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IEEE Transactions on Visualization and Computer Graphics
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IEEE Transactions on Visualization and Computer Graphics
Year: 2018 | Volume: 24 | Issue: 1
Preface: Message from the VIS Paper Chairs and Guest Editors
Tim Dwyer;Niklas Elmqvist;Brian Fisher;Steve Franconeri;Ingrid Hotz;Robert M. Mike Kirby;Shixia Liu;Tobias Schreck;Xiaoru Yuan
Publication Year: 2018,Page(s):xi - xv
IEEE Visualization and Graphics Technical Committee (VGTC)
Publication Year: 2018,Page(s):xvi - xvi
IEEE Visualization and Graphics Technical Committee (VGTC)
Year: 2018 | Volume: 24 | Issue: 1
VIS Conference Committee
Year: 2018 | Volume: 24 | Issue: 1
VAST International Program Committee
Year: 2018 | Volume: 24 | Issue: 1
VAST Steering Committee
Year: 2018 | Volume: 24 | Issue: 1
VAST Paper Reviewers
Year: 2018 | Volume: 24 | Issue: 1
The 2017 Visualization Career Award
Year: 2018 | Volume: 24 | Issue: 1
The 2017 Visualization Technical Achievement Award
Publication Year: 2018,Page(s):xxvii - xxviii
The 2017 Visualization Technical Achievement Award
Year: 2018 | Volume: 24 | Issue: 1
Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow
Kanit Wongsuphasawat;Daniel Smilkov;James Wexler;Jimbo Wilson;Dandelion Mané;Doug Fritz;Dilip Krishnan;Fernanda B. Viégas;Martin Wattenberg
Publication Year: 2018,Page(s):1 - 12
Cited by: Papers (231)
We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the grap...Show More
Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis
Manuel Stein;Halldor Janetzko;Andreas Lamprecht;Thorsten Breitkreutz;Philipp Zimmermann;Bastian Goldlücke;Tobias Schreck;Gennady Andrienko;Michael Grossniklaus;Daniel A. Keim
Publication Year: 2018,Page(s):13 - 22
Cited by: Papers (110)
Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rel...
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First Page of the Article
Show MoreVoila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data
Nan Cao;Chaoguang Lin;Qiuhan Zhu;Yu-Ru Lin;Xian Teng;Xidao Wen
Publication Year: 2018,Page(s):23 - 33
Cited by: Papers (70)
The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity ...Show More
Clustering Trajectories by Relevant Parts for Air Traffic Analysis
Gennady Andrienko;Natalia Andrienko;Georg Fuchs;Jose Manuel Cordero Garcia
Publication Year: 2018,Page(s):34 - 44
Cited by: Papers (62)
Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., ...Show More
Sequence Synopsis: Optimize Visual Summary of Temporal Event Data
Publication Year: 2018,Page(s):45 - 55
Cited by: Papers (78)
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 visualizat...Show More
EventThread: Visual Summarization and Stage Analysis of Event Sequence Data
Shunan Guo;Ke Xu;Rongwen Zhao;David Gotz;Hongyuan Zha;Nan Cao
Publication Year: 2018,Page(s):56 - 65
Cited by: Papers (76)
Event sequence data such as electronic health records, a person's academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publi...Show More
Understanding a Sequence of Sequences: Visual Exploration of Categorical States in Lake Sediment Cores
Publication Year: 2018,Page(s):66 - 76
Cited by: Papers (8)
This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analysis of lake sediment cores. The goal is to gain hypotheses about landscape evolution and climate conditions in the past. To this end, geoscientists identify which categorical sequence...Show More
Analyzing the Training Processes of Deep Generative Models
Publication Year: 2018,Page(s):77 - 87
Cited by: Papers (119)
Among the many types of deep models, deep generative models (DGMs) provide a solution to the important problem of unsupervised and semi-supervised learning. However, training DGMs requires more skill, experience, and know-how because their training is more complex than other types of deep models such as convolutional neural networks (CNNs). We develop a visual analytics approach for better underst...Show More
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
Publication Year: 2018,Page(s):88 - 97
Cited by: Papers (222)
While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequ...Show More
DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks
Nicola Pezzotti;Thomas Höllt;Jan Van Gemert;Boudewijn P.F. Lelieveldt;Elmar Eisemann;Anna Vilanova
Publication Year: 2018,Page(s):98 - 108
Cited by: Papers (125)
Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of l...Show More
Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses
Alexander Kumpf;Bianca Tost;Marlene Baumgart;Michael Riemer;Rüdiger Westermann;Marc Rautenhaus
Publication Year: 2018,Page(s):109 - 119
Cited by: Papers (38)
In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be ...Show More
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 A. Keim
Publication Year: 2018,Page(s):120 - 130
Cited by: Papers (56)
Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions t...Show More
Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics
John Wenskovitch;Ian Crandell;Naren Ramakrishnan;Leanna House;Scotland Leman;Chris North
Publication Year: 2018,Page(s):131 - 141
Cited by: Papers (65)
Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However,...Show More
Clustervision: Visual Supervision of Unsupervised Clustering
Bum Chul Kwon;Ben Eysenbach;Janu Verma;Kenney Ng;Christopher De Filippi;Walter F. Stewart;Adam Perer
Publication Year: 2018,Page(s):142 - 151
Cited by: Papers (93)
Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is ...Show More
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