anchor.diff:  VAST,TVCG,EventThread: Visual Summarization and Stage Analysis of Event Sequence Data,"Shunan Guo, Ke Xu, Rongwen Zhao, David Gotz, Hongyuan Zha, Nan Cao",http://gotz.web.unc.edu/files/2013/10/PREPRINT-Guo-VIS-2017-EventThread-Preprint.pdf,"Event sequence data such as electronic health records, a person’s academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.",,,,vimeo 230831051,,2018,2017,VAST,301-C,Wednesday,Sequences and Events,2:20 PM,2:40 PM
   VAST,TVCG,Graphiti: Interactive Specification of Attribute-based Edges for Network Modeling and Visualization,"Arjun Srinivasan, Hyunwoo Park, Alex Endert, Rahul Basole",http://arjun010.github.io/static/papers/graphiti-vast-17.pdf,"Network visualizations, often in the form of node-link diagrams, are an effective means to understand relationships between entities, discover entities with interesting characteristics, and to identify clusters. While several existing tools allow users to visualize pre-defined networks, creating these networks from raw data remains a challenging task, often requiring users to program custom scripts or write complex SQL commands. Some existing tools also allow users to both visualize and model networks. Interaction techniques adopted by these tools often assume users know the exact conditions for defining edges in the resulting networks. This assumption may not always hold true, however. In cases where users do not know much about attributes in the dataset or when there are several attributes to choose from, users may not know which attributes they could use to formulate linking conditions. We propose an alternate interaction technique to model networks that allows users to demonstrate to the system a subset of nodes and links they wish to see in the resulting network. The system, in response, recommends conditions that can be used to model networks based on the specified nodes and links. In this paper, we show how such a demonstration-based interaction technique can be used to model networks by employing it in a prototype tool, Graphiti. Through multiple usage scenarios, we show how Graphiti not only allows users to model networks from a tabular dataset but also facilitates updating a pre-defined network with additional edge types.",,,,vimeo 230830104,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,5:35 PM,5:55 PM
   VAST,TVCG,How Do Ancestral Traits Shape Family Trees over Generations?,"Siwei Fu, Hao Dong, Weiwei Cui, Jian Zhao, Huamin Qu",,"Whether and how does the structure of family trees differ by ancestral traits over generations? This is a fundamental question regarding the structural heterogeneity of family trees for the multi-generational transmission research. However, previous work mostly focuses on parent-child scenarios due to the lack of proper tools to handle the complexity of extending the research to multi-generational processes. Through an iterative design study with social scientists and historians, we develop TreeEvo that assists users to generate and test empirical hypotheses for multi-generational research. TreeEvo summarizes and organizes family trees by structural features in a dynamic manner based on a traditional Sankey diagram. A pixel-based technique is further proposed to compactly encode trees with complex structures in each Sankey Node. Detailed information of trees is accessible through a space-efficient visualization with semantic zooming. Moreover, TreeEvo embeds Multinomial Logit Model (MLM) to examine statistical associations between tree structure and ancestral traits. We demonstrate the effectiveness and usefulness of TreeEvo through an in-depth case-study with domain experts using a real-world dataset (containing 54,128 family trees of 126,196 individuals).",,,,youtube 7MqeoD49wVg,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:55 PM,5:15 PM
  -VAST,TVCG,LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets,"Jiazhi Xia, Fenjin Ye, Wei Chen, Yusi Wang, Weifeng Chen, Yuxin Ma, Anthony K. H. Tung",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/LDSScanner.pdf,"Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the x axis and y axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS (x axis) and the variation of LTS in structures (the combination of x axis and y axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.",,,,vimeo 230830366,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,8:30 AM,8:50 AM
