%0 Conference Paper %B ACM CHI 2024 %D 2024 %T Evaluating Navigation and Comparison Performance of Computational Notebooks on Desktop and in Virtual Reality %A Sungwon In %A Eric Krokos %A Whitley, Kirsten %A North, Chris %A Yang, Yalong %B ACM CHI 2024 %P 15 %8 05/2024 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2024 %T Multiple Monitors or Single Canvas? Evaluating Window Management and Layout Strategies on Virtual Displays %A Leonardo Pavanatto Soares %A Feiyu Lu %A North, Chris %A Bowman, Doug A. %B IEEE Transactions on Visualization and Computer Graphics %V to appear %8 12/2024 %0 Conference Paper %B Symposium on Visualization in Data Science (VDS) at IEEE VIS %D 2023 %T Aardvark: Comparative Visualization of Data Analysis Scripts %A Faust, Rebecca %A C. Scheidegger %A North, Chris %B Symposium on Visualization in Data Science (VDS) at IEEE VIS %P 30-38 %8 10/2023 %R 10.1109/VDS60365.2023.00009 %0 Journal Article %J Frontiers in Virtual Reality %D 2023 %T Different realities: a comparison of augmented and virtual reality for the sensemaking process %A Lee Lisle %A Kylie Davidson %A Edward J.K. Gitre %A North, Chris %A Bowman, Doug A. %B Frontiers in Virtual Reality %V 4 %P 16 %8 08/2023 %R 10.3389/frvir.2023.1177855 %0 Conference Paper %B International Conference on Information Visualization Theory and Applications (IVAPP) %D 2023 %T Evaluating Differences in Insights from Interactive Dimensionality Reduction Visualizations through Complexity and Vocabulary %A Mia Taylor %A Lata Kodali %A House, Leanna %A North, Chris %B International Conference on Information Visualization Theory and Applications (IVAPP) %P 8 pages %8 02/2023 %0 Conference Paper %B 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) %D 2023 %T Evaluating the Feasibility of Predicting Information Relevance During Sensemaking with Eye Gaze Data %A Tahmid, Ibrahim A. %A Lee Lisle %A Kylie Davidson %A Whitley, Kirsten %A North, Chris %A Bowman, Doug A. %B 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) %P 713–722 %8 10/2023 %R 10.1109/ISMAR59233.2023.00086 %0 Journal Article %J Machine Vision and Applications %D 2023 %T Explainable interactive projections of images %A Huimin Han %A Faust, Rebecca %A Norambuena, Brian Felipe Keith %A Jiayue Lin %A Song Li %A North, Chris %B Machine Vision and Applications %V 34 %8 09/2023 %N 6 %R https://doi.org/10.1007/s00138-023-01452-9 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2023 %T Exploring the Evolution of Sensemaking Strategies in Immersive Space to Think %A Kylie Davidson %A Lee Lisle %A Whitley, Kirsten %A Bowman, Doug A. %A North, Chris %B IEEE Transactions on Visualization and Computer Graphics %V 29 %P 5294-5307 %8 12/2023 %N 12 %R 10.1109/TVCG.2022.3207357 %0 Conference Paper %B ACM Intelligent User Interfaces (IUI) %D 2023 %T Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations %A Brian Keith Norambuena %A Tanu Mitra %A North, Chris %B ACM Intelligent User Interfaces (IUI) %8 03/2023 %0 Journal Article %J Computing in Science & Engineering %D 2023 %T Reflecting on the Scalable Adaptive Graphics Environment Team’s 20-Year Translational Research Endeavor in Digital Collaboration Tools %A Mahdi Belcaid %A Jason Leigh %A North, Chris %A Jesse Harden %A et al %B Computing in Science & Engineering %V 25 %P 50-56 %8 03/2023 %N 2 %R 10.1109/MCSE.2023.3297753 %0 Conference Paper %B Gateways 2023 %D 2023 %T SAGE3: Smart Amplified Group Environment %A Roderick Tabalba %A Nurit Kirshenbaum %A Jesse Harden %A Christman, Elizabeth %A Mahdi Belcaid %A North, Chris %A Jason Leigh %A et al. %B Gateways 2023 %P 5 %8 11/2023 %0 Conference Paper %B IEEE International Symposium on Mixed and Augmented Reality (ISMAR) %D 2023 %T Spaces to Think: A Comparison of Small, Large, and Immersive Displays for the Sensemaking Process %A Lee Lisle %A Kylie Davidson %A Leonardo Pavanatto Soares %A Tahmid, Ibrahim A. %A North, Chris %A Bowman, Doug A. %B IEEE International Symposium on Mixed and Augmented Reality (ISMAR) %P 1084-1093 %8 10/2023 %R 10.1109/ISMAR59233.2023.00125 %0 Journal Article %J ACM Computing Surveys %D 2023 %T A Survey on Event-Based News Narrative Extraction %A Norambuena, Brian Felipe Keith %A Tanu Mitra %A North, Chris %B ACM Computing Surveys %V 55 %P 39 %8 07/2023 %N 14s %R https://doi.org/10.1145/3584741 %0 Conference Paper %B Graphics Interface 2023 %D 2023 %T There is no reason anybody should be using 1D anymore: Design and Evaluation of 2D Jupyter Notebooks %A Jesse Harden %A Christman, Elizabeth %A Nurit Kirshenbaum %A Mahdi Belcaid %A Jason Leigh %A North, Chris %B Graphics Interface 2023 %P 12 %8 05/2023 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2023 %T This is the Table I Want! Interactive Data Transformation on Desktop and in Virtual Reality %A Sungwon In %A Tica Lin %A North, Chris %A Pfister, Hanspeter %A Yang, Yalong %B IEEE Transactions on Visualization and Computer Graphics %P 17 %8 07/2023 %R 10.1109/TVCG.2023.3299602 %0 Conference Paper %B 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) %D 2023 %T Uncovering Best Practices in Immersive Space to Think %A Kylie Davidson %A Lee Lisle %A Tahmid, Ibrahim A. %A Whitley, Kirsten %A North, Chris %A Bowman, Doug A. %B 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) %P 1094-1103 %8 10/2023 %R 