anchor.diff:  TVCG,TVCG,Robust Tracing and Visualization of Heterogeneous Microvascular Networks,T),"Pavel Govyadinov, Tasha Womack, Jason L. Eriksen, Guoning Chen, David Mayerich",,,,,,,vimeo 289789196,10.1109/tvcg.2018.2818701,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Biological Applications,23-Oct,5:20 PM,5:40 PM
   VAST,TVCG,Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities,J),"Alexander Kumpf, Marc Rautenhaus, Michael Riemer, Rüdiger Westermann",,,,,,,vimeo 289787833,10.1109/tvcg.2018.2864901,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,9:00 AM,9:20 AM
   VAST,TVCG,EnsembleLens: Ensemble-based Visual Exploration of Anomaly Detection Algorithms with Multidimensional Data,J),"Ke Xu, Meng Xia, Xing Mu, Yun Wang, Nan Cao",https://lukeluker.github.io/papers/EnsembleLens_VAST2018_KeXu.pdf,"The results of anomaly detection are sensitive to the choice of detection algorithms as they are specialized for different properties of data, especially for multidimensional data. Thus, it is vital to select the algorithm appropriately. To systematically select the algorithms, ensemble analysis techniques have been developed to support the assembly and comparison of heterogeneous algorithms. However, challenges remain due to the absence of the ground truth, interpretation, or evaluation of these anomaly detectors. In this paper, we present a visual analytics system named EnsembleLens that evaluates anomaly detection algorithms based on the ensemble analysis process. The system visualizes the ensemble processes and results by a set of novel visual designs and multiple coordinated contextual views to meet the requirements of correlation analysis, assessment and reasoning of anomaly detection algorithms. We also introduce an interactive analysis workflow that dynamically produces contextualized and interpretable data summaries that allow further refinements of exploration results based on user feedback. We demonstrate the effectiveness of EnsembleLens through a quantitative evaluation, three case studies with real-world data and interviews with two domain experts.",,,,,vimeo 289787339,10.1109/TVCG.2018.2864825,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,9:20 AM,9:40 AM
  -TVCG,TVCG,Exploring Variability within Ensembles of Decadal Climate Predictions,T),"Christopher P. Kappe, Michael Böttinger, Heike Leitte",,,,,,,vimeo 289789327,10.1109/tvcg.2018.2810919,,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,9:40 AM,10:00 AM
  +TVCG,TVCG,Exploring Variability within Ensembles of Decadal Climate Predictions,T),"Christopher P. Kappe, Michael Böttinger, Heike Leitte",,,,,,,vimeo 289789327,10.1109/tvcg.2018.2810919,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,9:40 AM,10:00 AM
   VAST,TVCG,KnowledgePearls: Provenance-Based Visualization Retrieval,J),"Holger Stitz, Samuel Gratzl, Harald Piringer, Thomas Zichner, Marc Streit",https://www.vrvis.at/publications/pdfs/PB-VRVis-2018-031.pdf,"Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.",,https://github.com/Caleydo/knowledge-pearls,,,vimeo 289787528,10.1109/tvcg.2018.2865024,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,10:00 AM,10:20 AM
   VAST,TVCG,Enhancing Web-based Analytics Applications through Provenance,J),"Akhilesh Camisetty, Chaitanya Chandurkar, Maoyuan Sun, David Koop",http://www.cis.umassd.edu/~dkoop/pubs/simprov.pdf,"Visual analytics systems continue to integrate new technologies and leverage modern environments for exploration and collaboration, making tools and techniques available to a wide audience through web browsers. Many of these systems have been developed with rich interactions, offering users the opportunity to examine details and explore hypotheses that have not been directly encoded by a designer. Understanding is enhanced when users can replay and revisit the steps in the sensemaking process, and in collaborative settings, it is especially important to be able to review not only the current state but also what decisions were made along the way. Unfortunately, many web-based systems lack the ability to capture such reasoning, and the path to a result is transient, forgotten when a user moves to a new view. This paper explores the requirements to augment existing client-side web applications with support for capturing, reviewing, sharing, and reusing steps in the reasoning process. Furthermore, it considers situations where decisions are made with streaming data, and the insights gained from revisiting those choices when more data is available. It presents a proof of concept, the Shareable Interactive Manipulation Provenance framework (SIMProv.js), that addresses these requirements in a modern, client-side JavaScript library, and describes how it can be integrated with existing frameworks.",,,,,vimeo 289787554,10.1109/tvcg.2018.2865039,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,10:20 AM,10:40 AM
   InfoVis,TVCG,Comparing Similarity Perception in Time Series Visualizations,J),"Anna Gogolou, Theophanis Tsandilas, Themis Palpanas, Anastasia Bezerianos",https://hal.inria.fr/hal-01845008/document,"A common challenge faced by many domain experts working with time series data is how to identify and compare similar patterns. This operation is fundamental in high-level tasks, such as detecting recurring phenomena or creating clusters of similar temporal sequences. While automatic measures exist to compute time series similarity, human intervention is often required to visually inspect these automatically generated results. The visualization literature has examined similarity perception and its relation to automatic similarity measures for line charts, but has not yet considered if alternative visual representations, such as horizon graphs and colorfields, alter this perception. Motivated by how neuroscientists evaluate epileptiform patterns, we conducted two experiments that study how these three visualization techniques affect similarity perception in EEG signals. We seek to understand if the time series results returned from automatic similarity measures are perceived in a similar manner, irrespective of the visualization technique; and if what people perceive as similar with each visualization aligns with different automatic measures and their similarity constraints. Our findings indicate that horizon graphs align with similarity measures that allow local variations in temporal position or speed (i.e., dynamic time warping) more than the two other techniques. On the other hand, horizon graphs do not align with measures that are insensitive to amplitude and y-offset scaling (i.e., measures based on z-normalization), but the inverse seems to be the case for line charts and colorfields. Overall, our work indicates that the choice of visualization affects what temporal patterns we consider as similar, i.e., the notion of similarity in time series is not visualization independent.",,,,,vimeo 289785077,10.1109/tvcg.2018.2865077,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,9:00 AM,9:20 AM
  -TVCG,TVCG,A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series,T),"Erick Cuenca, Arnaud Sallaberry, Florence Ying Wang, Pascal Poncelet",http://www.lirmm.fr/~poncelet/publications/papers/Multistream2018.pdf,"Multiple time series are a set of multiple quantitative variables occurring at the same interval. They are present in many domains such as medicine, finance, and manufacturing for analytical purposes. In recent years, streamgraph visualization (evolved from ThemeRiver) has been widely used for representing temporal evolution patterns in multiple time series. However, streamgraph as well as ThemeRiver suffer from scalability problems when dealing with several time series. To solve this problem, multiple time series can be organized into a hierarchical structure where individual time series are grouped hierarchically according to their proximity. In this paper, we present a new streamgraph-based approach to convey the hierarchical structure of multiple time series to facilitate the exploration and comparisons of temporal evolution. Based on a focus+context technique, our method allows time series exploration at different granularities (e. g., from overview to details). To illustrate our approach, two usage examples are presented.",http://advanse.lirmm.fr/multistream/,https://github.com/erickedu85/multistream,,,vimeo 289789365,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,9:20 AM,9:40 AM
  +TVCG,TVCG,A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series,T),"Erick Cuenca, Arnaud Sallaberry, Florence Ying Wang, Pascal Poncelet",http://www.lirmm.fr/~poncelet/publications/papers/Multistream2018.pdf,"Multiple time series are a set of multiple quantitative variables occurring at the same interval. They are present in many domains such as medicine, finance, and manufacturing for analytical purposes. In recent years, streamgraph visualization (evolved from ThemeRiver) has been widely used for representing temporal evolution patterns in multiple time series. However, streamgraph as well as ThemeRiver suffer from scalability problems when dealing with several time series. To solve this problem, multiple time series can be organized into a hierarchical structure where individual time series are grouped hierarchically according to their proximity. In this paper, we present a new streamgraph-based approach to convey the hierarchical structure of multiple time series to facilitate the exploration and comparisons of temporal evolution. Based on a focus+context technique, our method allows time series exploration at different granularities (e. g., from overview to details). To illustrate our approach, two usage examples are presented.",http://advanse.lirmm.fr/multistream/,https://github.com/erickedu85/multistream,,,vimeo 289789365,10.1109/TVCG.2018.2796591,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,9:20 AM,9:40 AM
