are measured. Specialized interactive visual methods are required to help analysts in understanding the characteristics of the attributes when these multiple aspects are considered concurrently. Here, we develop attribute signatures-interactively crafted graphics that show the geographic variability of statistics of attributes through which the extent of dependency between the attributes and geography can be visually explored. We compute a number of statistical measures, which can also account for variations in time and scale, and use them as a basis for our visualizations. We then employ different graphical configurations to show and compare both continuous and discrete variation of location and scale. Our methods allow variation in multiple statistical summaries of multiple attributes to be considered concurrently and geographically, as evidenced by examples in which the census geography of London and the wider UK are explored. | false | false | [
"Cagatay Turkay",
"Aidan Slingsby",
"Helwig Hauser",
"Jo Wood",
"Jason Dykes"
] | [] | [] | [] |
InfoVis | 2,014 | Axis Calibration for Improving Data Attribute Estimation in Star Coordinates Plots | 10.1109/TVCG.2014.2346258 | Star coordinates is a well-known multivariate visualization method that produces linear dimensionality reduction mappings through a set of radial axes defined by vectors in an observable space. One of its main drawbacks concerns the difficulty to recover attributes of data samples accurately, which typically lie in the [0], [1] interval, given the locations of the low-dimensional embeddings and the vectors. In this paper we show that centering the data can considerably increase attribute estimation accuracy, where data values can be read off approximately by projecting embedded points onto calibrated (i.e., labeled) axes, similarly to classical statistical biplots. In addition, this idea can be coupled with a recently developed orthonormalization process on the axis vectors that prevents unnecessary distortions. We demonstrate that the combination of both approaches not only enhances the estimates, but also provides more faithful representations of the data. | false | false | [
"Manuel Rubio-Sánchez",
"Alberto Sánchez 0001"
] | [] | [] | [] |
InfoVis | 2,014 | Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time | 10.1109/TVCG.2014.2346456 | With the continuous rise in complexity of modern supercomputers, optimizing the performance of large-scale parallel programs is becoming increasingly challenging. Simultaneously, the growth in scale magnifies the impact of even minor inefficiencies - potentially millions of compute hours and megawatts in power consumption can be wasted on avoidable mistakes or sub-optimal algorithms. This makes performance analysis and optimization critical elements in the software development process. One of the most common forms of performance analysis is to study execution traces, which record a history of per-process events and interprocess messages in a parallel application. Trace visualizations allow users to browse this event history and search for insights into the observed performance behavior. However, current visualizations are difficult to understand even for small process counts and do not scale gracefully beyond a few hundred processes. Organizing events in time leads to a virtually unintelligible conglomerate of interleaved events and moderately high process counts overtax even the largest display. As an alternative, we present a new trace visualization approach based on transforming the event history into logical time inferred directly from happened-before relationships. This emphasizes the code's structural behavior, which is much more familiar to the application developer. The original timing data, or other information, is then encoded through color, leading to a more intuitive visualization. Furthermore, we use the discrete nature of logical timelines to cluster processes according to their local behavior leading to a scalable visualization of even long traces on large process counts. We demonstrate our system using two case studies on large-scale parallel codes. | false | false | [
"Katherine E. Isaacs",
"Peer-Timo Bremer",
"Ilir Jusufi",
"Todd Gamblin",
"Abhinav Bhatele",
"Martin Schulz 0001",
"Bernd Hamann"
] | [] | [] | [] |
InfoVis | 2,014 | Comparative Eye Tracking Study on Node-Link Visualizations of Trajectories | 10.1109/TVCG.2014.2346420 | We present the results of an eye tracking study that compares different visualization methods for long, dense, complex, and piecewise linear spatial trajectories. Typical sources of such data are from temporally discrete measurements of the positions of moving objects, for example, recorded GPS tracks of animals in movement ecology. In the repeated-measures within-subjects user study, four variants of node-link visualization techniques are compared, with the following representations of directed links: standard arrow, tapered, equidistant arrows, and equidistant comets. In addition, we investigate the effect of rendering order for the halo visualization of those links as well as the usefulness of node splatting. All combinations of link visualization techniques are tested for different trajectory density levels. We used three types of tasks: tracing of paths, identification of longest links, and estimation of the density of trajectory clusters. Results are presented in the form of the statistical evaluation of task completion time, task solution accuracy, and two eye tracking metrics. These objective results are complemented by a summary of subjective feedback from the participants. The main result of our study is that tapered links perform very well. However, we discuss that equidistant comets and equidistant arrows are a good option to perceive direction information independent of zoom-level of the display. | false | false | [
"Rudolf Netzel",
"Michael Burch",
"Daniel Weiskopf"
] | [] | [] | [] |