  +VAST,TVCG,LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets,"Jiazhi Xia, Fenjin Ye, Wei Chen, Yusi Wang, Weifeng Chen, Yuxin Ma, Anthony K. H. Tung",,"Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the x axis and y axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS (x axis) and the variation of LTS in structures (the combination of x axis and y axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.",,,,vimeo 230830366,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,8:30 AM,8:50 AM
   VAST,TVCG,PhenoLines: Phenotype Comparison Visualizations for Disease Subtyping via Topic Models,"Michael Glueck, Mahdi Pakdaman Naeini, Finale Doshi-Velez, Fanny Chevalier, Azam Khan, Daniel Wigdor, Michael Brudno",,,,,,,,2018,2017,VAST,207,Wednesday,Text Analytics,11:30 AM,11:50 AM
   VAST,TVCG,Podium: Ranking Data Using Mixed-Initiative Visual Analytics,"Emily Wall, Subhajit Das, Ravish Chawla, Bharath Kalidindi, Eli T. Brown, Alex Endert",https://www.cc.gatech.edu/~ewall9/media/papers/PodiumVAST17.pdf,"People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user’s data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user’s subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings.Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas",,,,youtube Z4N9wKxK2i4,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:00 PM,2:20 PM
   VAST,TVCG,Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework,"Mennatallah El-Assady, Rita Sevastjanova, Fabian Sperrle, Daniel Keim, Christopher Collins",https://bib.dbvis.de/uploadedFiles/progressivelearningtopic.pdf,"Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.",,,,vimeo 230830619,,2018,2017,VAST,207,Wednesday,Text Analytics,11:50 AM,12:10 PM
  
positive.code: TVCG,TVCG,A Statistical Direct Volume Rendering Framework for Visualization of Uncertain Data,"Elham Sakhaee, Alireza Entezari",https://www.cise.ufl.edu/~esakhaee/papers/VIS/uncertaintyDVR.pdf,"With uncertainty present in almost all modalities of data acquisition, reduction, transformation, and representation, there is a growing demand for mathematical analysis of uncertainty propagation in data processing pipelines. In this paper, we present a statistical framework for quantification of uncertainty and its propagation in the main stages of the visualization pipeline. We propose a novel generalization of Irwin-Hall distributions from the statistical viewpoint of splines and box-splines, that enables interpolation of random variables. Moreover, we introduce a probabilistic transfer function classification model that allows for incorporating probability density functions into the volume rendering integral. Our statistical framework allows for incorporating distributions from various sources of uncertainty which makes it suitable in a wide range of visualization applications. We demonstrate effectiveness of our approach in visualization of ensemble data, visualizing large datasets at reduced scale, iso-surface extraction, and visualization of noisy data.",,,,vimeo 230834547,10.1109/TVCG.2016.2637333,2016,2017,SciVis,207,Tuesday,Volume Rendering,4:35 PM,4:55 PM
  TVCG,TVCG,The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspaces,"Bing Wang, Klaus Mueller",https://arxiv.org/ftp/arxiv/papers/1603/1603.04781.pdf,"Analyzing high-dimensional data and finding hidden patterns is a difficult problem and has attracted numerous research efforts. Automated methods can be useful to some extent but bringing the data analyst into the loop via interactive visual tools can help the discovery process tremendously. An inherent problem in this effort is that humans lack the mental capacity to truly understand spaces exceeding three spatial dimensions. To keep within this limitation, we describe a framework that decomposes a high-dimensional data space into a continuum of generalized 3D subspaces. Analysts can then explore these 3D subspaces individually via the familiar trackball interface while using additional facilities to smoothly transition to adjacent subspaces for expanded space comprehension. Since the number of such subspaces suffers from combinatorial explosion, we provide a set of data-driven subspace selection and navigation tools which can guide users to interesting subspaces and views. A subspace trail map allows users to manage the explored subspaces, keep their bearings, and return to interesting subspaces and views. Both trackball and trail map are each embedded into a word cloud of attribute labels which aid in navigation. We demonstrate our system via several use cases in a diverse set of application areas – cluster analysis and refinement, information discovery, and supervised training of classifiers. We also report on a user study that evaluates the usability of the various interactions our system provides.",,,,youtube c-yKtajfGQE,10.1109/TVCG.2017.2672987,2016,2017,VAST,301-C,Wednesday,High-dimensional Data,9:50 AM,10:10 AM