   TVCG,TVCG,Line Graph or Scatter Plot? Automatic Selection of Methods for Visualizing Trends in Time Series,T),"Yunhai Wang, Fubo Han, Lifeng Zhu, Oliver Deussen, Baoquan Chen",http://www.yunhaiwang.org/vis-selection/timeseries.pdf,"Line graphs are usually considered to be the best choice for visualizing time series data, whereas sometimes also scatter plots are used for showing main trends. So far there are no guidelines that indicate which of these visualization methods better display trends in time series for a given canvas. Assuming that the main information in a time series is its overall trend, we propose an algorithm that automatically picks the visualization method that reveals this trend best. This is achieved by measuring the visual consistency between the trend curve represented by a LOESS fit and the trend described by a scatter plot or a line graph. To measure the consistency between our algorithm and user choices, we performed an empirical study with a series of controlled experiments that show a large correspondence. In a factor analysis we furthermore demonstrate that various visual and data factors have effects on the preference for a certain type of visualization.",,,,,vimeo 289788836,10.1109/tvcg.2017.2653106,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,9:40 AM,10:00 AM
   TVCG,TVCG,A Vector Field Design Approach to Animated Transitions,T),"Yong Wang, Daniel Archambault, Carlos E. Scheidegger, Huamin Qu",http://www.cs.swansea.ac.uk/~csdarchambault/publications/animatedTransitionFinalSubVersion.pdf,"Animated transitions can be effective in explaining and exploring a small number of visualizations where there are drastic changes in the scene over a short interval of time. This is especially true if data elements cannot be visually distinguished by other means. Current research in animated transitions has mainly focused on linear transitions (all elements follow straight line paths) or enhancing coordinated motion through bundling of linear trajectories. In this paper, we introduce animated transition design, a technique to build smooth, non-linear transitions for clustered data with either minimal or no user involvement. The technique is flexible and simple to implement, and has the additional advantage that it explicitly enhances coordinated motion and can avoid crowding, which are both important factors to support object tracking in a scene. We investigate its usability, provide preliminary evidence for the effectiveness of this technique through metric evaluations and user study and discuss limitations and future directions.",,,,,vimeo 289789555,10.1109/tvcg.2017.2750689,2017,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,10:00 AM,10:20 AM
  -InfoVis,TVCG,Temporal Treemaps: Static Visualization of Evolving Trees,J),"Wiebke Köpp, Tino Weinkauf",http://www.csc.kth.se/~weinkauf/publications/documents/koepp19a.pdf,"We consider temporally evolving trees with changing topology and data: tree nodes may persist for a time range, merge or split, and the associated data may change. Essentially, one can think of this as a time series of trees with a node correspondence per hierarchy level between consecutive time steps. Existing visualization approaches for such data include animated 2D treemaps, where the dynamically changing layout makes it difficult to observe the data in its entirety. We present a method to visualize this dynamic data in a static, nested, and space-filling visualization. This is based on two major contributions: First, the layout constitutes a graph drawing problem. We approach it for the entire time span at once using a combination of a heuristic and simulated annealing. Second, we propose a rendering that emphasizes the hierarchy through an adaption of the classic cushion treemaps. We showcase the wide range of applicability using data from feature tracking in time-dependent scalar fields, evolution of file system hierarchies, and world population.",http://www.csc.kth.se/~weinkauf/publications/documents/koepp19a_algorithm.mp4,,,,vimeo 289784452,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,10:20 AM,10:40 AM
  +InfoVis,TVCG,Temporal Treemaps: Static Visualization of Evolving Trees,J),"Wiebke Köpp, Tino Weinkauf",http://www.csc.kth.se/~weinkauf/publications/documents/koepp19a.pdf,"We consider temporally evolving trees with changing topology and data: tree nodes may persist for a time range, merge or split, and the associated data may change. Essentially, one can think of this as a time series of trees with a node correspondence per hierarchy level between consecutive time steps. Existing visualization approaches for such data include animated 2D treemaps, where the dynamically changing layout makes it difficult to observe the data in its entirety. We present a method to visualize this dynamic data in a static, nested, and space-filling visualization. This is based on two major contributions: First, the layout constitutes a graph drawing problem. We approach it for the entire time span at once using a combination of a heuristic and simulated annealing. Second, we propose a rendering that emphasizes the hierarchy through an adaption of the classic cushion treemaps. We showcase the wide range of applicability using data from feature tracking in time-dependent scalar fields, evolution of file system hierarchies, and world population.",http://www.csc.kth.se/~weinkauf/publications/documents/koepp19a_algorithm.mp4,,,,vimeo 289784452,10.1109/TVCG.2018.2865265,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,10:20 AM,10:40 AM
   SciVis,TVCG,Visual Analysis of Aneurysm Data using Statistical Graphics,J),"Monique Meuschke, Tobias Günther, Philipp Berg, Ralph Wickenhoefer, Markus Gross, Bernhard Preim, Kai Lawonn",,,,,,,vimeo 290325604,10.1109/tvcg.2018.2864509,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:00 AM,9:20 AM
  -SciVis,TVCG,Interactive Visualization of 3D Histopathology in Native Resolution,J),"Martin Falk, Anders Ynnerman, Darren Treanor, Claes Lundström",,,,,,,vimeo 290325921,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:20 AM,9:40 AM
  -SciVis,TVCG,Visualization of Neuronal Structures in Wide-field Microscopy Brain Images,J),"Saeed Boorboor, Shreeraj Jadhav, Mala Ananth, David Talmage, Lorna W Role, Arie Kaufman",,,,,,,vimeo 290328215,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:40 AM,10:00 AM
  -TVCG,TVCG,Classification of Blood Flow Patterns in Cerebral Aneurysms,T),"Monique Meuschke, Steffen Oeltze-Jafra, Oliver Beuing, Bernhard Preim, Kai Lawonn",,,,,,,vimeo 289789890,10.1109/tvcg.2018.2834923,,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,10:00 AM,10:20 AM
  -TVCG,TVCG,Bridging Text Visualization and Mining: A Task-Driven Survey,T),"Shixia Liu, Xiting Wang, Christopher Collins, Wenwen Dou, Fangxin Ouyang, Mennatallah El-Assady, Liu Jiang, Daniel Keim",,,,,,,vimeo 289789579,10.1109/tvcg.2018.2834341,,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:00 AM,9:20 AM
  +SciVis,TVCG,Interactive Visualization of 3D Histopathology in Native Resolution,J),"Martin Falk, Anders Ynnerman, Darren Treanor, Claes Lundström",,,,,,,vimeo 290325921,10.1109/TVCG.2018.2864816,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:20 AM,9:40 AM
  +SciVis,TVCG,Visualization of Neuronal Structures in Wide-field Microscopy Brain Images,J),"Saeed Boorboor, Shreeraj Jadhav, Mala Ananth, David Talmage, Lorna W Role, Arie Kaufman",,,,,,,vimeo 290328215,10.1109/TVCG.2018.2864852,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:40 AM,10:00 AM
  +TVCG,TVCG,Classification of Blood Flow Patterns in Cerebral Aneurysms,T),"Monique Meuschke, Steffen Oeltze-Jafra, Oliver Beuing, Bernhard Preim, Kai Lawonn",,,,,,,vimeo 289789890,10.1109/tvcg.2018.2834923,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,10:00 AM,10:20 AM
  +TVCG,TVCG,Bridging Text Visualization and Mining: A Task-Driven Survey,T),"Shixia Liu, Xiting Wang, Christopher Collins, Wenwen Dou, Fangxin Ouyang, Mennatallah El-Assady, Liu Jiang, Daniel Keim",,,,,,,vimeo 289789579,10.1109/tvcg.2018.2834341,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:00 AM,9:20 AM
   VAST,TVCG,Doccurate: A Curation-Based Approach for Clinical Text Visualization,J),Nicole Sultanum,,,,,,,vimeo 289787431,10.1109/TVCG.2018.2864905,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:20 AM,9:40 AM
