anchor.diff:  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,Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs,"Jian Zhao, Michael Glueck, Petra Isenberg, Fanny Chevalier, Azam Khan","During asynchronous collaborative analysis, handoff of partial findings is challenging because externalizations produced by analysts may not adequately communicate their investigative process. To address this challenge, we developed techniques to automatically capture and help encode tacit aspects of the investigative process based on an analyst’s interactions, and streamline explicit authoring of handoff annotations. We designed our techniques to mediate awareness of analysis coverage, support explicit communication of progress and uncertainty with annotation, and implicit communication through playback of investigation histories. To evaluate our techniques, we developed an interactive visual analysis system, KTGraph, that supports an asynchronous investigative document analysis task. We conducted a two-phase user study to characterize a set of handoff strategies and to compare investigative performance with and without our techniques. The results suggest that our techniques promote the use of more effective handoff strategies, help increase an awareness of prior investigative process and insights, as well as improve final investigative outcomes.",,,,,youtube 5iEyczTOvxQ,,2018,2017,VAST,207,Thursday,Sensemaking,4:55 PM,5:15 PM
  +VAST,TVCG,The Interactive Visualization Gap in Initial Exploratory Data Analysis,"Andrea Batch, Niklas Elmqvist",,,,,,,10.1109/TVCG.2017.2743990,2018,2017,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",https://infovis.cs.vt.edu/sites/default/files/systematic-combination-dimension.pdf,"Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms.",,,,vimeo 230830954,,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,Visual Diagnosis of Tree Boosting Methods,"Jiannan Xiao, Junlin Liu, Xiting Wang, Jing Wu, Jun Zhu, Shixia Liu",http://shixialiu.com/publications/boostvis/paper.pdf,"Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms.",http://www.shixialiu.com:8082/static/index.html,,,video http://shixialiu.com/publications/boostvis/video.mp4,,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,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",https://idl.cs.washington.edu/files/2018-TensorFlowGraph-VAST.pdf,"We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model’s modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.",,,,vimeo 232930758,,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 Visual Analytics System for Optimizing Communications in Massively Parallel Applications,"Takanori Fujiwara, Preeti Malakar, Khairi Reda, Venkatram Vishwanath, Michael Papka, Kwan-Liu Ma",,,,,,youtube RzXfxXjAoYk,,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,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",,,,,,vimeo 230830262,,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
  
positive.code: VAST,TVCG,EVA: Visual Analytics to Identify Fraudulent Events,"Roger Leite, Theresia Gschwandtner, Silvia Miksch, Simone Kriglstein, Margit Pohl, Erich Gstrein, Johannes Kuntner",,,,,,,,2018,2017,VAST,207,Thursday,Sensemaking,4:35 PM,4:55 PM
  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",,,,,,,,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,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
  
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
  