  TVCG,TVCG,TopKube: A Rank-Aware Data Cube for Real-Time Exploration of Spatiotemporal Data,"Fabio Miranda, Lauro Lins, James Klosowski, Claudio Silva",https://vgc.poly.edu/~fmiranda/topkube/tvcg-2017-topkube.pdf,"From economics to sports to entertainment and social media, ranking objects according to some notion of importance is a fundamental tool we humans use all the time to better understand our world. With the ever-increasing amount of user-generated content found online, “what’s trending” is now a commonplace phrase that tries to capture the zeitgeist of the world by ranking the most popular microblogging hashtags in a given region and time. However, before we can understand what these rankings tell us about the world, we need to be able to more easily create and explore them, given the significant scale of today’s data. In this paper, we describe the computational challenges in building a real-time visual exploratory tool for finding top-ranked objects; build on the recent work involving in-memory and rank-aware data cubes to propose TOPKUBE: a data structure that answers top-k queries up to one order of magnitude faster than the previous state of the art; demonstrate the usefulness of our methods using a set of real-world, publicly available datasets; and provide a new set of benchmarks for other researchers to validate their methods and compare to our own.",,,,,10.1109/TVCG.2017.2671341,2017,2017,InfoVis,301-D,Tuesday,Time and Space,5:35 PM,5:55 PM
  TVCG,TVCG,Perceptual Biases in Font Size as a Data Encoding,"Eric Carlson Alexander, Chih-Ching Chang, Mariana Shimabukuro, Steve Franconeri, Christopher Collins, Michael Gleicher",http://vialab.science.uoit.ca/wp-content/papercite-data/pdf/shi2017fontsize.pdf,"Many visualizations, including word clouds, cartographic labels, and word trees, encode data within the sizes of fonts. While font size can be an intuitive dimension for the viewer, using it as an encoding can introduce factors that may bias the perception of the underlying values. Viewers might conflate the size of a word’s font with a word’s length, the number of letters it contains, or with the larger or smaller heights of particular characters (‘o’ vs. ‘p’ vs. ‘b’). We present a collection of empirical studies showing that such factors—which are irrelevant to the encoded values—can indeed influence comparative judgements of font size, though less than conventional wisdom might suggest. We highlight the largest potential biases, and describe a strategy to mitigate them.",,,,vimeo 230834531,10.1109/TVCG.2017.2723397,2017,2017,InfoVis,301-D,Wednesday,Perception,9:10 AM,9:30 AM
  TVCG,TVCG,Data Flow Analysis and Visualization for Spatiotemporal Statistical Data without Trajectory Information,"Seokyeon Kim, Seongmin Jeong, Insoo Woo, Yun Jang, Ross Maciejewski, David Ebert",http://rmaciejewski.faculty.asu.edu/papers/2017/Yun-Flow.pdf,"Geographic visualization research has focused on a variety of techniques to represent and explore spatiotemporal data. The goal of those techniques is to enable users to explore events and interactions over space and time in order to facilitate the discovery of patterns, anomalies and relationships within the data. However, it is difficult to extract and visualize data flow patterns over time for non-directional statistical data without trajectory information. In this work, we develop a novel flow analysis technique to extract, represent, and analyze flow maps of non-directional spatiotemporal data unaccompanied by trajectory information. We estimate a continuous distribution of these events over space and time, and extract flow fields for spatial and temporal changes utilizing a gravity model. Then, we effectively visualize the spatiotemporal patterns in the data by employing flow visualization techniques. The user is presented with temporal trends of geo-referenced discrete events on a map. As such, overall spatiotemporal data flow patterns help users analyze geo-referenced temporal events, such as disease outbreaks, crime patterns, etc. To validate our model, we discard the trajectory information in an origin-destination dataset and apply our technique to the data and compare the derived trajectories and the original. Finally, we present spatiotemporal trend analysis for statistical datasets including twitter data, maritime search and rescue events, and syndromic surveillance",,,,vimeo 230834322,10.1109/TVCG.2017.2666146,2017,2017,VAST,301-C,Tuesday,"Space, Time, Movement",3:20 PM,3:40 PM