   VAST,TVCG,VIS Author Profiles: Interactive Descriptions of Publication Records Combining Text and Visualization,J),"Shahid Latif, Fabian Beck, Devin Singh, Michael Brudno, Fanny Chevalier",https://www.vis.wiwi.uni-due.de/uploads/tx_itochairt3/publications/vap-preprint.pdf,"Publication records and collaboration networks are important for assessing the expertise and experience of researchers. Existing digital libraries show the raw publication lists in author profiles, whereas visualization techniques focus on specific subproblems. Instead, we look at publication records from various perspectives mixing low-level publication data with high-level abstractions and background information. This work presents VIS Author Profiles, a novel approach to generate integrated textual and visual descriptions to highlight patterns in publication records. We leverage template-based natural language generation to summarize notable publication statistics, evolution of research topics, and collaboration relationships. Seamlessly integrated visualizations augment the textual description and are interactively connected with each other and the text. The underlying publication data and detailed explanations of the analysis are available on demand. We compare our approach to existing systems by taking into account information needs of users and demonstrate its usefulness in two realistic application examples.",https://vis-tools.paluno.uni-due.de/vap/,,,,vimeo 289787504,10.1109/TVCG.2018.2865022,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:40 AM,10:00 AM
  -TVCG,TVCG,A Visual Analytics Framework for Identifying Topic Drivers in Media,T),"Yafeng Lu, Hong Wang, Steven Landis, Ross Maciejewski",,,,,,,vimeo 289788885,,,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,10:00 AM,10:20 AM
  +TVCG,TVCG,A Visual Analytics Framework for Identifying Topic Drivers in Media,T),"Yafeng Lu, Hong Wang, Steven Landis, Ross Maciejewski",,,,,,,vimeo 289788885,10.1109/TVCG.2017.2752166,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,10:00 AM,10:20 AM
   TVCG,TVCG,Visualizing a Thinker’s Life,T),"Patrick Riehmann, Dora Kiesel, Martin Kohlhaas, Bernd Fröhlich",,,,,,,vimeo 289789544,,,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,10:20 AM,10:40 AM
  -TVCG,TVCG,Commercial Visual Analytics Systems – Advances in the Big Data Analytics Field,T),"Michael Behrisch, Dirk Streeb, Florian Stoffel, Daniel Seebacher, Stefan Hagen Weber, Sebastian Mittelstaedt, Hanspeter Pfister, Daniel Keim",,,,,,,vimeo 289789498,,,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:00 AM,11:20 AM
  +TVCG,TVCG,Commercial Visual Analytics Systems – Advances in the Big Data Analytics Field,T),"Michael Behrisch, Dirk Streeb, Florian Stoffel, Daniel Seebacher, Stefan Hagen Weber, Sebastian Mittelstaedt, Hanspeter Pfister, Daniel Keim",,,,,,,vimeo 289789498,10.1109/TVCG.2018.2859973,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:00 AM,11:20 AM
   VAST,TVCG,BitExTract: Interactive Visualization for Extracting Bitcoin Exchange Intelligence,J),"Xuanwu Yue, Xinhuan Shu, Xinyu ZHU, Xinnan Du, Zheqing Yu, Dimitrios Papadopoulos, Siyuan Liu",,,,,,,vimeo 289787312,10.1109/TVCG.2018.2864814,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:20 AM,11:40 AM
  -TVCG,TVCG,KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis,T),"Markus Wagner, Djordje Slijepcevic, Brian Horsak, Alexander Rind, Matthias Zeppelzauer, Wolfgang Aigner",,,,,,,vimeo 289788975,10.1109/tvcg.2017.2785271,,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:40 AM,12:00 PM
  -TVCG,TVCG,Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots,T),"G.Elisabeta Marai, Chihua Ma, Andrew T. Burks, Filippo Pellolio, Guadalupe Canahuate, David M. Vock, Abdallah S.R. Mohamed, C. David Fuller",,,,,,,vimeo 289789079,10.1109/tvcg.2018.2817557,,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,12:00 PM,12:20 PM
  +TVCG,TVCG,KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis,T),"Markus Wagner, Djordje Slijepcevic, Brian Horsak, Alexander Rind, Matthias Zeppelzauer, Wolfgang Aigner",,,,,,,vimeo 289788975,10.1109/tvcg.2017.2785271,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:40 AM,12:00 PM
  +TVCG,TVCG,Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots,T),"G.Elisabeta Marai, Chihua Ma, Andrew T. Burks, Filippo Pellolio, Guadalupe Canahuate, David M. Vock, Abdallah S.R. Mohamed, C. David Fuller",,,,,,,vimeo 289789079,10.1109/tvcg.2018.2817557,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,12:00 PM,12:20 PM
   VAST,VAST,VUSphere: Visual Analysis of Video Utilization in Online Distance Education,C),"Huan He, Qinghua Zheng, Bo Dong",,,,,,,vimeo 289788052,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,12:20 PM,12:40 PM
  -InfoVis,TVCG,Juniper: A Tree+Table Approach to Multivariate Graph Visualization,J),"Carolina Nobre, Marc Streit, Alexander Lex",http://sci.utah.edu/~vdl/papers/2018_infovis_juniper.pdf,"Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree+table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.",,https://github.com/caleydo/lineage/tree/juniper,,,vimeo 289784894,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:00 AM,11:20 AM
  +InfoVis,TVCG,Juniper: A Tree+Table Approach to Multivariate Graph Visualization,J),"Carolina Nobre, Marc Streit, Alexander Lex",http://sci.utah.edu/~vdl/papers/2018_infovis_juniper.pdf,"Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree+table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.",,https://github.com/caleydo/lineage/tree/juniper,,,vimeo 289784894,10.1109/TVCG.2018.2865149,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:00 AM,11:20 AM
   TVCG,TVCG,Graph Thumbnails: Identifying and Comparing Multiple Graphs at a Glance,T),"Vahan Yoghourdjian, Tim Dwyer, Karsten Klein, Kim Marriott, Michael Wybrow",http://vahany.com/docs/Graph_Thumbnails_preprint.pdf,"We propose <i>Graph Thumbnails</i>, small icon-like visualisations of the high-level structure of network data. Graph Thumbnails are designed to be legible in small multiples to support rapid browsing within large graph corpora. Compared to existing graph-visualisation techniques our representation has several advantages: (1) the visualisation can be computed in linear time; (2) it is canonical in the sense that isomorphic graphs will always have identical thumbnails; and (3) it provides precise information about the graph structure. We report the results of two user studies. The first study compares Graph Thumbnails to node-link and matrix views for identifying similar graphs. The second study investigates the comprehensibility of the different representations. We demonstrate the usefulness of this representation for summarising the evolution of protein-protein interaction networks across a range of species.",,,,,vimeo 289789353,10.1109/tvcg.2018.2790961,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:20 AM,11:40 AM
   InfoVis,TVCG,Structure-Based Suggestive Exploration: A New Approach for Effective Exploration of Large Networks,J),"Fangzhou Guo, Wei Chen, Dongming Han, Jiacheng Pan, Xiaotao Nie, Jiazhi Xia, Xiaolong (Luke) Zhang",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/Structure-Based%20Suggestive%20Exploration.pdf,"When analyzing a visualized network, users need to explore different sections of the network to gain insight. However, effective exploration of large networks is often a challenge. While various tools are available for users to explore the global and local features of a network, these tools usually require significant interaction activities, such as repetitive navigation actions to follow network nodes and edges. In this paper, we propose a structure-based suggestive exploration approach to support effective exploration of large networks by suggesting appropriate structures upon user request. Encoding nodes with vectorized representations by transforming information of surrounding structures of nodes into a high dimensional space, our approach can identify similar structures within a large network, enable user interaction with multiple similar structures simultaneously, and guide the exploration of unexplored structures. We develop a web-based visual exploration system to incorporate this suggestive exploration approach and compare performances of our approach under different vectorizing methods and networks. We also present the usability and effectiveness of our approach through a controlled user study with two datasets.",,,,,vimeo 289785045,10.1109/tvcg.2018.2865139,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:40 AM,12:00 PM
   InfoVis,TVCG,Structure-aware Fisheye Views for Efficient Large Graph Exploration,J),"Yunhai Wang, Yanyan Wang, Yinqi Sun, Haifeng Zhang, Chi-Wing Fu, Michael Sedlmair, Baoquan Chen, Oliver Deussen",https://homepage.univie.ac.at/michael.sedlmair/papers/wang2019fisheye.pdf,"Traditional fisheye views for exploring large graphs introduce substantial distortions that often lead to a decreased readability of paths and other interesting structures. To overcome these problems, we propose a framework for <i>structure-aware</i> fisheye views. Using edge orientations as constraints for graph layout optimization allows us not only to reduce spatial and temporal distortions during fisheye zooms, but also to improve the readability of the graph structure. Furthermore, the framework enables us to optimize fisheye lenses towards specific tasks and design a family of new lenses: polyfocal, cluster, and path lenses. A GPU implementation lets us process large graphs with up to 15,000 nodes at interactive rates. A comprehensive evaluation, a user study, and two case studies demonstrate that our structure-aware fisheye views improve layout readability and user performance.",,,,,vimeo 289785238,10.1109/tvcg.2018.2864911,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,12:00 PM,12:20 PM