10.1109/ISMAR59233.2023.00126 %0 Conference Paper %B 2022 ASEE Annual Conference & Exposition %D 2022 %T Andromeda in the Classroom: Collaborative Data Analysis for 8th Grade Engineering Design %A Mia Taylor %A Danny Mathieson %A House, Leanna %A North, Chris %B 2022 ASEE Annual Conference & Exposition %I ASEE Conferences %C Minneapolis, MN %8 08/2022 %0 Conference Paper %B Proceedings of Symposium on Visualization in Data Science (VDS) %D 2022 %T Case Study Comparison of Computational Notebook Platforms for Interactive Visual Analytics %A Han Liu %A North, Chris %B Proceedings of Symposium on Visualization in Data Science (VDS) %8 10/2022 %0 Journal Article %J Information Visualization %D 2022 %T Design guidelines for narrative maps in sensemaking tasks %A Brian Keith Norambuena %A Tanu Mitra %A North, Chris %B Information Visualization %P 1-26 %8 03/2022 %R 10.1177/14738716221079593 %0 Conference Paper %B 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) %D 2022 %T Evaluating the Benefits of Explicit and Semi-Automated Clusters for Immersive Sensemaking %A Tahmid, Ibrahim A. %A Lee Lisle %A Kylie Davidson %A North, Chris %A Bowman, Doug A. %B 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR) %P 479-488 %R 10.1109/ISMAR55827.2022.00064 %0 Conference Paper %B Advances in Visual Computing: 17th International Symposium, ISVC 2022, San Diego, CA, USA, October 3–5, 2022, Proceedings, Part I %D 2022 %T Explainable Interactive Projections For Image Data %A Huimin Han %A Faust, Rebecca %A Norambuena, Brian Felipe Keith %A Prabhu, Ritvik %A Smith, Timothy %A Song Li %A North, Chris %K Explainable AI %K Image data %K Interactive dimension reduction %K Semantic interaction %X Making sense of large collections of images is difficult. Dimension reductions (DR) assist by organizing images in a 2D space based on similarities, but provide little support for explaining why images were placed together or apart in the 2D space. Additionally, they do not provide support for modifying and updating the 2D space to explore new relationships and organizations of images. To address these problems, we present an interactive DR method for images that uses visual features extracted by a deep neural network to project the images into 2D space and provides visual explanations of image features that contributed to the 2D location. In addition, it allows people to directly manipulate the 2D projection space to define alternative relationships and explore subsequent projections of the images. With an iterative cycle of semantic interaction and explainable-AI feedback, people can explore complex visual relationships in image data. Our approach to human-AI interaction integrates visual knowledge from both human mental models and pre-trained deep neural models to explore image data. We demonstrate our method through examples with collaborators in agricultural science. %B Advances in Visual Computing: 17th International Symposium, ISVC 2022, San Diego, CA, USA, October 3–5, 2022, Proceedings, Part I %I Springer-Verlag %C Berlin, Heidelberg %P 77–90 %@ 978-3-031-20712-9 %U https://doi.org/10.1007/978-3-031-20713-6_6 %R 10.1007/978-3-031-20713-6_6 %0 Conference Paper %B 2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) %D 2022 %T Exploring Organization of Computational Notebook Cells in 2D Space %A Jesse Harden %A Christman, Elizabeth %A Nurit Kirshenbaum %A Wenskovitch, John %A Jason Leigh %A North, Chris %B 2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) %P 1-6 %8 09/2022 %R 10.1109/VL/HCC53370.2022.9833128 %0 Conference Paper %B NAPPN Annual Conference %D 2022 %T Interactive Deep Learning for Sorting Plant Images by Visual Phenotypes %A Huimin Han %A Song Li %A North, Chris %B NAPPN Annual Conference %P 5 pages %8 02/2022 %0 Conference Paper %B 2022 IEEE Visualization in Data Science (VDS) %D 2022 %T Interactive Visualization for Data Science Scripts %A Faust, Rebecca %A C. Scheidegger %A K. Isaacs %A W. Z. Bernstein %A M. Sharp %A North, Chris %K behavioral sciences %K codes %K data science %K Data visualization %K debugging %K prototypes %K visualization %X As the field of data science continues to grow, so does the need for adequate tools to understand and debug data science scripts. Current debugging practices fall short when applied to a data science setting, due to the exploratory and iterative nature of analysis scripts. Additionally, computational notebooks, the preferred scripting environment of many data scientists, present additional challenges to understanding and debugging workflows, including the non-linear execution of code snippets. This paper presents Anteater, a trace-based visual debugging method for data science scripts. Anteater automatically traces and visualizes execution data with minimal analyst input. The visualizations illustrate execution and value behaviors that aid in understanding the results of analysis scripts. To maximize the number of workflows supported, we present prototype implementations in both Python and Jupyter. Last, to demonstrate Anteater’s support for analysis understanding tasks, we provide two usage scenarios on real world analysis scripts. %B 2022 IEEE Visualization in Data Science (VDS) %I IEEE Computer Society %C Los Alamitos, CA, USA %P 37-45 %8 10/2022 %U https://doi.ieeecomputersociety.org/10.1109/VDS57266.2022.00009 %R 10.1109/VDS57266.2022.00009 %0 Journal Article %J Visual Informatics %D 2021 %T Bridging cognitive gaps between user and model in interactive dimension reduction %A Ming Wang %A Wenskovitch, John %A House, Leanna %A Nicholas Polys %A North, Chris %B Visual Informatics %V 53 %P 13-25 %N 2 %0 Conference Paper %B 26th International Conference on Intelligent User Interfaces (IUI ’21) %D 2021 %T DeepSI: Interactive Deep Learning for Semantic Interaction %A Yali Bian %A North, Chris %B 26th International Conference on Intelligent User Interfaces (IUI ’21) %P 197-207 %8 04/2021 %R https://doi.org/10.1145/3397481.3450670 %0 Conference Paper %B IEEE Virtual Reality and 3D User Interfaces (VR) %D 2021 %T Do we still need physical monitors? An evaluation of the usability of AR virtual monitors for productivity work %A Leonardo Pavanatto Soares %A Doug Bowman %A North, Chris %A Carmen Badea %A Rich Stoakley %B IEEE Virtual Reality and 3D User Interfaces (VR) %P 759-767 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2021 %T An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration %A Wenskovitch, John %A North, Chris %B IEEE Transactions on Visualization and Computer Graphics %V 27 %P 1742-1752 %8 1/2021 %N 2 %R 10.1109/TVCG.2020.3028890 %0 Conference Paper %B IEEE Visualization Conference (VIS) %D 2021 %T Narrative Sensemaking: Strategies for Narrative Maps Construction %A Brian Keith Norambuena %A Tanu Mitra %A North, Chris %B IEEE Visualization Conference (VIS) %P 181-185 %8 10/2021 %R 10.1109/VIS49827.2021.9623296 %0 Conference Paper %B IEEE Visualization Conference (VIS) %D 2021 %T Semantic Explanation of Interactive Dimensionality Reduction %A Yali Bian %A North, Chris %A Eric Krokos %A Sarah Joseph %B IEEE Visualization Conference (VIS) %P 5 pages %8 10/2021 %R 10.1109/VIS49827.2021.9623322 %0 Conference Paper %B IEEE Virtual Reality and 3D User Interfaces (VR) %D 2021 %T Sensemaking Strategies with Immersive Space to Think %A Lee Lisle %A Kylie Davidson %A Ed Gitre %A North, Chris %A Doug Bowman %B IEEE Virtual Reality and 3D User Interfaces (VR) %P 529-537 %8 03/2021 %R 10.1109/VR50410.2021.00077 %0 Conference Paper %B Interactive Surfaces & Spaces %D 2021 %T Traces of Time through Space: Advantages of Creating Complex Canvases in Collaborative Meetings %A Nurit Kirshenbaum %A Kylie Davidson %A Jesse Harden %A North, Chris %A Jason Leigh %B Interactive Surfaces & Spaces %P 18 pages %8 11/2021 %R https://doi.org/10.1145/3488552 %0 Conference Paper %B SIGCSE 2020 %D 2020 %T Auto-Grading Jupyter Notebooks %A Hamza Manzoor %A Amit Naik %A Shaffer, Clifford A. %A North, Chris %A Stephen H. Edwards %B SIGCSE 2020 %8 03/2020 %0 Journal Article %J IEEE Computer Graphics and Applications %D 2020 %T Challenges in Evaluating Interactive Visual Machine Learning Systems %A N. Boukhelifa %A A. Bezarianos %A et al %B IEEE Computer Graphics and Applications %V 40 %P 88-96 %8 11/2020 %N 6 %R 10.1109/MCG.2020.3017064 %0 Conference Paper %B IEEE VIS Short Papers %D 2020 %T CrowdTrace: Visualizing Provenance in Distributed Sensemaking %A Li, Tianyi %A Belghith, Yasmine %A North, Chris %A Luther, Kurt %B IEEE VIS Short Papers %P 5 pages %8 10/2020 %0 Conference Paper %B IEEE 6th Workshop on Everyday Virtual Reality (WEVR) %D 2020 %T Evaluating the Benefits of the Immersive Space to Think %A Lee Lisle %A Xiaoyu Chen %A Edward J.K. Gitre %A North, Chris %A Bowman, Doug A. %B IEEE 6th Workshop on Everyday Virtual Reality (WEVR) %S WEVR %I IEEE %8 03/2020 %0 Conference Paper %B 4th Workshop on Immersive Analytics at ACM CHI 2020 %D 2020 %T Immersive Space to Think: The Role of 3D Space for Sensemaking %A Payel Bandyopadhyay %A Lee Lisle %A North, Chris %A Bowman, Doug A. %A Polys, Nicholas F. %B 4th Workshop on Immersive Analytics at ACM CHI 2020 %P 8 %8 05/2020 %0 Journal Article %J IEEE Computer %D 2020 %T Interactive Artificial Intelligence: Designing for the "Two Black Boxes" Problem %A Wenskovitch, John %A North, Chris %B IEEE Computer %V 53 %P 29-39 %8 07/2020 %N 8 %R 10.1109/MC.2020.2996416 %0 Journal Article %J Computer Graphics Forum %D 2020 %T Making Sense of Scientific Simulation Ensembles With Semantic Interaction %A Mai Dahshan %A Nicholas Polys %A Richard Jayne %A Ryan Pollyea %B Computer Graphics Forum %V 39 %8 01/2020 %R 10.1111/cgf.14029. %0 Journal Article %J Journal of Engineering, Design and Technology %D 2020 %T Modelling the Effect of Computation Sampling on Insight Error in Computational Fluid Dynamics Scientific Simulation %A Moeti M. Masiane %A Eric Jacques %A Wuchun Feng %A North, Chris %B Journal of Engineering, Design and Technology %P 32 %8 07/2020 %R 10.1108/JEDT-05-2020-0161 %0 Conference Paper %B NeurIPS 2020 Workshop on Human and Model in the Loop Evaluation and Training Strategies (HAMLETS) %D 2020 %T NetReAct: Interactive Learning for Network Summarization %A Amiri, Sorour %A Adhikari, Bijaya %A Wenskovitch, John %A Rodriguez, Alexander %A Michelle Dowling %A North, Chris %A Prakash, Aditya %B NeurIPS 2020 Workshop on Human and Model in the Loop Evaluation and Training Strategies (HAMLETS) %8 12/2020 %0 Conference Paper %B 4th Workshop on Immersive Analytics at ACM CHI 2020 %D 2020 %T The Smart Amplified Group Environment %A Nurit Kirshenbaum %A Dylan Kobayashi %A Mahdi Belcaid %A