anchor.diff:  VAST,TVCG,Analyzing the Training Processes of Deep Generative Models,"Mengchen Liu, Jiaxin Shi, Kelei Cao, Jun Zhu, Shixia Liu",,,,,,,,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
   VAST,TVCG,Beyond Tasks: An Activity Typology for Visual Analytics,"Darren Edge, Nathalie Henry Riche, Jonathan Larson, Christopher White",,,,,,,,2018,2017,VAST,301-C,Thursday,Theory and Analysis Process,10:30 AM,10:50 AM
  -VAST,TVCG,BiDots: Visual Exploration of Weighted Biclusters,"Jian Zhao, Maoyuan Sun, Francine Chen, Patrick Chiu",,,,,,,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:35 PM,4:55 PM
  -VAST,TVCG,Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis,"Manuel Stein, Halldór Janetzko, Andreas Lamprecht, Thorsten Breitkreutz, Philipp Zimmermann, Bastian Goldlücke, Tobias Schreck, Gennady Andrienko, Michael Grossniklaus, Daniel Keim",,,,,,,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:00 PM,2:20 PM
  -VAST,TVCG,Clustering Trajectories by Relevant Parts for Air Traffic Analysis,"Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jose Manuel Cordero Garcia",,,,,,,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:40 PM,3:00 PM
  +VAST,TVCG,BiDots: Visual Exploration of Weighted Biclusters,"Jian Zhao, Maoyuan Sun, Francine Chen, Patrick Chiu",,,,,,youtube 79VSgVIl11M,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,4:35 PM,4:55 PM
  +VAST,TVCG,Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis,"Manuel Stein, Halldór Janetzko, Andreas Lamprecht, Thorsten Breitkreutz, Philipp Zimmermann, Bastian Goldlücke, Tobias Schreck, Gennady Andrienko, Michael Grossniklaus, Daniel Keim",,,,,,vimeo 230830922,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:00 PM,2:20 PM
  +VAST,TVCG,Clustering Trajectories by Relevant Parts for Air Traffic Analysis,"Gennady Andrienko, Natalia Andrienko, Georg Fuchs, Jose Manuel Cordero Garcia",http://openaccess.city.ac.uk/18119/1/main-reducedSize.pdf,"Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.",,,,vimeo 227918021,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:40 PM,3:00 PM
   VAST,TVCG,Clustervision: Visual Supervision of Unsupervised Clustering,"Bum Chul Kwon, Ben Eysenbach, Janu Verma, Kenney Ng, Christopher deFilippi, Walter Stewart, Adam Perer",http://perer.org/papers/adamPerer-Clustervision-VAST2017.pdf,"Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built <em>Clustervision</em>, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.",,,,vimeo 232177941,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,5:15 PM,5:35 PM
  -VAST,TVCG,Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study,"Jürgen Bernard, Marco Hutter, Matthias Zeppelzauer, Dieter Fellner, Michael Sedlmair",,,,,,,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:20 PM,2:40 PM
  +VAST,TVCG,Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study,"Jürgen Bernard, Marco Hutter, Matthias Zeppelzauer, Dieter Fellner, Michael Sedlmair",http://homepage.univie.ac.at/michael.sedlmair/papers/bernard2017labeling.pdf,"Labeling data instances is an important task in machine learning and visual analytics. Both fields provide a broad set of labeling strategies, whereby machine learning (and in particular active learning) follows a rather model-centered approach and visual analytics employs rather user-centered approaches (visual-interactive labeling). Both approaches have individual strengths and weaknesses. In this work, we conduct an experiment with three parts to assess and compare the performance of these different labeling strategies. In our study, we (1) identify different visual labeling strategies for user-centered labeling, (2) investigate strengths and weaknesses of labeling strategies for different labeling tasks and task complexities, and (3) shed light on the effect of using different visual encodings to guide the visual-interactive labeling process. We further compare labeling of single versus multiple instances at a time, and quantify the impact on efficiency. We systematically compare the performance of visual interactive labeling with that of active learning. Our main findings are that visual-interactive labeling can outperform active learning, given the condition that dimension reduction separates well the class distributions. Moreover, using dimension reduction in combination with additional visual encodings that expose the internal state of the learning model turns out to improve the performance of visual-interactive labeling.",,,,video http://homepage.univie.ac.at/michael.sedlmair/papers/bernard2017labeling.mp4,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,2:20 PM,2:40 PM