   InfoVis,TVCG,"Graphicle: Exploring Units, Networks, and Context in a Blended Visualization Approach ",J),"Timothy Major, Rahul C. Basole",http://entsci.gatech.edu/resources/basole-2019-tvcg-graphicle.pdf,"Many real-world datasets are large, multivariate, and relational in nature and relevant associated decisions frequently require a simultaneous consideration of both attributes and connections. Existing visualization systems and approaches, however, often make an explicit trade-off between either affording rich exploration of individual data units and their attributes or exploration of the underlying network structure. In doing so, important analysis opportunities and insights are potentially missed. In this study, we aim to address this gap by (1) considering visualizations and interaction techniques that blend the spectrum between unit and network visualizations, (2) discussing the nature of different forms of contexts and the challenges in implementing them, and (3) demonstrating the value of our approach for visual exploration of multivariate, relational data for a real-world use case. Specifically, we demonstrate through a system called Graphicle how network structure can be layered on top of unit visualization techniques to create new opportunities for visual exploration of physician characteristics and referral data. We report on the design, implementation, and evaluation of the system and effectiveness of our blended approach.",,,,,vimeo 262664447,10.1109/tvcg.2018.2865151,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,12:20 PM,12:40 PM
  -SciVis,TVCG,Interactive Obstruction-free Lensing for Volumetric Data Visualization,J),"Michael Traoré, Christophe Hurter, Alexandru Telea",,,,,,,vimeo 290325542,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:00 AM,11:20 AM
  +SciVis,TVCG,Interactive Obstruction-free Lensing for Volumetric Data Visualization,J),"Michael Traoré, Christophe Hurter, Alexandru Telea",,,,,,,vimeo 290325542,10.1109/TVCG.2018.2864690,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:00 AM,11:20 AM
   SciVis,TVCG,Dynamic Volume Lines: Visual Comparison of 3D Volumes through Space-filling Curves,J),"Johannes Weissenböck, Bernhard Fröhler, Eduard Gröller, Johann Kastner, Christoph Heinzl",,,,,,,vimeo 290325634,10.1109/tvcg.2018.2864510,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:20 AM,11:40 AM
   SciVis,TVCG,A Declarative Grammar of Flexible Volume Visualization Pipelines,J),"Min Shih, Charles Rozhon, Kwan-Liu Ma",,,,,,,vimeo 290327611,10.1109/tvcg.2018.2864841,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:40 AM,12:00 PM
  -TVCG,TVCG,Multi-Material Volume Rendering with a Physically-Based Surface Reflection Model,T),"Oleg Igouchkine, Yubo Zhang, Kwan-Liu Ma",,,,,,,vimeo 289789120,10.1109/tvcg.2017.2784830,,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:00 PM,12:20 PM
  -TVCG,TVCG,A Generative Model for Volume Rendering,T),"Matthew Berger, Jixian Li, Joshua A. Levine",,,,,,,vimeo 289789468,10.1109/tvcg.2018.2816059,,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:20 PM,12:40 PM
  +TVCG,TVCG,Multi-Material Volume Rendering with a Physically-Based Surface Reflection Model,T),"Oleg Igouchkine, Yubo Zhang, Kwan-Liu Ma",,,,,,,vimeo 289789120,10.1109/tvcg.2017.2784830,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:00 PM,12:20 PM
  +TVCG,TVCG,A Generative Model for Volume Rendering,T),"Matthew Berger, Jixian Li, Joshua A. Levine",,,,,,,vimeo 289789468,10.1109/tvcg.2018.2816059,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:20 PM,12:40 PM
   InfoVis,TVCG,Vistrates: A Component Model for Ubiquitous Analytics,J),"Sriram Karthik Badam, Andreas Mathisen, Roman Rädle, Clemens Nylandsted Klokmose, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/vistrates/vistrates.pdf,"Visualization tools are often specialized for specific tasks, which turns the user's analytical workflow into a fragmented process performed across many tools. In this paper, we present a component model design for data visualization to promote modular designs of visualization tools that enhance their analytical scope. Rather than fragmenting tasks across tools, the component model supports unification, where components�the building blocks of this model�can be assembled to support a wide range of tasks. Furthermore, the model also provides additional key properties, such as support for collaboration, sharing across multiple devices, and adaptive usage depending on expertise, from creating visualizations using dropdown menus, through instantiating components, to actually modifying components or creating entirely new ones from scratch using JavaScript or Python source code. To realize our model, we introduce VISTRATES, a literate computing platform for developing, assembling, and sharing visualization components. From a visualization perspective, Vistrates features cross-cutting components for visual representations, interaction, collaboration, and device responsiveness maintained in a component repository. From a development perspective, Vistrates offers a collaborative programming environment where novices and experts alike can compose component pipelines for specific analytical activities. Finally, we present several Vistrates use cases that span the full range of the classic �anytime� and �anywhere� motto for ubiquitous analysis: from mobile and on-the-go usage, through office settings, to collaborative smart environments covering a variety of tasks and devices.",,https://github.com/karthikbadam/Vistrates,,,vimeo 289784994,10.1109/tvcg.2018.2865144,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:00 AM,11:20 AM
   InfoVis,TVCG,SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays,J),"Hariharan Subramonyam, Eytan Adar",,,,,,,vimeo 289784720,10.1109/tvcg.2018.2865231,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:20 AM,11:40 AM
   InfoVis,TVCG,"Multiple Coordinated Views at Large Displays for Multiple Users: Empirical Findings on User Behavior, Movements, and Distances ",J),"Ricardo Langner, Ulrike Kister, Raimund Dachselt",,,,,,,vimeo 289784543,10.1109/tvcg.2018.2865235,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:40 AM,12:00 PM
   InfoVis,TVCG,Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation,J),"Matthew Brehmer, Bongshin Lee, Petra Isenberg, Eun Kyoung Choe",https://hal.inria.fr/hal-01857469/document,"In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.",,https://github.com/Microsoft/RangesOnMobile/blob/master/StudyApp,https://github.com/Microsoft/RangesOnMobile/blob/master/StudyDataAnalysis,,vimeo 289784625,10.1109/tvcg.2018.2865234,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,12:00 PM,12:20 PM
   InfoVis,TVCG,Glanceable Visualization: Studies of Data Comparison Performance on Smartwatches,J),"Tanja Blascheck, Lonni Besançon, Anastasia Bezerianos, Bongshin Lee, Petra Isenberg",https://hal.inria.fr/hal-01851306/file/Blascheck_2018_Glanceable_Visualization.pdf,"We present the results of two perception studies to assess how quickly people can perform a simple data comparison task for small-scale visualizations on a smartwatch. The main goal of these studies is to extend our understanding of design constraints for smartwatch visualizations. Previous work has shown that a vast majority of smartwatch interactions last under 5 s. It is still unknown what people can actually perceive from visualizations during such short glances, in particular with such a limited display space of smartwatches. To shed light on this question, we conducted two perception studies that assessed the lower bounds of task time for a simple data comparison task. We tested three chart types common on smartwatches: bar charts, donut charts, and radial bar charts with three different data sizes: 7, 12, and 24 data values. In our first study, we controlled the differences of the two target bars to be compared, while the second study varied the difference randomly. For both studies, we found that participants performed the task on average in <300 ms for the bar chart, <220 ms for the donut chart, and in <1780 ms for the radial bar chart. Thresholds in the second study per chart type were on average 1.14–1.35× higher than in the first study. Our results show that bar and donut charts should be preferred on smartwatch displays when quick data comparisons are necessary.",,,,,vimeo 289785013,10.1109/tvcg.2018.2865142,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,12:20 PM,12:40 PM
  -CG&A,CGA,Physical Visualization of Geospatial Datasets,,"Hessam Djavaherpour, Ali Mahdavi-Amiri, Faramarz F. Samavati",,,,,,,vimeo 289785692,10.1109/mcg.2017.38,,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:00 AM,11:20 AM
  -CG&A,CGA,Typology of Uncertainty in Static Geolocated Graphs for Visualization,,"Tatiana von Landesberger, Sebastian Bremm, Marcel Wunderlich",,,,,,,vimeo 289785707,10.1109/mcg.2017.3621220,,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:20 AM,11:40 AM
  -CG&A,CGA,Impact of Spatial Scales on the Intercomparison of Climate Scenarios,,"Wei Luo, Michael Steptoe, Zheng Chang, Robert Link, Leon Clarke, Ross Maciejewski",,,,,,,vimeo 289785725,10.1109/mcg.2017.3621222,,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:40 AM,12:00 PM
  -CG&A,CGA,Urban Space Explorer: A Visual Analytics System for Urban Planning,,"Alireza Karduni, Isaac Cho, Ginette Wessel, William Ribarsky, Eric Sauda, Wenwen Dou",,,,,,,vimeo 289785751,10.1109/mcg.2017.3621223,,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,12:00 PM,12:20 PM