Jason Leigh %A Luc Renambot %A Andrew Burks %A Krishna Bharadwaj %A Lance Long %A Maxine Brown %A Jason Haga %A North, Chris %B 4th Workshop on Immersive Analytics at ACM CHI 2020 %P 6 %8 05/2020 %0 Conference Paper %B Proceedings of the 25th International Conference on Intelligent User Interfaces %D 2020 %T With Respect to What? Simultaneous Interaction with Dimension Reduction and Clustering Projections %A Wenskovitch, John %A Michelle Dowling %A North, Chris %K clustering %K dimension reduction %K interaction %K Visual Analytics %B Proceedings of the 25th International Conference on Intelligent User Interfaces %S IUI ’20 %I Association for Computing Machinery %C New York, NY, USA %P 177–188 %8 03/2020 %@ 9781450371186 %U https://doi.org/10.1145/3377325.3377516 %R 10.1145/3377325.3377516 %0 Conference Paper %B 2019 Symposium on Visualization in Data Science (VDS’19) %D 2019 %T Albireo: An Interactive Tool for Visually Summarizing Computational Notebook Structure %A Wenskovitch, John %A Zhao, Jian %A Carter, Scott %A Cooper, Matthew %A North, Chris %B 2019 Symposium on Visualization in Data Science (VDS’19) %8 10/2019 %0 Conference Paper %B Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS’19. %D 2019 %T DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning %A Yali Bian %A Wenskovitch, John %A North, Chris %B Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS’19. %8 10/2019 %0 Journal Article %J Proc. ACM Hum.-Comput. Interact. %D 2019 %T Dropping the Baton?: Understanding Errors and Bottlenecks in a Crowdsourced Sensemaking Pipeline %A Li, Tianyi %A Manns, Chandler J. %A North, Chris %A Luther, Kurt %K crowdsourcing %K Intelligence Analysis %K investigations %K mysteries %K sensemaking %K text analytics %X Crowdsourced sensemaking has shown great potential for enabling scalable analysis of complex data sets, from planning trips, to designing products, to solving crimes. Yet, most crowd sensemaking approaches still require expert intervention because of worker errors and bottlenecks that would otherwise harm the output quality. Mitigating these errors and bottlenecks would significantly reduce the burden on experts, yet little is known about the types of mistakes crowds make with sensemaking micro-tasks and how they propagate in the sensemaking loop. In this paper, we conduct a series of studies with 325 crowd workers using a crowd sensemaking pipeline to solve a fictional terrorist plot, focusing on understanding why errors and bottlenecks happen and how they propagate. We classify types of crowd errors and show how the amount and quality of input data influence worker performance. We conclude by suggesting design recommendations for integrated crowdsourcing systems and speculating how a complementary top-down path of the pipeline could refine crowd analyses. %B Proc. ACM Hum.-Comput. Interact. %I ACM %C New York, NY, USA %V 3 %P 136:1–136:26 %8 11/2019 %U http://doi.acm.org/10.1145/3359238 %R 10.1145/3359238 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2019 %T The Effect of Edge Bundling and Seriation on Sensemaking of Biclusters in Bipartite Graphs %A Sun, Maoyuan %A Zhao, Jian %A Hao Wu %A Luther, Kurt %A North, Chris %A Ramakrishnan, Naren %K Bicluster %K bicluster visualizations %K bicluster-based seriation %K Bioinformatics %K Bipartite graph %K bipartite graph based visualizations %K data analysis %K data visualisation %K edge bundles %K edge bundling %K edge crossings %K exploratory data analysis %K graph theory %K Image edge detection %K Layout %K pattern clustering %K product bundles %K seriation %K Visual Analytics %X Exploring coordinated relationships (e.g., shared relationships between two sets of entities) is an important analytics task in a variety of real-world applications, such as discovering similarly behaved genes in bioinformatics, detecting malware collusions in cyber security, and identifying products bundles in marketing analysis. Coordinated relationships can be formalized as biclusters. In order to support visual exploration of biclusters, bipartite graphs based visualizations have been proposed, and edge bundling is used to show biclusters. However, it suffers from edge crossings due to possible overlaps of biclusters, and lacks in-depth understanding of its impact on user exploring biclusters in bipartite graphs. To address these, we propose a novel bicluster-based seriation technique that can reduce edge crossings in bipartite graphs drawing and conducted a user experiment to study the effect of edge bundling and this proposed technique on visualizing biclusters in bipartite graphs. We found that they both had impact on reducing entity visits for users exploring biclusters, and edge bundles helped them find more justified answers. Moreover, we identified four key trade-offs that inform the design of future bicluster visualizations. The study results suggest that edge bundling is critical for exploring biclusters in bipartite graphs, which helps to reduce low-level perceptual problems and support high-level inferences. %B IEEE Transactions on Visualization and Computer Graphics %V 25 %P 2983-2998 %8 07/2019 %N 10 %R 10.1109/TVCG.2018.2861397 %0 Conference Paper %B EValuation of Interactive VisuAl Machine Learning systems, an IEEE VIS 2019 Workshop %D 2019 %T Evaluating