   VAST,TVCG,ConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding,"Deokgun Park, Seungyeon Kim, Jurim Lee, Jaegul Choo, Nicholas Diakopoulos, Niklas Elmqvist",http://www.umiacs.umd.edu/~elm/projects/conceptvector/conceptvector.pdf,"Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building such concepts from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of human language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides the user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts using user seed terms, we introduce a bipolar concept model and support for irrelevant words. We validate the interactive lexicon building interface via a user study and expert reviews. The quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones.",,,,youtube -_gANFu-ulY,,2018,2017,VAST,207,Wednesday,Text Analytics,11:10 AM,11:30 AM
   VAST,TVCG,DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks,"Nicola Pezzotti, Thomas Höllt, Jan van Gemert, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova",https://graphics.tudelft.nl/Publications-new/2018/PHVLEV18/paper216.pdf,"Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.",,,,vimeo 230830385,,2018,2017,VAST,301-C,Wednesday,ML1: Deep Learning,11:30 AM,11:50 AM
   VAST,TVCG,Do Convolutional Neural Networks learn Class Hierarchy?,"Bilal Alsallakh, Amin Jourabloo, Mao Ye, Xiaoming Liu, Liu Ren",http://cvlab.cse.msu.edu/pdfs/DoConvolutionalNeuralNetworksLearnClassHierarchy.pdf,"Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.",,,,vimeo 230831852,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,8:30 AM,8:50 AM
  
positive.code: VAST,TVCG,Sequence Synopsis: Optimize Visual Summary of Temporal Event Data,"Yuanzhe Chen, Panpan Xu, Liu Ren",http://lliquid.github.io/homepage/files/ss_vast17.pdf,"Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback.",,http://lliquid.github.io/homepage/files/ss_vast17_sup.pdf,,vimeo 230830645,,2018,2017,VAST,301-C,Wednesday,Sequences and Events,2:00 PM,2:20 PM
  VAST,TVCG,SkyLens: Visual Analysis of Skyline on Multi-dimensional Data,"Xun Zhao, Yanhong Wu, Weiwei Cui, Xinnan Du, Yuan Chen, Yong Wang, Dik-Lun Lee, Huamin Qu",http://yhwu.me/publications/skylens_vast17.pdf,"Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens.",,,,vimeo 230829927,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,9:10 AM,9:30 AM
  VAST,TVCG,SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance,"Dominik Sacha, Matthias Kraus, Jürgen Bernard, Michael Behrisch, Tobias Schreck, Yuki Asano, Daniel Keim",,,,,,,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,4:35 PM,4:55 PM
  VAST,TVCG,Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs,"Jian Zhao, Michael Glueck, Petra Isenberg, Fanny Chevalier, Azam Khan",,,,,,,,2018,2017,VAST,207,Thursday,Sensemaking,4:55 PM,5:15 PM
  VAST,TVCG,The Interactive Visualization Gap in Initial Exploratory Analysis,"Andrea Batch, Niklas Elmqvist",,,,,,,,2018,2017,VAST,301-C,Thursday,Theory and Analysis Process,11:50 AM,12:10 PM
  VAST,TVCG,Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics,"John Wenskovitch, Ian Crandell, Naren Ramakrishnan, Leanna House, Scotland Leman, Chris North",,,,,,,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,4:55 PM,5:15 PM
  VAST,TVCG,TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees,"Thomas Mühlbacher, Lorenz Linhardt, Torsten Möller, Harald Piringer",,,,,,youtube a3QC3crIsxc,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,9:30 AM,9:50 AM
  VAST,TVCG,Understanding a sequence of sequences: Visual exploration of categorical states in lake sediment cores,"Andrea Unger, Nadine Dräger, Mike Sips, Dirk Lehmann",http://46.38.235.241/webpage/dirkfiles/publications/Lehmann_2017_TVCG2_VAST.pdf,"This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analysis of lake sediment cores. The goal is to gain hypotheses about landscape evolution and climate conditions in the past. To this end, geoscientists identify which categorical sequences are similar in the sense that they indicate similar conditions. Categorical sequences are similar if they have similar meaning (semantic similarity) and appear in similar time periods (temporal similarity). For data sets with many different categorical sequences, the task to identify similar sequences becomes a challenge. Our contribution is a tailored visual analysis concept that effectively supports the analytical process. Our visual interface comprises coupled visualizations of semantics and temporal context for the exploration and assessment of the similarity of categorical sequences. Integrated automatic methods reduce the analytical effort substantially. They (1) extract unique sequences in the data and (2) rank sequences by a similarity measure during the search for similar sequences. We evaluated our concept by demonstrations of our prototype to a larger audience and hands-on analysis sessions for two different lakes. According to geoscientists, our approach fills an important methodological gap in the application domain",,,,youtube upjkAJTgVXI,,2018,2017,VAST,301-C,Wednesday,Sequences and Events,3:20 PM,3:40 PM