  +CG&A,CGA,Physical Visualization of Geospatial Datasets,,"Hessam Djavaherpour, Ali Mahdavi-Amiri, Faramarz F. Samavati",,,,,,,vimeo 289785692,10.1109/mcg.2017.38,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:00 AM,11:20 AM
  +CG&A,CGA,Typology of Uncertainty in Static Geolocated Graphs for Visualization,,"Tatiana von Landesberger, Sebastian Bremm, Marcel Wunderlich",,,,,,,vimeo 289785707,10.1109/mcg.2017.3621220,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:20 AM,11:40 AM
  +CG&A,CGA,Impact of Spatial Scales on the Intercomparison of Climate Scenarios,,"Wei Luo, Michael Steptoe, Zheng Chang, Robert Link, Leon Clarke, Ross Maciejewski",,,,,,,vimeo 289785725,10.1109/mcg.2017.3621222,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:40 AM,12:00 PM
  +CG&A,CGA,Urban Space Explorer: A Visual Analytics System for Urban Planning,,"Alireza Karduni, Isaac Cho, Ginette Wessel, William Ribarsky, Eric Sauda, Wenwen Dou",,,,,,,vimeo 289785751,10.1109/mcg.2017.3621223,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,12:00 PM,12:20 PM
   CG&A,CGA,Name Profiler Toolkit,,"Feng Wang, Brett Hansen, Ryan Simmons, Ross Maciejewski",http://rmaciejewski.faculty.asu.edu/papers/2017/Names-CGA.pdf,"The Name Profiler Toolkit is a visual analytics system designed to enable the interactive exploration and analysis of forename and surname geographical distributions across the United States. Using demographic data from the US Census Bureau and Zillow, the toolkit lets users interactively compare distributions of names and name attributes.",,,,,vimeo 289785766,10.1109/mcg.2017.3621224,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,12:20 PM,12:40 PM
   VAST,TVCG,"SIRIUS: Dual, Symmetric, Interactive Dimension Reductions ",J),"Michelle Dowling, John Wenskovitch, J.T. Fry, Leanna House, Scotland Leman, Chris North",,,,,,,vimeo 289787667,10.1109/tvcg.2018.2865047,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:20 PM,2:40 PM
  -TVCG,TVCG,A Perception-Driven Approach to Supervised Dimensionality Reduction for Visualization,T),"Yunhai Wang, Kang Feng, Xiaowei Chu, Jian Zhang, Chi-Wing Fu, Michael Sedlmair, Xiaohui Yu, Baoquan Chen",,,,,,,vimeo 289788938,10.1109/tvcg.2017.2701829,,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:40 PM,3:00 PM
  +TVCG,TVCG,A Perception-Driven Approach to Supervised Dimensionality Reduction for Visualization,T),"Yunhai Wang, Kang Feng, Xiaowei Chu, Jian Zhang, Chi-Wing Fu, Michael Sedlmair, Xiaohui Yu, Baoquan Chen",,,,,,,vimeo 289788938,10.1109/tvcg.2017.2701829,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:40 PM,3:00 PM
   VAST,VAST,SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach,C),"Michael Blumenschein, Michael Behrisch, Stefanie Schmid, Simon Butscher, Deborah R. Wahl, Karoline Villinger, Britta Renner, Harald Reiterer, Daniel Keim",https://bib.dbvis.de/uploadedFiles/BlumenscheinetalVAST2018SMARTexplore.pdf,"We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst’s trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing highdimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.",,,,,vimeo 289787735,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:00 PM,3:20 PM
  -TVCG,TVCG,ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color,T),"Shenghui Cheng, Wei Xu, Klaus Mueller",,,,,,,vimeo 289788955,10.1109/tvcg.2018.2808489,,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:20 PM,3:40 PM
  +TVCG,TVCG,ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color,T),"Shenghui Cheng, Wei Xu, Klaus Mueller",,,,,,,vimeo 289788955,10.1109/tvcg.2018.2808489,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:20 PM,3:40 PM
   VAST,VAST,EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection,C),"Quan Li, Kristanto Sean Njotoprawiro, Hammad Haleem, Qiaoan Chen, Chris YI, Xiaojuan Ma",https://arxiv.org/pdf/1808.09074.pdf,"Constructing latent vector representation for nodes in a network through embedding models has shown its practicality in many graph analysis applications, such as node classification, clustering, and link prediction. However, despite the high efficiency and accuracy of learning an embedding model, people have little clue of what information about the original network is preserved in the embedding vectors. The abstractness of low-dimensional vector representation, stochastic nature of the construction process, and non-transparent hyper-parameters all obscure understanding of network embedding results. Visualization techniques have been introduced to facilitate embedding vector inspection, usually by projecting the embedding space to a two-dimensional display. Although the existing visualization methods allow simple examination of the structure of embedding space, they cannot support in-depth exploration of the embedding vectors. In this paper, we design an exploratory visual analytics system that supports the comparative visual interpretation of embedding vectors at the cluster, instance, and structural levels. To be more specific, it facilitates comparison of what and how node metrics are preserved across different embedding models and investigation of relationships between node metrics and selected embedding vectors. Several case studies confirm the efficacy of our system. Experts’ feedback suggests that our approach indeed helps them better embrace the understanding of network embedding models.",,,,,vimeo 289788000,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:40 PM,4:00 PM
  -InfoVis,TVCG,Evaluating 'Graphical Perception' with CNNs,,"Daniel Haehn, James Tompkin, Hanspeter Pfister",https://danielhaehn.com/papers/haehn2018evaluating.pdf,"Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.",,https://github.com/rhoana/perception,,,vimeo 280506639,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:20 PM,2:40 PM
  +InfoVis,TVCG,Evaluating 'Graphical Perception' with CNNs,,"Daniel Haehn, James Tompkin, Hanspeter Pfister",https://danielhaehn.com/papers/haehn2018evaluating.pdf,"Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.",,https://github.com/rhoana/perception,,,vimeo 280506639,10.1109/TVCG.2018.2865138,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:20 PM,2:40 PM
   InfoVis,TVCG,NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models,J),"Shusen Liu, Zhimin Li, Tao Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer",http://www.sci.utah.edu/~shusenl/publications/paper_entailVis.pdf,"With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a tight yet flexible integration between visualization elements and the underlying model, allows a user to interrogate the model by perturbing the input, internal state, and prediction while observing changes in other parts of the pipeline. We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model, probe its inner states, and interpret and form hypotheses about fundamental model mechanisms such as attention.",,,,,vimeo 289784737,10.1109/tvcg.2018.2865230,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:40 PM,3:00 PM
  -InfoVis,TVCG,Elastic Documents: Coupling Text and Tables through Contextual Visualizations for Enhanced Document Reading,J),"Sriram Karthik Badam, Zhicheng Liu, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/elastic-documents/elastic-documents.pdf,"Today's data-rich documents are often complex datasets in themselves, consisting of information in different formats such as text, gures, and data tables. These additional media augment the textual narrative in the document. However, the static layout of a traditional for-print document often impedes deep understanding of its content because of the need to navigate to access content scattered throughout the text. In this paper, we seek to facilitate enhanced comprehension of such documents through a contextual visualization technique that couples text content with data tables contained in the document. We parse the text content and data tables, cross-link the components using a keyword-based matching algorithm, and generate on-demand visualizations based on the reader's current focus within a document. We evaluate this technique in a user study comparing our approach to a traditional reading experience. Results from our study show that (1) participants comprehend the content better with tighter coupling of text and data, (2) the contextual visualizations enable participants to develop better summaries that capture the main data-rich insights within the document, and (3) overall, our method enables participants to develop a more detailed understanding of the document content.",,,,,vimeo 289784966,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:00 PM,3:20 PM
  +InfoVis,TVCG,Elastic Documents: Coupling Text and Tables through Contextual Visualizations for Enhanced Document Reading,J),"Sriram Karthik Badam, Zhicheng Liu, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/elastic-documents/elastic-documents.pdf,"Today's data-rich documents are often complex datasets in themselves, consisting of information in different formats such as text, gures, and data tables. These additional media augment the textual narrative in the document. However, the static layout of a traditional for-print document often impedes deep understanding of its content because of the need to navigate to access content scattered throughout the text. In this paper, we seek to facilitate enhanced comprehension of such documents through a contextual visualization technique that couples text content with data tables contained in the document. We parse the text content and data tables, cross-link the components using a keyword-based matching algorithm, and generate on-demand visualizations based on the reader's current focus within a document. We evaluate this technique in a user study comparing our approach to a traditional reading experience. Results from our study show that (1) participants comprehend the content better with tighter coupling of text and data, (2) the contextual visualizations enable participants to develop better summaries that capture the main data-rich insights within the document, and (3) overall, our method enables participants to develop a more detailed understanding of the document content.",,,,,vimeo 289784966,10.1109/TVCG.2018.2865119,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:00 PM,3:20 PM