Semantic Interaction on Word Embeddings via Simulation %A Yali Bian %A Michelle Dowling %A North, Chris %B EValuation of Interactive VisuAl Machine Learning systems, an IEEE VIS 2019 Workshop %8 10/2019 %0 Journal Article %J Frontiers in Robotics and AI %D 2019 %T Immersive Analytics: Theory and Research Agenda %A Skarbez, Richard %A Polys, Nicholas F. %A Ogle, J. Todd %A North, Chris %A Bowman, Doug A. %X Advances in a variety of computing fields, including “big data,” machine learning, visualization, and augmented/mixed/virtual reality, have combined to give rise to the emerging field of immersive analytics, which investigates how these new technologies support analysis and decision making. Thus far, we feel that immersive analytics research has been somewhat ad hoc, possibly owing to the fact that there is not yet an organizing framework for immersive analytics research. In this paper, we address this lack by proposing a definition for immersive analytics and identifying some general research areas and specific research questions that will be important for the development of this field. We also present three case studies that, while all being examples of what we would consider immersive analytics, present different challenges, and opportunities. These serve to demonstrate the breadth of immersive analytics and illustrate how the framework proposed in this paper applies to practical research. %B Frontiers in Robotics and AI %V 6 %P 82 %8 09/2019 %U https://www.frontiersin.org/article/10.3389/frobt.2019.00082 %R 10.3389/frobt.2019.00082 %0 Journal Article %J COMMUNICATIONS OF THE ACM %D 2019 %T Intelligent Systems for Geosciences: An Essential Research Agenda %A Yolanda Gil %A Suzanne Pierce %B COMMUNICATIONS OF THE ACM %V 62 %P 76-84 %8 01/2019 %N 1 %R DOI:10.1145/3192335 %0 Conference Paper %B VIS 2019 Short Papers %D 2019 %T Interactive Bicluster Aggregation in Bipartite Graphs %A Sun, Maoyuan %A Koop, David %A Zhao, Jian %A North, Chris %A Ramakrishnan, Naren %B VIS 2019 Short Papers %8 10/2019 %0 Journal Article %J Big Data Research %D 2019 %T Interactive Visual Analytics for Sensemaking with Big Text %A Michelle Dowling %A Nathan Wycoff %A Brian Mayer %A Wenskovitch, John %A Leman, Scotland %A House, Leanna %A Nicholas Polys %A North, Chris %A Peter Hauck %K Big data %K interactive visual analytics %K Semantic interaction %K text analytics %K Topic modeling %K visualization %X Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human's spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model's ability to forage as a result of the analyst's synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts' sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles. %B Big Data Research %V 16 %P 49 - 58 %8 July/2019 %U http://www.sciencedirect.com/science/article/pii/S2214579618302995 %R https://doi.org/10.1016/j.bdr.2019.04.003 %0 Conference Paper %B Proceedings of the ACM CHI Conference Workshop on Human-Centered Machine Learning Perspectives at CHI’19. %D 2019 %T Machine Learning from Interaction in Multi-Model Visual Analytics %A Wenskovitch, John %A North, Chris %B Proceedings of the ACM CHI Conference Workshop on Human-Centered Machine Learning Perspectives at CHI’19. %8 04/2019 %0 Conference Paper %B Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS’19. %D 2019 %T Machine Learning from User Interaction for Visualization and Analytics: A Workshop-Generated Research Agenda %A Wenskovitch, John %A Michelle Dowling %A Grose, Laura %A North, Chris %A Chang, Remco %A Endert, Alex %A Rogers, David %B Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS’19. %8 10/2019 %0 Conference Paper %B 2019 Symposium on Visualization in Data Science (VDS’19) %D 2019 %T Pollux: Interactive Cluster-First Projections of High-Dimensional Data %A Wenskovitch, John %A North, Chris %B 2019 Symposium on Visualization in Data Science (VDS’19) %8 10/2019 %0 Conference Paper %B Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion %D 2019 %T Simultaneous Interaction with Dimension Reduction and Clustering Projections %A Wenskovitch, John %A Michelle Dowling %A North, Chris %K clustering %K dimension reduction %K interaction %K Visual Analytics %B Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion %S IUI '19 %I ACM %C New York, NY, USA %P 89–90 %8 03/2019 %@ 978-1-4503-6673-1 %U http://doi.acm.org/10.1145/3308557.3308718 %R 10.1145/3308557.3308718 %0 Journal Article %J Behaviour & Information Technology %D 2019 %T Towards insight-driven sampling for big data visualisation %A Moeti M. Masiane %A Anne Driscoll %A Wuchun Feng %A Wenskovitch, John %A North, Chris %B Behaviour & Information Technology %I Taylor & Francis %P 1-20 %8 05/2019 %U https://doi.org/10.1080/0144929X.2019.1616223 %R 10.1080/0144929X.2019.1616223 %0 Conference Proceedings %B 2019 Symposium on Visualization in Data Science Posters %D 2019 %T Uncertainty in Interactive WMDS Visualizations %A Lata Kodali %A Wenskovitch, John %A Nathan Wycoff %A House, Leanna %A North, Chris %K poster %X Visualizations are useful when learning from high-dimensional data. However, visualizations can be misleading when they do not incorporate measures of uncertainty; e.g., uncertainty from the data or the dimension reduction algorithm used to create the visual display. In our work, we extend a