anchor.diff:  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
  -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,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.",,,,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",,"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,10.1109/TVCG.2017.2745279,2018,2017,VAST,207,Thursday,Sensemaking,4:55 PM,5:15 PM
  
positive.code: 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",,"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
  
negative.code: SciVis,TVCG,The Topology ToolKit,"Julien Tierny, Guillaume Favelier, Joshua Levine, Charles Gueunet, Michaël Michaux",https://hal.archives-ouvertes.fr/hal-01499905/document,"This system paper presents the Topology ToolKit (TTK), a software platform designed for the topological analysis of scalar data in scientific visualization. While topological data analysis has gained in popularity over the last two decades, it has not yet been widely adopted as a standard data analysis tool for end users or developers. TTK aims at addressing this problem by providing a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependency-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code, online documentation and video tutorials are available on TTK’s website [108].",https://topology-tool-kit.github.io/,https://github.com/topology-tool-kit/ttk,,youtube Hn8w0V-jHgw,,2018,2017,SciVis,207,Wednesday,Topology-based methods,2:40 PM,3:00 PM
  SciVis,TVCG,TopoAngler: Interactive Topology-based Extraction of Fishes,"Alexander Bock, Harish Doraiswamy, Adam Summers, Claudio Silva",https://alexanderbock.github.io/papers/vis17-bock-topoangler.pdf,"We present TopoAngler, a visualization framework that enables an interactive user-guided segmentation of fishes contained in a micro-CT scan. The inherent noise in the CT scan coupled with the often disconnected (and sometimes broken) skeletal structure of fishes makes an automatic segmentation of the volume impractical. To overcome this, our framework combines techniques from computational topology with an interactive visual interface, enabling the human-in-the-loop to effectively extract fishes from the volume. In the first step, the join tree of the input is used to create a hierarchical segmentation of the volume. Through the use of linked views, the visual interface then allows users to interactively explore this hierarchy, and gather parts of individual fishes into a coherent sub-volume, thus reconstructing entire fishes. Our framework was primarily developed for its application to CT scans of fishes, generated as part of the ScanAllFish project, through close collaboration with their lead scientist. However, we expect it to also be applicable in other biological applications where a single data set contains multiple specimen; a common routine that is now widely followed in laboratories to increase throughput of expensive CT scanners.",,,,youtube gz22DjMRJjk,,2018,2017,SciVis,207,Wednesday,Topology-based methods,2:00 PM,2:20 PM
  SciVis,TVCG,Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models,"Subhashis Hazarika, Ayan Biswas, Han-Wei Shen",,,,,,,,2018,2017,SciVis,207,Tuesday,"Mix: Foundations, uncertainty, particles",2:40 PM,3:00 PM
  SciVis,TVCG,Visualization Multi-Pipeline for Communicating Biology,"Peter Mindek, David Kouřil, Johannes Sorger, Daniel Toloudis, Blair Lyons, Graham Johnson, M. Eduard Gröller, Ivan Viola",https://www.cg.tuwien.ac.at/research/publications/2017/mindek-2017-marion/mindek-2017-marion-Paper.pdf,"We propose a system to facilitate biology communication by developing a pipeline to support the instructional visualization of heterogeneous biological data on heterogeneous user-devices. Discoveries and concepts in biology are typically summarized with illustrations assembled manually from the interpretation and application of heterogenous data. The creation of such illustrations is time consuming, which makes it incompatible with frequent updates to the measured data as new discoveries are made. Illustrations are typically non-interactive, and when an illustration is updated, it still has to reach the user. Our system is designed to overcome these three obstacles. It supports the integration of heterogeneous datasets, reflecting the knowledge that is gained from different data sources in biology. After pre-processing the datasets, the system transforms them into visual representations as inspired by scientific illustrations. As opposed to traditional scientific illustration these representations are generated in real-time - they are interactive. The code generating the visualizations can be embedded in various software environments. To demonstrate this, we implemented both a desktop application and a remote-rendering server in which the pipeline is embedded. The remote-rendering server supports multi-threaded rendering and it is able to handle multiple users simultaneously. This scalability to different hardware environments, including multi-GPU setups, makes our system useful for efficient public dissemination of biological discoveries.",,,,video https://www.cg.tuwien.ac.at/research/publications/2017/mindek-2017-marion/mindek-2017-marion-Preview.avi,,2018,2017,SciVis,207,Thursday,Visualization in Biology and Medicine,9:30 AM,9:50 AM
  SciVis,TVCG,Decision Graph Embedding for High-Resolution Manometry Diagnosis,"Julian Kreiser, Alexander Hann, Eugen Zizer, Timo Ropinski",,,,,,,,2018,2017,SciVis,207,Thursday,Visualization in Biology and Medicine,9:10 AM,9:30 AM