   InfoVis,TVCG,Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication,J),"Arjun Srinivasan, Steven Drucker, Alex Endert, John Stasko",https://arjun010.github.io/static/papers/voder-infovis18.pdf,"Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG) capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying visualization. However, depending on the features of the visualization (e.g., visualization type, data density) and the complexity of the data fact, mentally mapping facts to visualizations can be a challenging task. Furthermore, more than one visualization could be used to illustrate a single data fact. Unfortunately, current tools provide little or no support for users to explore such alternatives. In this paper, we explore how system-generated data facts can be treated as interactive widgets to help users interpret visualizations and communicate their findings. We present Voder, a system that lets users interact with automatically-generated data facts to explore both alternative visualizations to convey a data fact as well as a set of embellishments to highlight a fact within a visualization. Leveraging data facts as interactive widgets, Voder also facilitates data fact-based visualization search. To assess Voder's design and features, we conducted a preliminary user study with 12 participants having varying levels of experience with visualization tools. Participant feedback suggested that interactive data facts aided them in interpreting visualizations. Participants also stated that the suggestions surfaced through the facts helped them explore alternative visualizations and embellishments to communicate individual data facts.",,,,,vimeo 289784943,10.1109/tvcg.2018.2865145,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:20 PM,3:40 PM
   InfoVis,TVCG,What Do We Talk About When We Talk About Dashboards?,J),"Alper Sarikaya, Michael Correll, Lyn Bartram, Melanie Tory, Danyel A Fisher",https://alper.datav.is/assets/publications/dashboards/dashboards-preprint.pdf,"Dashboards are one of the most common use cases for data visualization, and their design and contexts of use are considerably different from exploratory visualization tools. In this paper, we look at the broad scope of how dashboards are used in practice through an analysis of dashboard examples and documentation about their use. We systematically review the literature surrounding dashboard use, construct a design space for dashboards, and identify major dashboard types. We characterize dashboards by their design goals, levels of interaction, and the practices around them. Our framework and literature review suggest a number of fruitful research directions to better support dashboard design, implementation, and use.",,,,,vimeo 289785315,10.1109/tvcg.2018.2864903,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:40 PM,4:00 PM
   SciVis,TVCG,Visualization of Bubble Formation in Porous Media,J),"Hui Zhang, Steffen Frey, Holger Steeb, David Uribe, Thomas Ertl, Wenping Wang",,,,,,,vimeo 290325359,10.1109/tvcg.2018.2864506,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,2:20 PM,2:40 PM
   SciVis,TVCG,Gaia Sky: Navigating the Gaia Catalog,J),"Toni Sagristà Sellés, Stefan Jordan, Thomas Mueller, Filip Sadlo",,,,,,,vimeo 290325474,10.1109/tvcg.2018.2864508,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,2:40 PM,3:00 PM
   SciVis,TVCG,Interactive 3D Visual Analysis of Atmospheric Fronts,J),"Michael Alexander Kern, Timothy David Hewson, Andreas Schäfler, Rüdiger Westermann, Marc Rautenhaus",,,,,,,vimeo 290325737,10.1109/tvcg.2018.2864806,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:00 PM,3:20 PM
   SciVis,TVCG,An Interactive Framework for Visualization of Weather Forecast Ensembles,J),"Bo Ma, Alireza Entezari",,,,,,,vimeo 290325855,10.1109/tvcg.2018.2864815,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:20 PM,3:40 PM
  -TVCG,TVCG,Animation Plans for Before-And-After Satellite images,T),"María-Jesús Lobo, Caroline Appert, Emmanuel Pietriga",,,,,,,vimeo 289789265,,,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:40 PM,4:00 PM
  +TVCG,TVCG,Animation Plans for Before-And-After Satellite images,T),"María-Jesús Lobo, Caroline Appert, Emmanuel Pietriga",,,,,,,vimeo 289789265,10.1109/TVCG.2018.2796557,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:40 PM,4:00 PM
   VAST,TVCG,Vulnus: Visual Vulnerability Analysis for Network Security,J),"Marco Angelini, Graziano Blasilli, Tiziana Catarci, Simone Lenti, Giuseppe Santucci",,,,,,,vimeo 289787623,10.1109/tvcg.2018.2865028,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:00 AM,9:20 AM
   VAST,TVCG,GraphProtector: a Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms,J),"Xumeng Wang, Wei Chen, Huihua Guan, Wenlong Chen, Rusheng Pan, Jia-Kai Chou, Chris Bryan, Kwan-Liu Ma",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/GraphProtector.pdf,"Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques—along with evaluating how applying the strategies will affect the utility of the anonymized results—remains a signicant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.",,,,,vimeo 289787471,10.1109/TVCG.2018.2865021,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:20 AM,9:40 AM
   VAST,TVCG,Situ: Identifying and explaining suspicious behavior in networks,J),"John Goodall, Eric Ragan, Chad Steed, Joel Reed, Gregory Richardson, Kelly Huffer, Robert Bridges, Jason Laska",https://www.cise.ufl.edu/~eragan/papers/Goodall_Situ_2018.pdf,"Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major nancial damages. Anomaly detection methods are benecial for detecting new types of attacks and abnormal network activity, but such algorithms can be difcult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. We discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.",,,,,vimeo 289787635,10.1109/TVCG.2018.2865029,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:40 AM,10:00 AM
  
positive.code: VAST,VAST,The Effect of Proximity in Social Data Charts on Perceived Unity,C),"Marlen Promann, Sabine Brunswicker",,,,,,,vimeo 289787716,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,2:40 PM,3:00 PM
  VAST,TVCG,Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices,J),"Sara Alspaugh, Nava Zokaei, Andrea Liu, Cindy Jin, Marti Hearst",http://people.ischool.berkeley.edu/~hearst/papers/vast2018.pdf,"We report the results of interviewing thirty professional data analysts working in a range of industrial, academic, and regulatory environments. This study focuses on participants' descriptions of exploratory activities and tool usage in these activities. Highlights of the ndings include: distinctions between exploration as a precursor to more directed analysis versus truly open-ended exploration; conrmation that some analysts see “nding something interesting” as a valid goal of data exploration while others explicitly disavow this goal; conicting views about the role of intelligent tools in data exploration; and pervasive use of visualization for exploration, but with only a subset using direct manipulation interfaces. These ndings provide guidelines for future tool development, as well as a better understanding of the meaning of the term “data exploration” based on the words of practitioners “in the wild.”",,,,,vimeo 289787961,10.1109/TVCG.2018.2865040,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:00 PM,3:20 PM
  VAST,VAST,The Effect of Semantic Interaction on Foraging in Text Analysis,C),"John Wenskovitch, Lauren Bradel, Michelle Dowling, Leanna House, Chris North",https://infovis.cs.vt.edu/sites/default/files/effect-semantic-interaction.pdf,"Completing text analysis tasks is a continuous sensemaking loop of foraging for information and incrementally synthesizing it into hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the task at hand. StarSPIRE is a visual analytics tool designed to explore collections of documents, leveraging users’ semantic interactions to steer (1) a synthesis model that aids in document layout, and (2) a foraging model to automatically retrieve new relevant information. In contrast to traditional keyword search foraging (KSF), “semantic interaction foraging” (SIF) occurs as a result of the user’s synthesis actions. To quantify the value of semantic interaction foraging, we use StarSPIRE to evaluate its utility for an intelligence analysis sensemaking task. Semantic interaction foraging accounted for 26% of useful documents found, and it also resulted in increased synthesis interactions and improved sensemaking task performance by users in comparison to only using keyword search.",,,,,vimeo 289787752,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:20 PM,3:40 PM