  TVCG,TVCG,Evaluating Interactive Graphical Encodings for Data Visualization,"Bahador Saket, Arjun Srinivasan, Eric D. Ragan, Alex Endert",http://bahadorsaket.com/publication/encodingsPaper.pdf,"User interfaces for data visualization often consist of two main components: control panels for user interaction and visual representation. A recent trend in visualization is directly embedding user interaction into the visual representations. For example, instead of using control panels to adjust visualization parameters, users can directly adjust basic graphical encodings (e.g., changing distances between points in a scatterplot) to perform similar parameterizations. However, enabling embedded interactions for data visualization requires a strong understanding of how user interactions influence the ability to accurately control and perceive graphical encodings. In this paper, we study the effectiveness of these graphical encodings when serving as the method for interaction. Our user study includes 12 interactive graphical encodings. We discuss the results in terms of task performance and interaction effectiveness metrics.",https://medium.com/@GT_Vis/evaluating-interactive-graphical-encodings-for-data-visualization-b9b8b4bf47f8,https://github.com/gtvalab/interactive-graphical-encodings,https://github.com/gtvalab/interactive-graphical-encodings/tree/master/Supplementary%20Materials/Raw%20Data,vimeo 230834446,10.1109/TVCG.2017.2680452,2017,2017,InfoVis,301-D,Wednesday,Perception,9:50 AM,10:10 AM
  TVCG,TVCG,PETMiner - A Visual Analysis Tool for Petrophysical Properties of Core Sample Data,"Dave G. Harrison, Nick D. Efford, Quentin J. Fisher, Roy A. Ruddle",http://eprints.whiterose.ac.uk/113580/1/harrison-ieee-tvcg-2017.pdf,"The aim of the PETMiner software is to reduce the time and monetary cost of analysing petrophysical data that is obtained from reservoir sample cores. Analysis of these data requires tacit knowledge to fill ’gaps’ so that predictions can be made for incomplete data. Through discussions with 30 industry and academic specialists, we identified three analysis use cases that exemplified the limitations of current petrophysics analysis tools. We used those use cases to develop nine core requirements for PETMiner, which is innovative because of its ability to display detailed images of the samples as data points, directly plot multiple sample properties and derived measures for comparison, and substantially reduce interaction cost. An 11-month evaluation demonstrated benefits across all three use cases by allowing a consultant to: (1) generate more accurate reservoir flow models, (2) discover a previously unknown relationship between one easy-to-measure property and another that is costly, and (3) make a 100-fold reduction in the time required to produce plots for a report.",,,,vimeo 230834380,10.1109/TVCG.2017.2682865,2017,2017,SciVis,106-ABC,Friday,Applications and Visual analysis,9:10 AM,9:30 AM
  TVCG,TVCG,Indexed-Points Parallel Coordinates Visualization of Multivariate Correlations,"Liang Zhou, Daniel Weiskopf",http://www.cs.utah.edu/~zhoul/Research/mypapers/pflatsInPCP_authors.pdf,"We address the problem of visualizing multivariate correlations in parallel coordinates. We focus on multivariate correlation in the form of linear relationships between multiple variables. Traditional parallel coordinates are well prepared to show negative correlations between two attributes by distinct visual patterns. However, it is difficult to recognize positive correlations in parallel coordinates. Furthermore, there is no support to highlight multivariate correlations in parallel coordinates. In this paper, we exploit the indexed point representation of p-flats (planes in multidimensional data) to visualize local multivariate correlations in parallel coordinates. Our method yields clear visual signatures for negative and positive correlations alike, and it supports large datasets. All information is shown in a unified parallel coordinates framework, which leads to easy and familiar user interactions for analysts who have experience with traditional parallel coordinates. The usefulness of our method is demonstrated through examples of typical multidimensional datasets.",,,,vimeo 230834568,10.1109/TVCG.2017.2698041,2017,2017,InfoVis,301-D,Wednesday,Multidimensional Data,3:20 PM,3:40 PM
  TVCG,TVCG,Visual Analysis of Inclusion Dynamics in Two-Phase Flow,"Grzegorz Karch, Fabian Beck, Moritz Ertl, Christian Meister, Kathrin Schulte, Bernhard Weigand, Thomas Ertl, Filip Sadlo",,,,,,vimeo 230834468,10.1109/TVCG.2017.2692781,2017,2017,SciVis,207,Wednesday,Flow Visualization,8:50 AM,9:10 AM