framework called Bayesian Visual Analytics (BaVA) on a dimension reduction algorithm, Weighted Multidimensional Scaling (WMDS), to incorporate uncertainty as analysts explore data visually. BaVA-WMDS visualizations are interactive, and possible interactions include manipulating variable weights and/or the coordinates of the two-dimensional projection. Uncertainty exists in these visualizations on the variable weights, the user interactions, and the fit of WMDS. We quantify these uncertainties using Bayesian models exploring randomness in both coordinates and weights in a method we call Interactive Probabilistic WMDS (IP-WMDS). Specifically, we use posterior estimates to assess fit of WMDS, the range of motion of coordinates, as well as variability in weights. Visually, we display such uncertainty in the form of color and ellipses, and practically, these uncertainties reflect trust in fitting a dimension reduction algorithm. Our results show that these displays of uncertainty highlight different aspects of the visualization, which can help inform analysts. %B 2019 Symposium on Visualization in Data Science Posters %S VDS'19 %C Vancouver, BC, Canada %0 Journal Article %J IEEE Transactions on Learning Technologies %D 2018 %T Be the Data: Embodied Visual Analytics %A Xin Chen %A Self, Jessica Zeitz %A House, Leanna %A Wenskovitch, John %A Sun, Maoyuan %A Nathan Wycoff %A Jane Robertson Evia %A Leman, Scotland %A North, Chris %B IEEE Transactions on Learning Technologies %V 11 %P 81-95 %N 1 %R 10.1109/TLT.2017.2757481 %0 Conference Paper %B VIS Workshop on Machine Learning from User Interaction for Visualization and Analytics %D 2018 %T A Bidirectional Pipeline for Semantic Interaction %A Michelle Dowling %A Wenskovitch, John %A Peter Hauck %A Adam Binford %A Nicholas Polys %A North, Chris %B VIS Workshop on Machine Learning from User Interaction for Visualization and Analytics %8 10/2018 %0 Conference Paper %B CHI '18 Workshop on Sensemaking in a Senseless World %D 2018 %T The Cognitive and Computational Benefits and Limitations of Clustering for Sensemaking %A Wenskovitch, John %A Michelle Dowling %A North, Chris %K clustering %K exploratory data analysis %K interaction %K sensemaking %K tasks %K visualization %X The cognitive process of sensemaking refers to acquiring, representing, and organizing information in order to understand that information. The organization component naturally supports the introduction of clusters, an important enabler for grouping objects such that similar objects are placed in the same cluster. This paper explores the benefits and limitations of introducing clusters into systems for exploratory data analysis. We consider these issues for tasks that the system may support, methods for visualizing and interacting with data in the system, and algorithms that are encoded into the system. We discuss the use of clusters in these systems with respect to cognition and computation, and we call out future areas of research in this area. %B CHI '18 Workshop on Sensemaking in a Senseless World %C Montreal, QC, Canada %8 04/2018 %0 Unpublished Work %D 2018 %T Construction and Usage of the Semantic Interaction Pipeline %A Michelle Dowling %A Wenskovitch, John %A Peter Hauck %A Adam Binford %A Theo Long %A Nicholas Polys %A North, Chris %X Semantic interaction techniques in visual data analytics allow users to indirectly adjust model parameters by directly manipulating the visual output of the models. Many existing tools that support semantic interaction do so with a number of similar features, including using an underlying bidirectional pipeline, using a series of statistical models, and performing inverse computations to transform user interactions into model updates. We propose a visual analytics pipeline that captures these necessary features of semantic interactions. Our flexible, multi-model, bidirectional pipeline has modular functionality to enable rapid prototyping. This enables quick alterations to the type of data being visualized, models for transforming the data, semantic interaction methods, and visual encodings. To demonstrate how this pipeline can be used, we developed a series of applications that employ semantic interactions. We also discuss how the pipeline can be used or extended for future research on semantic interactions in visual analytics. %I InfoVis Lab, Virginia Tech %C Blacksburg, VA %P 1-29 %9 Technical Report %0 Journal Article %J Proc. ACM Hum.-Comput. Interact. %D 2018 %T CrowdIA: Solving Mysteries with Crowdsourced Sensemaking %A Li, Tianyi %A Luther, Kurt %A North, Chris %K crowdsourcing %K Intelligence Analysis %K investigation %K mysteries %K sensemaking %K text analytics %B Proc. ACM Hum.