  VAST,TVCG,Understanding the Relationship between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy,"Jie Liu, Tim Dwyer, Kim Marriott, Jeremy Millar, Annette Haworth",,,,,,,,2018,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,3:00 PM,3:20 PM
  VAST,TVCG,VIGOR: Interactive Visual Exploration of Graph Query Results,"Robert Pienta, Fred Hohman, Alex Endert, Acar Tamersoy, Kevin Roundy, Chris Gates, Shamkant Navathe, Duen Horng Chau",https://www.cc.gatech.edu/~dchau/papers/17-vast-vigor.pdf,"Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR’s ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors.",,,,youtube -jeck0yrT_Q,,2018,2017,VAST,301-C,Tuesday,Graphs and Trees,5:15 PM,5:35 PM
  VAST,TVCG,Visual Diagnosis of Tree Boosting Methods,"Jiannan Xiao, Junlin Liu, Xiting Wang, Jing Wu, Jun Zhu, Shixia Liu",,,,,,,,2018,2017,VAST,102-ABC,Friday,ML3: Classification,8:50 AM,9:10 AM
  VAST,TVCG,Visualizing Big Data Outliers through Distributed Aggregation,Leland Wilkinson,,,,,,,,2018,2017,VAST,301-C,Wednesday,High-dimensional Data,9:30 AM,9:50 AM
  VAST,TVCG,Visualizing Confidence in Cluster-based Ensemble Weather Forecast Analyses,"Alexander Kumpf, Bianca Tost, Marlene Baumgart, Michael Riemer, Rüdiger Westermann, Marc Rautenhaus",https://wwwcg.in.tum.de/fileadmin/user_upload/Lehrstuehle/Lehrstuhl_XV/Research/Publications/2017/Cluster_Confidence/kumpf_etal_clustering_2017.pdf,"In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we –a team of visualization scientists and meteorologists– deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of “Tropical Cyclone Karl”, guiding the user towards the cluster robustness information required for subsequent ensemble analysis",,,,vimeo 230830865,,2018,2017,VAST,301-C,Thursday,ML2: Cluster Analysis,4:15 PM,4:35 PM
  VAST,TVCG,Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow,"Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, Martin Wattenberg",,,,,,,,2018,2017,Other,301-CD,Tuesday,VIS Awards & Best Papers,9:30 AM,9:50 AM
  VAST,TVCG,Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data,"Nan Cao, Chaoguang Lin, Qiuhan Zhu, Yu-Ru Lin, Xian Teng, Xidao Wen",,,,,,,,2018,2017,VAST,301-C,Tuesday,"Space, Time, Movement",2:20 PM,2:40 PM
  VAST,VAST,A Visual Analytics System for Optimizing Communications in Massively Parallel Applications,"Takanori Fujiwara, Preeti Malakar, Khairi Reda, Venkatram Vishwanath, Michael Papka, Kwan-Liu Ma",,,,,,,,2017,2017,VAST,301-C,Thursday,Visual Representation and Design Study,8:50 AM,9:10 AM
  VAST,VAST,A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations,"Josua Krause, Aritra Dasgupta, Jordan Swartz, Yindalon Aphinyanaphongs, Enrico Bertini",https://arxiv.org/pdf/1705.01968.pdf,"Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverages “instance-level explanations”, measures of local feature relevance that explain single instances, and uses them to build a set of visual representations that guide the users in their investigation. The workflow is based on three main visual representations and steps: one based on aggregate statistics to see how data distributes across correct / incorrect decisions; one based on explanations to understand which features are used to make these decisions; and one based on raw data, to derive insights on potential root causes for the observed patterns. The workflow is derived from a long-term collaboration with a group of machine learning and healthcare professionals who used our method to make sense of machine learning models they developed. The case study from this collaboration demonstrates that the proposed workflow helps experts derive useful knowledge about the model and the phenomena it describes, thus experts can generate useful hypotheses on how a model can be improved.",,,,vimeo 230831026,,2017,2017,VAST,102-ABC,Friday,ML3: Classification,9:10 AM,9:30 AM
  VAST,VAST,CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization,"Haeyong Chung, Sai Prashanth Dasari, Santhosh Nandhakumar, Christopher Andrews",,,,,,,,2017,2017,VAST,207,Thursday,Sensemaking,5:15 PM,5:35 PM
  VAST,VAST,CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data,"Isaac Cho, Ryan Wesslen, Svitlana Volkova, Bill Ribarsky, Wenwen Dou",,,,,,,,2017,2017,VAST,301-C,Wednesday,Sequences and Events,2:40 PM,3:00 PM