  VAST,TVCG,Cost-benefit Analysis of Visualization in Virtual Environments,J),"Min Chen, Kelly Gaither, Nigel John, Brian McCann",,,,,,,vimeo 289787910,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:40 PM,4:00 PM
  InfoVis,TVCG,Shape-preserving Star Coordinates,J),"Vladimir Molchanov, Lars Linsen",,,,,,,vimeo 289785145,10.1109/tvcg.2018.2865118,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:20 PM,2:40 PM
  TVCG,TVCG,Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-operative Prostate Cancer,T),"Jürgen Bernard, David Sessler, Jörn Kohlhammer, Roy A. Ruddle",http://eprints.whiterose.ac.uk/128739/1/bernard-ieee-tvcg-dashboard-networks.pdf,"In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.",,,,,vimeo 289789053,10.1109/tvcg.2018.2803829,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:40 PM,3:00 PM
  InfoVis,TVCG,SRVis: Towards Better Spatial Integration in Ranking Visualization,J),"Di Weng, Ran Chen, Zikun Deng, Feiran Wu, Jingmin Chen, Yingcai Wu",http://zjuvis.org/files/srvis.pdf,"Interactive ranking techniques have substantially promoted analysts' ability in making judicious and informed decisions effectively based on multiple criteria. However, the existing techniques cannot satisfactorily support the analysis tasks involved in ranking large-scale spatial alternatives, such as selecting optimal locations for chain stores, where the complex spatial contexts involved are essential to the decision-making process. Limitations observed in the prior attempts of integrating rankings with spatial contexts motivate us to develop a context-integrated visual ranking technique. Based on a set of generic design requirements we summarized by collaborating with domain experts, we propose SRVis, a novel spatial ranking visualization technique that supports efficient spatial multi-criteria decision-making processes by addressing three major challenges in the aforementioned context integration, namely, a) the presentation of spatial rankings and contexts, b) the scalability of rankings' visual representations, and c) the analysis of context-integrated spatial rankings. Specifically, we encode massive rankings and their cause with scalable matrix-based visualizations and stacked bar charts based on a novel two-phase optimization framework that minimizes the information loss, and the flexible spatial filtering and intuitive comparative analysis are adopted to enable the in-depth evaluation of the rankings and assist users in selecting the best spatial alternative. The effectiveness of the proposed technique has been evaluated and demonstrated with an empirical study of optimization methods, two case studies, and expert interviews.",https://srvis.zjuvis.org/,,,,vimeo 289784783,,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:00 PM,3:20 PM
  
negative.code: VAST,TVCG,MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration,J),"Po-Ming Law, Zhicheng Liu, Sana Malik, Rahul Basole",http://www.zcliu.org/maqui/MAQUI_vast18.pdf,"Exploring event sequences by defining queries alone or by using mining algorithms alone is often not sufficient to support analysis. Analysts often interweave querying and mining in a recursive manner during event sequence analysis: sequences extracted as query results are used for mining patterns, patterns generated are incorporated into a new query for segmenting the sequences, and the resulting segments are mined or queried again. To support flexible analysis, we propose a framework that describes the process of interwoven querying and mining. Based on this framework, we developed MAQUI, a Mining And Querying User Interface that enables recursive event sequence exploration. To understand the efficacy of MAQUI, we conducted two case studies with domain experts. The findings suggest that the capability of interweaving querying and mining helps the participants articulate their questions and gain novel insights from their data.",,https://github.com/terrancelaw/MAQUI,,,vimeo 289787863,10.1109/TVCG.2018.2864886,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:00 AM,9:20 AM
  VAST,TVCG,iForest: Interpreting Random Forests via Visual Analytics,J),"Xun Zhao, Yanhong Wu, Dik Lun Lee, Weiwei Cui",http://zhaoxun.me/publication/iForest.pdf,"As an ensemble model that consists of many independent decision trees, random forests generate predictions by feeding the input to internal trees and summarizing their outputs. The ensemble nature of the model helps random forests outperform any individual decision tree. However, it also leads to a poor model interpretability, which significantly hinders the model from being used in fields that require transparent and explainable predictions, such as medical diagnosis and financial fraud detection. The interpretation challenges stem from the variety and complexity of the contained decision trees. Each decision tree has its unique structure and properties, such as the features used in the tree and the feature threshold in each tree node. Thus, a data input may lead to a variety of decision paths. To understand how a final prediction is achieved, it is desired to understand and compare all decision paths in the context of all tree structures, which is a huge challenge for any users. In this paper, we propose a visual analytic system aiming at interpreting random forest models and predictions. In addition to providing users with all the tree information, we summarize the decision paths in random forests, which eventually reflects the working mechanism of the model and reduces users’ mental burden of interpretation. To demonstrate the effectiveness of our system, two usage scenarios and a qualitative user study are conducted.",,,,,vimeo 289787165,10.1109/tvcg.2018.2864475,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:20 AM,9:40 AM
  VAST,TVCG,Visual Progression Analysis of Event Sequence Data,J),"Shunan Guo, Zhuochen Jin, David Gotz, Fan Du, Hongyuan Zha, Nan Cao",http://gotz.web.unc.edu/files/2018/08/2018_VAST_EventThread_V2_Preprint.pdf,"Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identication and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a signicant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help dene those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2 , which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2 : (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design",,,,,vimeo 289787414,10.1109/TVCG.2018.2864885,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams,T),"Yingcai Wu, Zhutian Chen, Guodao Sun, Xiao Xie, Nan Cao, Shixia Liu, Weiwei Cui",,,,,,,vimeo 289788908,10.1109/tvcg.2017.2764459,2017,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:00 AM,10:20 AM
  VAST,TVCG,Duet: Helping Data Analysis Novices Conduct Pairwise Comparisons by Minimal Specification,J),"Po-Ming Law, Rahul Basole, Yanhong Wu",https://terrancelaw.github.io/publications/duet_vast18.pdf,"Data analysis novices often encounter barriers in executing low-level operations for pairwise comparisons. They may also run into barriers in interpreting the artifacts (e.g., visualizations) created as a result of the operations. We developed Duet, a visual analysis system designed to help data analysis novices conduct pairwise comparisons by addressing execution and interpretation barriers. To reduce the barriers in executing low-level operations during pairwise comparison, Duet employs minimal specication: when one object group (i.e. a group of records in a data table) is specied, Duet recommends object groups that are similar to or different from the specied one; when two object groups are specied, Duet recommends similar and different attributes between them. To lower the barriers in interpreting its recommendations, Duet explains the recommended groups and attributes using both visualizations and textual descriptions. We conducted a qualitative evaluation with eight participants to understand the effectiveness of Duet. The results suggest that minimal specication is easy to use and Duet's explanations are helpful for interpreting the recommendations despite some usability issues.",,,,,vimeo 289787850,10.1109/TVCG.2018.2864526,2018,2018,VAST,"Convention Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:20 AM,10:40 AM