-Comput. Interact. %I Association for Computing Machinery %C New York, NY, USA %V 2 %U https://doi.org/10.1145/3274374 %R 10.1145/3274374 %0 Conference Paper %B CHI 2018 Workshop on Sensemaking in a Senseless World %D 2018 %T Crowdsourcing Intelligence Analysis with Context Slices %A Li, Tianyi %A Asmita Shah %A Luther, Kurt %A North, Chris %B CHI 2018 Workshop on Sensemaking in a Senseless World %8 04/2018 %0 Conference Paper %B 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) %D 2018 %T The Effect of Semantic Interaction on Foraging in Text Analysis %A Wenskovitch, John %A Lauren Bradel %A Michelle Dowling %A House, Leanna %A North, Chris %X 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. %B 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) %0 Journal Article %J ACM Transactions on Knowledge Discovery from Data %D 2018 %T Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models %A Hao Wu %A Sun, Maoyuan %A Peng Mi %A Nikolaj Ta %A North, Chris %A Ramakrishnan, Naren %B ACM Transactions on Knowledge Discovery from Data %V 12 %8 02/2018 %N 1 %R 10.1145/3047017 %0 Journal Article %J ACM Transactions on Interactive Intelligent Systems %D 2018 %T Observation-Level and Parametric Interaction for High-Dimensional Data Analysis %A Self, Jessica Zeitz %A Michelle Dowling %A Wenskovitch, John %A Ian Crandell %A Ming Wang %A House, Leanna %A Leman, Scotland %A North, Chris %B ACM Transactions on Interactive Intelligent Systems %V 8 %8 07/2018 %N 2 %R 10.1145/3158230 %0 Conference Paper %B 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) %D 2018 %T SIRIUS: Dual, Symmetric, Interactive Dimension Reductions %A Michelle Dowling %A Wenskovitch, John %A J.T. Fry %A Leman, Scotland %A House, Leanna %A North, Chris %K attribute projection %K dimension reduction %K exploratory data analysis %K observation projection %K Semantic interaction %X Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights. %B 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) %8 Oct %0 Journal Article %J IEEE Transactions on Visualization & Computer Graphics %D 2018 %T Smooth, Efficient, and Interruptible Zooming and Panning %A Reach, Caleb %A North, Chris %B IEEE Transactions on Visualization & Computer Graphics %8 To appear %0 Generic %D 2018 %T Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics %A Wenskovitch, John %A Ian Crandell %A Ramakrishnan, Naren %A House, Leanna %A Leman, Scotland %A North, Chris %K Algorithm design and analysis %K clustering %K Clustering algorithms %K Data visualization %K Dimension reduction;algorithms %K Manifolds %K Partitioning algorithms %K Visual Analytics %K visualization %B IEEE Transactions on Visualization and Computer Graphics %V 24 %P 131-141 %8 01/2018 %R 10.1109/TVCG.2017.2745258 %0 Conference Paper %B Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference %D 2017 %T Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills %A Self, Jessica Zeitz %A Self, Nathan %A House, Leanna %A Jane Robertson Evia %A Leman, Scotland %A North, Chris %B Proceedings of the 33rd Annual Consortium of Computing Sciences in Colleges (CCSC) Eastern Regional Conference %P 10 %8 10/2017 %0 Generic %D 2017 %T Exploring the Design Space for Cyber Alerts in Context %A Michelle Dowling %A L. Franklin %A M. Feng %A M. Pirrung %A R. Jasper %A J. Cottam %A L. Blaha %I 2017 IEEE Symposium on Visualization for Cyber Security (VizSec) %C Phoenix, AZ %8 10/2017 %9 Poster %0 Conference Paper %B Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics %D 2017 %T Observation-Level Interaction with Clustering and Dimension Reduction Algorithms %A Wenskovitch, John %A North, Chris %K data clustering %K Observation-Level Interaction (OLI) %K Semantic interaction %K sensemaking %K Visual Analytics %B Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics %S HILDA'17 %I ACM %C New York, NY, USA %P 14:1–14:6 %@ 978-1-4503-5029-7 %U http://doi.acm.org/10.1145/3077257.3077259 %R 10.1145/3077257.3077259 %0 Conference Paper %B 2017 IEEE 19th International Conference on High Performance Computing and Communications %D 2017 %T Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis %A Dash, Sajal %A Anshuman Verma %A North, Chris %A Feng, Wu-chun %B 2017 IEEE 19th International Conference on High Performance Computing and Communications %8 12/2017 %R 10.1109/HPCC-SmartCity-DSS.2017.2 %0 Conference Paper %B 2017 IEEE Symposium on Visualization for Cyber Security (VizSec) %D 2017 %T Toward a visualization-supported workflow for cyber alert management using threat models and human-centered design %A L. Franklin %A M. Pirrung %A L. Blaha %A Michelle Dowling %A M. Feng %K analytic process %K Analytical models %K automated decision support %K complex processes %K Computer security %K cyber alert management %K cyber analysts %K cyber network analysts %K data analysis %K data stream monitoring %K data visualisation %K Data visualization %K decision support systems %K Electronic mail %K H.1.2 [Information Systems]: User/Machine Systems — Human Factors %K H.5.2 [Information Interfaces and presentation]: User Interfaces — User Centered Design %K human-centered design %K Interviews %K learning (artificial intelligence) %K machine learning algorithms %K noisy data sets %K potential threats %K prototype visual analytic-supported alert management workflow %K rich data sets %K security of data %K specific data mapping %K support tools %K threat model %K Tools %K visual analytic environments %K visual analytic tools %K visualization designs %K visualization-supported workflow %X Cyber network analysts follow complex processes in their investigations of potential threats to their network. Much research is dedicated to providing automated decision support in the effort to make their tasks more efficient, accurate, and timely. Support tools come in a variety of implementations from machine learning algorithms that monitor streams of data to