  VAST,VAST,E-Map: A Visual Analytics Approach for Exploring Significant Event Evolutions in Social Media,"Siming Chen, Shuai Chen, Lijing Lin, Xiaoru Yuan, Jie Liang, Xiaolong (Luke) Zhang",,,,,,,,2017,2017,VAST,301-C,Wednesday,Sequences and Events,3:00 PM,3:20 PM
  VAST,VAST,Interactive Visual Alignment of Medieval Text Versions,"Stefan Jänicke, David Wrisley",,,,,,,,2017,2017,VAST,301-C,Thursday,Interaction in the Analysis Process,3:20 PM,3:40 PM
  VAST,VAST,Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces,"Dominik Jäckle, Michael Hund, Michael Behrisch, Daniel A. Keim, Tobias Schreck",https://dominikjaeckle.com/projects/2017/pattern_trails/paper/2017_jaeckle_pattern_trails.pdf,"Subspace analysis methods have gained interest for identifying patterns in subspaces of high-dimensional data. Existing techniques allow to visualize and compare patterns in subspaces. However, many subspace analysis methods produce an abundant amount of patterns, which often remain redundant and are difficult to relate. Creating effective layouts for comparison of subspace patterns remains challenging. We introduce Pattern Trails, a novel approach for visually ordering and comparing subspace patterns. Central to our approach is the notion of pattern transitions as an interpretable structure imposed to order and compare patterns between subspaces. The basic idea is to visualize projections of subspaces side-by-side, and indicate changes between adjacent patterns in the subspaces by a linked representation, hence introducing pattern transitions. Our contributions comprise a systematization for how pairs of subspace patterns can be compared, and how changes can be interpreted in terms of pattern transitions. We also contribute a technique for visual subspace analysis based on a data-driven similarity measure between subspace representations. This measure is useful to order the patterns, and interactively group subspaces to reduce redundancy. We demonstrate the usefulness of our approach by application to several use cases, indicating that data can be meaningfully ordered and interpreted in terms of pattern transitions.",,,,vimeo 230829949,,2017,2017,VAST,301-C,Wednesday,High-dimensional Data,8:50 AM,9:10 AM
  
negative.code: InfoVis,TVCG,Nonlinear Dot Plots,"Nils Rodrigues, Daniel Weiskopf",,,,,,,,2018,2017,InfoVis,301-D,Tuesday,Techniques,2:40 PM,3:00 PM
  InfoVis,TVCG,Open vs Closed Shapes: New Perceptual Categories?,"David Burlinson, Kalpathi Subramanian, Paula Goolkasian",,,,,,,,2018,2017,InfoVis,301-D,Wednesday,Perception,8:50 AM,9:10 AM
  InfoVis,TVCG,Orko: Facilitating Multimodal Interaction for Visual Network Exploration and Analysis,"Arjun Srinivasan, John Stasko",http://arjun010.github.io/static/papers/orko-infovis-17.pdf,"Data visualization systems have predominantly been developed for WIMP-based direct manipulation interfaces. Only recently have other forms of interaction begun to appear, such as natural language or touch-based interaction, though usually operating only independently. Prior evaluations of natural language interfaces for visualization have indicated potential value in combining direct manipulation and natural language as complementary interaction techniques. We hypothesize that truly multimodal interfaces for visualization, those providing users with freedom of expression via both natural language and touch-based direct manipulation input, may provide an effective and engaging user experience. Unfortunately, however, little work has been done in exploring such multimodal visualization interfaces. To address this gap, we have created an architecture and a prototype visualization system called Orko that facilitates both natural language and direct manipulation input. Specifically, Orko focuses on the domain of network visualization, one that has largely relied on WIMP-based interfaces and direct manipulation interaction, and has little or no prior research exploring natural language interaction. We report results from an initial evaluation study of Orko, and use our observations to discuss opportunities and challenges for future work in multimodal network visualization interfaces.",,,,vimeo 230840776,,2018,2017,InfoVis,301-D,Thursday,Graphs and Paths,5:15 PM,5:35 PM