InfoVis | 2,014 | Constructing Visual Representations: Investigating the Use of Tangible Tokens | 10.1109/TVCG.2014.2346292 | The accessibility of infovis authoring tools to a wide audience has been identified as a major research challenge. A key task in the authoring process is the development of visual mappings. While the infovis community has long been deeply interested in finding effective visual mappings, comparatively little attention has been placed on how people construct visual mappings. In this paper, we present the results of a study designed to shed light on how people transform data into visual representations. We asked people to create, update and explain their own information visualizations using only tangible building blocks. We learned that all participants, most of whom had little experience in visualization authoring, were readily able to create and talk about their own visualizations. Based on our observations, we discuss participants' actions during the development of their visual representations and during their analytic activities. We conclude by suggesting implications for tool design to enable broader support for infovis authoring. | false | false | [
"Samuel Huron",
"Yvonne Jansen",
"Sheelagh Carpendale"
] | [] | [] | [] |
InfoVis | 2,014 | Design Activity Framework for Visualization Design | 10.1109/TVCG.2014.2346331 | An important aspect in visualization design is the connection between what a designer does and the decisions the designer makes. Existing design process models, however, do not explicitly link back to models for visualization design decisions. We bridge this gap by introducing the design activity framework, a process model that explicitly connects to the nested model, a well-known visualization design decision model. The framework includes four overlapping activities that characterize the design process, with each activity explicating outcomes related to the nested model. Additionally, we describe and characterize a list of exemplar methods and how they overlap among these activities. The design activity framework is the result of reflective discussions from a collaboration on a visualization redesign project, the details of which we describe to ground the framework in a real-world design process. Lastly, from this redesign project we provide several research outcomes in the domain of cybersecurity, including an extended data abstraction and rich opportunities for future visualization research. | false | false | [
"Sean McKenna",
"Dominika Mazur",
"James Agutter",
"Miriah D. Meyer"
] | [] | [] | [] |
InfoVis | 2,014 | DimpVis: Exploring Time-varying Information Visualizations by Direct Manipulation | 10.1109/TVCG.2014.2346250 | We introduce a new direct manipulation technique, DimpVis, for interacting with visual items in information visualizations to enable exploration of the time dimension. DimpVis is guided by visual hint paths which indicate how a selected data item changes through the time dimension in a visualization. Temporal navigation is controlled by manipulating any data item along its hint path. All other items are updated to reflect the new time. We demonstrate how the DimpVis technique can be designed to directly manipulate position, colour, and size in familiar visualizations such as bar charts and scatter plots, as a means for temporal navigation. We present results from a comparative evaluation, showing that the DimpVis technique was subjectively preferred and quantitatively competitive with the traditional time slider, and significantly faster than small multiples for a variety of tasks. | false | false | [
"Brittany Kondo",
"Christopher Collins 0001"
] | [] | [] | [] |
InfoVis | 2,014 | Domino: Extracting, Comparing, and Manipulating Subsets Across Multiple Tabular Datasets | 10.1109/TVCG.2014.2346260 | Answering questions about complex issues often requires analysts to take into account information contained in multiple interconnected datasets. A common strategy in analyzing and visualizing large and heterogeneous data is dividing it into meaningful subsets. Interesting subsets can then be selected and the associated data and the relationships between the subsets visualized. However, neither the extraction and manipulation nor the comparison of subsets is well supported by state-of-the-art techniques. In this paper we present Domino, a novel multiform visualization technique for effectively representing subsets and the relationships between them. By providing comprehensive tools to arrange, combine, and extract subsets, Domino allows users to create both common visualization techniques and advanced visualizations tailored to specific use cases. In addition to the novel technique, we present an implementation that enables analysts to manage the wide range of options that our approach offers. Innovative interactive features such as placeholders and live previews support rapid creation of complex analysis setups. We introduce the technique and the implementation using a simple example and demonstrate scalability and effectiveness in a use case from the field of cancer genomics. | false | false | [
"Samuel Gratzl",
"Nils Gehlenborg",
"Alexander Lex",
"Hanspeter Pfister",
"Marc Streit"
] | [
"HM"
] | [] | [] |
InfoVis | 2,014 | Effects of Presentation Mode and Pace Control on Performance in Image Classification | 10.1109/TVCG.2014.2346437 | A common task in visualization is to quickly find interesting items in large sets. When appropriate metadata is missing, automatic queries are impossible and users have to inspect all elements visually. We compared two fundamentally different, but obvious display modes for this task and investigated the difference with respect to effectiveness, efficiency, and satisfaction. The static mode is based on the page metaphor and presents successive pages with a static grid of items. The moving mode is based on the conveyor belt metaphor and lets a grid of items slide though the screen in a continuous flow. In our evaluation, we applied both modes to the common task of browsing images. We performed two experiments where 18 participants had to search for certain target images in a large image collection. The number of shown images per second (pace) was predefined in the first experiment, and under user control in the second one. We conclude that at a fixed pace, the mode has no significant impact on the recall. The perceived pace is generally slower for moving mode, which causes users to systematically choose for a faster real pace than in static mode at the cost of recall, keeping the average number of target images found per second equal for both modes. | false | false | [