  TVCG,TVCG,A Combined Eulerian-Lagrangian Data Representation for Large-scale Applications,"Franz Sauer, Jinrong Xie, Kwan-Liu Ma",http://vis.cs.ucdavis.edu/~fsauer/TVCG_17/TVCG_17_Paper-1.pdf,"The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations are becoming increasingly popular. However, due to their fundamentally different nature, drawing correlations between these data formats is a computationally difficult task, especially in a large-scale setting. In this work, we present a new data representation which combines both reference frames into a joint Eulerian-Lagrangian format. By reorganizing Lagrangian information according to the Eulerian simulation grid into a “unit cell” based approach, we can provide an efficient out-of-core means of sampling, querying, and operating with both representations simultaneously. We also extend this design to generate multi-resolution subsets of the full data to suit the viewer’s needs and provide a fast flow-aware trajectory construction scheme. We demonstrate the effectiveness of our method using three large-scale real world scientific datasets and provide insight into the types of performance gains that can be achieved.",,,,vimeo 230834211,10.1109/TVCG.2016.2620975,2016,2017,SciVis,207,Wednesday,Flow Visualization,9:10 AM,9:30 AM
  TVCG,TVCG,Keshif: Rapid and Expressive Tabular Data Exploration for Novices,"Mehmet Adil Yalcin, Niklas Elmqvist, Benjamin B Bederson",http://www.umiacs.umd.edu/~elm/projects/keshif/keshif.pdf,"General purpose graphical interfaces for data exploration are typically based on manual visualization and interaction specifications. While designing manual specification can be very expressive, it demands high efforts to make effective decisions, therefore reducing exploratory speed. Instead, principled automated designs can increase exploratory speed, decrease learning efforts, help avoid ineffective decisions, and therefore better support data analytics novices. Towards these goals, we present Keshif, a new systematic design for tabular data exploration. To summarize a given dataset, Keshif aggregates records by value within attribute summaries, and visualizes aggregate characteristics using a consistent design based on data types. To reveal data distribution details, Keshif features three complementary linked selections: highlighting, filtering, and comparison. Keshif further increases expressiveness through aggregate metrics, absolute/part-of scale modes, calculated attributes, and saved selections, all working in synchrony. Its automated design approach also simplifies authoring of dashboards composed of summaries and individual records from raw data using fluid interaction. We show examples selected from 160+ datasets from diverse domains. Our study with novices shows that after exploring raw data for 15 minutes, our participants reached close to 30 data insights on average, comparable to other studies with skilled users using more complex tools.",https://www.keshif.me/,https://github.com/adilyalcin/Keshif,,youtube 3Hmvms-1grU,10.1109/TVCG.2017.2723393,2017,2017,InfoVis,301-D,Wednesday,Multidimensional Data,2:40 PM,3:00 PM
  TVCG,TVCG,DSPCP: A Data Scalable Approach for Identifying Relationships in Parallel Coordinates,"Hoa Nguyen, Paul Rosen",http://www.cspaul.com/publications/Nguyen.2017.TVCG.pdf,"Parallel coordinates plots (PCPs) are a well-studied technique for exploring multi-attribute datasets. In many situations, users find them a flexible method to analyze and interact with data. Unfortunately, using PCPs becomes challenging as the number of data items grows large or multiple trends within the data mix in the visualization. The resulting overdraw can obscure important features. A number of modifications to PCPs have been proposed, including using color, opacity, smooth curves, frequency, density, and animation to mitigate this problem. However, these modified PCPs tend to have their own limitations in the kinds of relationships they emphasize. We propose a new data scalable design for representing and exploring data relationships in PCPs. The approach exploits the point/line duality property of PCPs and a local linear assumption of data to extract and represent relationship summarizations. This approach simultaneously shows relationships in the data and the consistency of those relationships. Our approach supports various visualization tasks, including mixed linear and nonlinear pattern identification, noise detection, and outlier detection, all in large data. We demonstrate these tasks on multiple synthetic and real-world datasets.",,,,youtube OApngoT5kbg,10.1109/TVCG.2017.2661309,2017,2017,SciVis,106-ABC,Friday,Applications and Visual analysis,8:50 AM,9:10 AM