  InfoVis,TVCG,Priming and Anchoring Effects in Visualizations,"André Calero Valdez, Martina Ziefle, Michael Sedlmair",http://homepage.univie.ac.at/michael.sedlmair/papers/calero-valdez2017priming.pdf,"We investigate priming and anchoring effects on perceptual tasks in visualization. Priming or anchoring effects depict the phenomena that a stimulus might influence subsequent human judgments on a perceptual level, or on a cognitive level by providing a frame of reference. Using visual class separability in scatterplots as an example task, we performed a set of five studies to investigate the potential existence of priming and anchoring effects. Our findings show that—under certain circumstances—such effects indeed exist. In other words, humans judge class separability of the same scatterplot differently depending on the scatterplot(s) they have seen before. These findings inform future work on better understanding and more accurately modeling human perception of visual patterns.",,,,youtube ynD_mbygJdU,,2018,2017,InfoVis,301-D,Wednesday,Perception,9:30 AM,9:50 AM
  InfoVis,TVCG,Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization,"Yunhai Wang, Yanyan Wang, Yingqi Sun, Lifeng Zhu, Chi-Wing Fu, Michael Sedlmair, Oliver Deussen, Baoquan Chen, Kecheng Lu",http://graphics.uni-konstanz.de/publikationen/Wang2018RevisitingStressMajorization/constrained-graph.pdf,"We present an improved stress majorization method that incorporates various constraints, including directional constraints without the necessity of solving a constraint optimization problem. This is achieved by reformulating the stress function to impose constraints on both the edge vectors and lengths instead of just on the edge lengths (node distances). This is a unified framework for both constrained and unconstrained graph visualizations, where we can model most existing layout constraints, as well as develop new ones such as the star shapes and cluster separation constraints within stress majorization. This improvement also allows us to parallelize computation with an efficient GPU conjugant gradient solver, which yields fast and stable solutions, even for large graphs. As a result, we allow the constraint-based exploration of large graphs with 10K nodes – an approach which previous methods cannot support.",,,,vimeo 230840913,,2018,2017,InfoVis,301-D,Thursday,Graphs and Paths,4:35 PM,4:55 PM
  InfoVis,TVCG,"Scatterplots: Tasks, Data, and Designs","Alper Sarikaya, Michael Gleicher",http://graphics.cs.wisc.edu/Papers/2018/SG18/scatterplots-preprint.pdf,"Traditional scatterplots fail to scale as the complexity and amount of data increases. In response, there exist many design options that modify or expand the traditional scatterplot design to meet these larger scales. This breadth of design options creates challenges for designers and practitioners who must select appropriate designs for particular analysis goals. In this paper, we help designers in making design choices for scatterplot visualizations. We survey the literature to catalog scatterplot-specific analysis tasks. We look at how data characteristics influence design decisions. We then survey scatterplot-like designs to understand the range of design options. Building upon these three organizations, we connect data characteristics, analysis tasks, and design choices in order to generate challenges, open questions, and example best practices for the effective design of scatterplots",,,,vimeo 230841068,,2018,2017,InfoVis,301-D,Wednesday,Design,10:30 AM,10:50 AM
  InfoVis,TVCG,Skeleton-based Scagnostics,"José Matute, Alex Telea, Lars Linsen",http://www.cs.rug.nl/~alext/PAPERS/InfoVis17/paper2.pdf,"Scatterplot matrices (SPLOMs) are widely used for exploring multidimensional data. Scatterplot diagnostics (scagnostics) approaches measure characteristics of scatterplots to automatically find potentially interesting plots, thereby making SPLOMs more scalable with the dimension count. While statistical measures such as regression lines can capture orientation, and graph-theoretic scagnostics measures can capture shape, there is no scatterplot characterization measure that uses both descriptors. Based on well-known results in shape analysis, we propose a scagnostics approach that captures both scatterplot shape and orientation using skeletons (or medial axes). Our representation can handle complex spatial distributions, helps discovery of principal trends in a multiscale way, scales visually well with the number of samples, is robust to noise, and is automatic and fast to compute. We define skeleton-based similarity metrics for the visual exploration and analysis of SPLOMs. We perform a user study to measure the human perception of scatterplot similarity and compare the outcome to our results as well as to graph-based scagnostics and other visual quality metrics. Our skeleton-based metrics outperform previously defined measures both in terms of closeness to perceptually-based similarity and computation time efficiency.",,,,vimeo 230841215,,2018,2017,InfoVis,301-D,Wednesday,Multidimensional Data,2:20 PM,2:40 PM
  