"Paul van der Corput",
"Jarke J. van Wijk"
] | [] | [] | [] |
InfoVis | 2,014 | Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error | 10.1109/TVCG.2014.2346298 | When making an inference or comparison with uncertain, noisy, or incomplete data, measurement error and confidence intervals can be as important for judgment as the actual mean values of different groups. These often misunderstood statistical quantities are frequently represented by bar charts with error bars. This paper investigates drawbacks with this standard encoding, and considers a set of alternatives designed to more effectively communicate the implications of mean and error data to a general audience, drawing from lessons learned from the use of visual statistics in the information visualization community. We present a series of crowd-sourced experiments that confirm that the encoding of mean and error significantly changes how viewers make decisions about uncertain data. Careful consideration of design tradeoffs in the visual presentation of data results in human reasoning that is more consistently aligned with statistical inferences. We suggest the use of gradient plots (which use transparency to encode uncertainty) and violin plots (which use width) as better alternatives for inferential tasks than bar charts with error bars. | false | false | [
"Michael Correll",
"Michael Gleicher"
] | [] | [] | [] |
InfoVis | 2,014 | Exploring the Placement and Design of Word-Scale Visualizations | 10.1109/TVCG.2014.2346435 | We present an exploration and a design space that characterize the usage and placement of word-scale visualizations within text documents. Word-scale visualizations are a more general version of sparklines-small, word-sized data graphics that allow meta-information to be visually presented in-line with document text. In accordance with Edward Tufte's definition, sparklines are traditionally placed directly before or after words in the text. We describe alternative placements that permit a wider range of word-scale graphics and more flexible integration with text layouts. These alternative placements include positioning visualizations between lines, within additional vertical and horizontal space in the document, and as interactive overlays on top of the text. Each strategy changes the dimensions of the space available to display the visualizations, as well as the degree to which the text must be adjusted or reflowed to accommodate them. We provide an illustrated design space of placement options for word-scale visualizations and identify six important variables that control the placement of the graphics and the level of disruption of the source text. We also contribute a quantitative analysis that highlights the effect of different placements on readability and text disruption. Finally, we use this analysis to propose guidelines to support the design and placement of word-scale visualizations. | false | false | [
"Pascal Goffin",
"Wesley Willett",
"Jean-Daniel Fekete",
"Petra Isenberg"
] | [] | [] | [] |
InfoVis | 2,014 | Four Experiments on the Perception of Bar Charts | 10.1109/TVCG.2014.2346320 | Bar charts are one of the most common visualization types. In a classic graphical perception paper, Cleveland &amp; McGill studied how different bar chart designs impact the accuracy with which viewers can complete simple perceptual tasks. They found that people perform substantially worse on stacked bar charts than on aligned bar charts, and that comparisons between adjacent bars are more accurate than between widely separated bars. However, the study did not explore why these differences occur. In this paper, we describe a series of follow-up experiments to further explore and explain their results. While our results generally confirm Cleveland &amp; McGill's ranking of various bar chart configurations, we provide additional insight into the bar chart reading task and the sources of participants' errors. We use our results to propose new hypotheses on the perception of bar charts. | false | false | [
"Justin Talbot",
"Vidya Setlur",
"Anushka Anand"
] | [] | [] | [] |
InfoVis | 2,014 | GLO-STIX: Graph-Level Operations for Specifying Techniques and Interactive eXploration | 10.1109/TVCG.2014.2346444 | The field of graph visualization has produced a wealth of visualization techniques for accomplishing a variety of analysis tasks. Therefore analysts often rely on a suite of different techniques, and visual graph analysis application builders strive to provide this breadth of techniques. To provide a holistic model for specifying network visualization techniques (as opposed to considering each technique in isolation) we present the Graph-Level Operations (GLO) model. We describe a method for identifying GLOs and apply it to identify five classes of GLOs, which can be flexibly combined to re-create six canonical graph visualization techniques. We discuss advantages of the GLO model, including potentially discovering new, effective network visualization techniques and easing the engineering challenges of building multi-technique graph visualization applications. Finally, we implement the GLOs that we identified into the GLO-STIX prototype system that enables an analyst to interactively explore a graph by applying GLOs. | false | false | [
"Charles D. Stolper",
"Minsuk Kahng",
"Zhiyuan Lin 0001",
"Florian Foerster",
"Aakash Goel",
"John T. Stasko",
"Polo Chau"
] | [] | [] | [] |
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