  InfoVis,TVCG,Visualizing Uncertain Tropical Cyclone Predictions using Representative Samples from Ensembles of Forecast Tracks,J),"Le Liu, Lace M. K. Padilla, Sarah Creem-Regehr, Donald House",http://lacepadilla.com/Downloads/publications/Liuetal_2018.pdf,"A common approach to sampling the space of a prediction is the generation of an ensemble of potential outcomes, where the ensemble's distribution reveals the statistical structure of the prediction space. For example, the US National Hurricane Center generates multiple day predictions for a storm's path, size, and wind speed, and then uses a Monte Carlo approach to sample this prediction into a large ensemble of potential storm outcomes. Various forms of summary visualizations are generated from such an ensemble, often using spatial spread to indicate its statistical characteristics. However, studies have shown that changes in the size of such summary glyphs, representing changes in the uncertainty of the prediction, are frequently confounded with other attributes of the phenomenon, such as its size or strength. In addition, simulation ensembles typically encode multivariate information, which can be difficult or confusing to include in a summary display. This problem can be overcome by directly displaying the ensemble as a set of annotated trajectories, however this solution will not be effective if ensembles are densely overdrawn or structurally disorganized. We propose to overcome these difficulties by selectively sampling the original ensemble, constructing a smaller representative and spatially well organized ensemble. This can be drawn directly as a set of paths that implicitly reveals the underlying spatial uncertainty distribution of the prediction. Since this approach does not use a visual channel to encode uncertainty, additional information can more easily be encoded in the display without leading to visual confusion. To demonstrate our argument, we describe the development of a visualization for ensembles of tropical cyclone forecast tracks, explaining how their spatial and temporal predictions, as well as other crucial storm characteristics such as size and intensity, can be clearly revealed. We verify the effectiveness of this visualization approach through a cognitive study exploring how storm damage estimates are affected by the density of tracks drawn, and by the presence or absence of annotating information on storm size and intensity.",,https://osf.io/hy3ba/,https://osf.io/hy3ba/,,vimeo 289784601,10.1109/TVCG.2018.2865193,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:00 AM,9:20 AM
  InfoVis,TVCG,Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data,J),"Alex Kale, Francis Nguyen, Matthew Kay, Jessica Hullman",http://users.eecs.northwestern.edu/~jhullman/hops_jobs_pfs.pdf,"Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistical properties of ensembles. However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication. We identify and motivate an appropriate task to investigate realistic judgments of uncertainty in the public domain through a qualitative analysis of uncertainty visualizations in the news. We contribute two crowdsourced experiments comparing the effectiveness of HOPs, error bars, and line ensembles for supporting perceptual decision-making from visualized uncertainty. Participants infer which of two possible underlying trends is more likely to have produced a sample of time series data by referencing uncertainty visualizations which depict the two trends with variability due to sampling error. By modeling each participant's accuracy as a function of the level of evidence presented over many repeated judgments, we find that observers are able to correctly infer the underlying trend in samples conveying a lower level of evidence when using HOPs rather than static aggregate uncertainty visualizations as a decision aid. Modeling approaches like ours contribute theoretically grounded and richly descriptive accounts of user perceptions to visualization evaluation.",https://medium.com/@uwdata/hypothetical-outcome-plots-hops-help-users-separate-signal-from-noise-870d4e2b75d7,,https://github.com/kalealex/jobs-report-hops,https://osf.io/8cte3/registrations/,vimeo 289785258,10.1109/tvcg.2018.2864909,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:20 AM,9:40 AM
  InfoVis,TVCG,In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation,J),"Jessica Hullman, Xiaoli Qiao, Michael Correll, Alex Kale, Matthew Kay",https://github.com/jhullman/uncertaintyVisEval/blob/master/uncertainty_vis_eval.pdf,"Understanding and accounting for uncertainty is critical to effectively reasoning about visualized data. However, evaluating the impact of an uncertainty visualization is complex due to the difficulties that people have interpreting uncertainty and the challenge of defining correct behavior with uncertainty information. Currently, evaluators of uncertainty visualization must rely on general purpose visualization evaluation frameworks which can be ill-equipped to provide guidance with the unique difficulties of assessing judgments under uncertainty. To help evaluators navigate these complexities, we present a taxonomy for characterizing decisions made in designing an evaluation of an uncertainty visualization. Our taxonomy differentiates six levels of decisions that comprise an uncertainty visualization evaluation: the behavioral targets of the study, expected effects from an uncertainty visualization, evaluation goals, measures, elicitation techniques, and analysis approaches. Applying our taxonomy to 86 user studies of uncertainty visualizations, we find that existing evaluation practice, particularly in visualization research, focuses on Performance and Satisfaction-based measures that assume more predictable and statistically-driven judgment behavior than is suggested by research on human judgment and decision making. We reflect on common themes in evaluation practice concerning the interpretation and semantics of uncertainty, the use of confidence reporting, and a bias toward evaluating performance as accuracy rather than decision quality. We conclude with a concrete set of recommendations for evaluators designed to reduce the mismatch between the conceptualization of uncertainty in visualization versus other fields.",,https://github.com/jhullman/uncertaintyVisEval,,,vimeo 289785271,10.1109/tvcg.2018.2864889,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:40 AM,10:00 AM
  InfoVis,TVCG,Where's my data? Evaluating Visualizations with Missing Data,J),"Hayeong Song, Danielle Albers Szafir",http://cmci.colorado.edu/visualab/papers/song_VIS_2018.pdf,Many real-world datasets are incomplete due to factors such as data collection failures or misalignments between fused datasets. Visualizations of incomplete datasets should allow analysts to draw conclusions from their data while effectively reasoning about the quality of the data and resulting conclusions. We conducted a pair of crowdsourced studies to measure how the methods used to impute and visualize missing data may influence analysts' perceptions of data quality and their confidence in their conclusions. Our experiments used different design choices for line graphs and bar charts to estimate averages and trends in incomplete time series datasets. Our results provide preliminary guidance for visualization designers to consider when working with incomplete data in different domains and scenarios.,,,http://cmci.colorado.edu/visualab/MissingData/,,vimeo 289785184,10.1109/TVCG.2018.2864914,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:00 AM,10:20 AM
  InfoVis,TVCG,A Framework for Externalizing Implicit Error Using Visualization,,"Nina McCurdy, Julie Gerdes, Miriah Meyer",http://sci.utah.edu/~vdl/papers/2018_infovis_IE-Framework.pdf,"This paper presents a framework for externalizing and analyzing expert knowledge about discrepancies in data through the use of visualization. Grounded in an 18-month design study with global health experts, the framework formalizes the notion of data discrepancies as implicit error, both in global health data and more broadly. We use the term implicit error to describe measurement error that is inherent to and pervasive throughout a dataset, but that isn't explicitly accounted for or defined. Instead, implicit error exists in the minds of experts, is mainly qualitative, and is accounted for subjectively during expert interpretation of the data. Externalizing knowledge surrounding implicit error can assist in synchronizing, validating, and enhancing interpretation, and can inform error analysis and mitigation. The framework consists of a description of implicit error components that are important for downstream analysis, along with a process model for externalizing and analyzing implicit error using visualization. As a second contribution, we provide a rich, reflective, and verifiable description of our research process as an exemplar summary toward the ongoing inquiry into ways of increasing the validity and transferability of design study research.",,,,,vimeo 289785202,10.1109/tvcg.2018.2864913,2018,2018,InfoVis,"Convention Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:20 AM,10:40 AM
  SciVis,TVCG,Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps,J),"Jun Tao, Martin Imre, Chaoli Wang, Nitesh V Chawla, Hanqi Guo, Gökhan Sever, Seung Hyun Kim",,,,,,,vimeo 290325769,10.1109/tvcg.2018.2864808,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:00 AM,9:20 AM
  SciVis,TVCG,Visual Analysis of Spatio-temporal Relations of Pairwise Attributes in Unsteady Flow,J),"Marzieh Berenjkoub, Rodolfo Ostilla Monico, Robert S. Laramee, Guoning Chen",,,,,,,vimeo 290325964,10.1109/tvcg.2018.2864817,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:20 AM,9:40 AM
  SciVis,TVCG,Time-Dependent Flow seen through Approximate Observer Killing Fields,J),"Markus Hadwiger, Matej Mlejnek, Thomas Theussl, Peter Rautek",,,,,,,vimeo 290327432,10.1109/tvcg.2018.2864839,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,An Exploratory Framework for Cyclone Identification and Tracking,T),"Akash Anil Valsangkar, Joy Merwin Monteiro, Vidya Narayanan, Ingrid Hotz, Vijay Natarajan",,,,,,,vimeo 289789170,10.1109/tvcg.2018.2810068,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,10:00 AM,10:20 AM
  TVCG,TVCG,Popup-Plots: Warping Temporal Data Visualization,T),"Johanna Schmidt, Dominik Fleischmann, Bernhard Preim, Norbert Brandle, Gabriel Mistelbauer",,,,,,,vimeo 289789393,10.1109/tvcg.2018.2841385,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,10:20 AM,10:40 AM