  TVCG,TVCG,RCLens: Interactive Rare Category Exploration and Identification,"Hanfei Lin, Siyuan Gao, David Gotz, Fan Du, Jingrui He, Nan Cao",http://frankdu.org/papers/lin2017tvcg.pdf,"Rare category identification is an important task in many application domains, ranging from network security, to financial fraud detection, to personalized medicine. These are all applications which require the discovery and characterization of sets of rare but structurally-similar data entities which are obscured within a larger but structurally different dataset. This paper introduces RCLens, a visual analytics system designed to support user-guided rare category exploration and identification. RCLens adopts a novel active learning-based algorithm to iteratively identify more accurate rare categories in response to user-provided feedback. The algorithm is tightly integrated with an interactive visualization-based interface which supports a novel and effective workflow for rare category identification. This paper (1) defines RCLens’ underlying active-learning algorithm; (2) describes the visualization and interaction designs, including a discussion of how the designs support user-guided rare category identification; and (3) presents results from an evaluation demonstrating RCLens’ ability to support the rare category identification process",,,,vimeo 230834420,10.1109/TVCG.2017.2711030,2017,2017,VAST,207,Thursday,Sensemaking,4:15 PM,4:35 PM
  
negative.code: InfoVis,TVCG,Visual Exploration of Semantic Relationships in Neural Word Embeddings,"Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Vivek Srikumar, Bei Wang, Yarden Livnat, Valerio Pascucci",http://www.sci.utah.edu/~shusenl/publications/paper157.pdf,"Constructing distributed representations for words through neural language models and using the resulting vector spaces for analysis has become a crucial component of natural language processing (NLP). However, despite their widespread application, little is known about the structure and properties of these spaces. To gain insights into the relationship between words, the NLP community has begun to adapt high-dimensional visualization techniques. In particular, researchers commonly use t-distributed stochastic neighbor embeddings (t-SNE) and principal component analysis (PCA) to create two-dimensional embeddings for assessing the overall structure and exploring linear relationships (e.g., word analogies), respectively. Unfortunately, these techniques often produce mediocre or even misleading results and cannot address domain-specific visualization challenges that are crucial for understanding semantic relationships in word embeddings. Here, we introduce new embedding techniques for visualizing semantic and syntactic analogies, and the corresponding tests to determine whether the resulting views capture salient structures. Additionally, we introduce two novel views for a comprehensive study of analogy relationships. Finally, we augment t-SNE embeddings to convey uncertainty information in order to allow a reliable interpretation. Combined, the different views address a number of domain-specific tasks difficult to solve with existing tools.",,,,vimeo 230840739,,2018,2017,InfoVis,301-D,Wednesday,Multidimensional Data,3:00 PM,3:20 PM
  InfoVis,TVCG,Visualizing Nonlinear Narratives with Story Curves,"Nam Wook Kim, Benjamin Bach, Hyejin Im, Sasha Schriber, Markus Gross, Hanspeter Pfister",https://vcg.seas.harvard.edu/content/3-publications/20180101-visualizing-nonlinear-narratives-with-story-curves/paper.pdf,"In this paper, we present story curves, a visualization technique for exploring and communicating nonlinear narratives in movies. A nonlinear narrative is a storytelling device that portrays events of a story out of chronological order, e.g., in reverse order or going back and forth between past and future events. Many acclaimed movies employ unique narrative patterns which in turn have inspired other movies and contributed to the broader analysis of narrative patterns in movies. However, understanding and communicating nonlinear narratives is a difficult task due to complex temporal disruptions in the order of events as well as no explicit records specifying the actual temporal order of the underlying story. Story curves visualize the nonlinear narrative of a movie by showing the order in which events are told in the movie and comparing them to their actual chronological order, resulting in possibly meandering visual patterns in the curve. We also present Story Explorer, an interactive tool that visualizes a story curve together with complementary information such as characters and settings. Story Explorer further provides a script curation interface that allows users to specify the chronological order of events in movies. We used Story Explorer to analyze 10 popular nonlinear movies and describe the spectrum of narrative patterns that we discovered, including some novel patterns not previously described in the literature. Feedback from experts highlights potential use cases in screenplay writing and analysis, education and film production. A controlled user study shows that users with no expertise are able to understand visual patterns of nonlinear narratives using story curves.",,,,youtube XQ6xPkAZsPU,,2018,2017,InfoVis,301-D,Tuesday,Techniques,2:00 PM,2:20 PM
  InfoVis,TVCG,What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization,"Oh-Hyun Kwon, Tarik Crnovrsanin, Kwan-Liu Ma",http://vis.cs.ucdavis.edu/papers/kwon_infovis17.pdf,"Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a “good” layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.",http://graphvis.net/wgl,,,vimeo 230840405,,2018,2017,InfoVis,301-D,Thursday,Graphs and Paths,4:15 PM,4:35 PM
  VAST,TVCG,A Utility-aware Visual Approach for Anonymizing Multi-attribute Tabular Data,"Xumeng Wang, Jia-Kai Chou, Wei Chen, Huihua Guan, Wenlong Chen, Tianyi Lao, Kwan-Liu Ma",,"Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach.",,,,vimeo 230830747,,2018,2017,VAST,301-C,Thursday,Visual Representation and Design Study,9:30 AM,9:50 AM
  VAST,TVCG,ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models,"Minsuk Kahng, Pierre Andrews, Aditya Kalro, Duen Horng Chau",https://arxiv.org/pdf/1704.01942.pdf,"While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ACTIVIS, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ACTIVIS has been deployed on Facebook’s machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ACTIVIS may work with different models.",,,,vimeo 230829900,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,11:10 AM,11:30 AM
  VAST,TVCG,Analyzing the Training Processes of Deep Generative Models,"Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, Shixia Liu",http://shixialiu.com/publications/dgmtracker/paper.pdf,"Among the many types of deep models, deep generative models (DGMs) provide a solution to the important problem of unsupervised and semi-supervised learning. However, training DGMs requires more skill, experience, and know-how because their training is more complex than other types of deep models such as convolutional neural networks (CNNs). We develop a visual analytics approach for better understanding and diagnosing the training process of a DGM. To help experts understand the overall training process, we first extract a large amount of time series data that represents training dynamics (e.g., activation changes over time). A blue-noise polyline sampling scheme is then introduced to select time series samples, which can both preserve outliers and reduce visual clutter. To further investigate the root cause of a failed training process, we propose a credit assignment algorithm that indicates how other neurons contribute to the output of the neuron causing the training failure. Two case studies are conducted with machine learning experts to demonstrate how our approach helps understand and diagnose the training processes of DGMs. We also show how our approach can be directly used to analyze other types of deep models, such as CNNs.",,,,video http://shixialiu.com/publications/dgmtracker/video.mp4,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,10:30 AM,10:50 AM
  VAST,TVCG,Applying Pragmatics Principles for Interaction with Visual Analytics,"Enamul Hoque, Vidya Setlur, Melanie Tory, Isaac Dykeman",https://research.tableau.com/sites/default/files/VAST2017_105.pdf,"Interactive visual data analysis is most productive when users can focus on answering the questions they have about their data, rather than focusing on how to operate the interface to the analysis tool. One viable approach to engaging users in interactive conversations with their data is a natural language interface to visualizations. These interfaces have the potential to be both more expressive and more accessible than other interaction paradigms. We explore how principles from language pragmatics can be applied to the flow of visual analytical conversations, using natural language as an input modality. We evaluate the effectiveness of pragmatics support in our system Evizeon, and present design considerations for conversation interfaces to visual analytics tools.",,,,vimeo 230831885,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:40 PM,3:00 PM
  