visual analytic environments for exploring rich and noisy data sets. Cyber analysts, however, need tools which help them merge the data they already have and help them establish appropriate baselines against which to compare anomalies. Furthermore, existing threat models that cyber analysts regularly use to structure their investigation are not often leveraged in support tools. We report on our work with cyber analysts to understand the analytic process and how one such model, the MITRE ATT&CK Matrix [42], is used to structure their analytic thinking. We present our efforts to map specific data needed by analysts into this threat model to inform our visualization designs. We leverage this expert knowledge elicitation to identify a capability gaps that might be filled with visual analytic tools. We propose a prototype visual analytic-supported alert management workflow to aid cyber analysts working with threat models. %B 2017 IEEE Symposium on Visualization for Cyber Security (VizSec) %C Phoenix, AZ %P 1-8 %8 10/2017 %R 10.1109/VIZSEC.2017.8062200 %0 Journal Article %J Informatics %D 2016 %T AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets %A Peng Mi %A Sun, Maoyuan %A Moeti Masiane %A Yong Cao %A North, Chris %B Informatics %7 Special Issue on Information Visualization for Massive Data %I MDPI %V 3 %P 18 %8 10/2016 %U http://www.mdpi.com/2227-9709/3/4/18 %N 4 %R 10.3390/informatics3040018 %0 Conference Paper %B IEEE Virtual Reality 2016 Workshop on Immersive Analytics %D 2016 %T Be the Data: A New Approach for Immersive Analytics %A Xin Chen %A Self, Jessica Zeitz %A House, Leanna %A North, Chris %B IEEE Virtual Reality 2016 Workshop on Immersive Analytics %P 6 %8 03/2016 %0 Conference Paper %B 2016 Annual Meeting of the American Educational Research Association (AERA) %D 2016 %T Be the Data: An Embodied Experience for Data Analytics %A Xin Chen %A House, Leanna %A Self, Jessica Zeitz %A Leman, Scotland %A Jane Robertson Evia %A James Thomas Fry %A North, Chris %B 2016 Annual Meeting of the American Educational Research Association (AERA) %P 20 %8 04/2016 %0 Conference Paper %B International Workshop on Visualization and Collaboration (VisualCol 2016) %D 2016 %T Be the Data: Social Meetings with Visual Analytics %A Xin Chen %A Self, Jessica Zeitz %A Sun, Maoyuan %A House, Leanna %A North, Chris %B International Workshop on Visualization and Collaboration (VisualCol 2016) %P 8 %8 11/2016 %0 Journal Article %J Visualization and Computer Graphics, IEEE Transactions on %D 2016 %T BiSet: Semantic Edge Bundling with Biclusters for Sensemaking %A Sun, Maoyuan %A Peng, Mi %A North, Chris %A Ramakrishnan, Naren %X Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, “in-between”, to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics. %B Visualization and Computer Graphics, IEEE Transactions on %I IEEE %V 22 %P 310-319 %8 01/2016 %N 1 %R 10.1109/TVCG.2015.2467813 %0 Conference Paper %B SIGMOD 2016 Workshop on Human-In-the-Loop Data Analytics (HILDA 2016) %D 2016 %T Bridging the Gap between User Intention and Model Parameters for Data Analytics %A Self, Jessica Zeitz %A Vinayagam, R.K. %A James Thomas Fry %A North, Chris %B SIGMOD 2016 Workshop on Human-In-the-Loop Data Analytics (HILDA 2016) %P 6 %8 06/2016 %0 Conference Paper %B CHI 2016 Workshop on Human-Centered Machine Learning (HCML) %D 2016 %T Designing Usable Interactive Visual Analytics Tools for Dimension Reduction %A Self, Jessica Zeitz %A Hu, Xinran %A House, Leanna %A Leman, Scotland %A North, Chris %B CHI 2016 Workshop on Human-Centered Machine Learning (HCML) %P 7 %8 05/2016 %0 Journal Article %J Informatics %D 2016 %T Interactive Graph Layout of a Million Nodes %A Peng Mi %A Sun, Maoyuan %A Moeti Masiane %A Yong Cao %A North, Chris %B Informatics %7 Special Issue on Information Visualization for Massive Data %V 3 %P 23 %8 12/2016 %U http://www.mdpi.com/2227-9709/3/4/23 %N 4 %R 10.3390/informatics3040023 %0 Conference Paper %B CHI 2016 Workshop on Human Centred Machine Learning %D 2016 %T Usability Challenges underlying Bicluster Interaction for Sensemaking %A Sun, Maoyuan %A Peng Mi %A Hao Wu %A North, Chris %A Ramakrishnan, Naren %B CHI 2016 Workshop on Human Centred Machine Learning %P 6 pages %8 05/2016 %0 Report %D 2015 %T Andromeda: Observation-Level and Parametric Interaction for Exploratory Data Analysis %A Self, Jessica Zeitz %A House, Leanna %A Leman, Scotland %A North, Chris %X Exploring high-dimensional number of dimensions in datasets increases, it becomes harder to discover patterns and develop insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be tweaked to assess the data from multiple perspectives. Alas, tweaking can be difficult for users without a strong knowledge base of the underlying algorithms. We present Andromeda, an interactive visual analytics tool we developed to enable non-experts of statistical models to explore domain- specific, high-dimensional data. This application implements interactive weighted multidimensional scaling (WMDS) and allows for both parametric and observation- level interaction to provide in-depth data exploration. In this paper, we present the results of a controlled usability study assessing Andromeda. We focus on the comparison of parametric interaction, observation-level interaction and a combination of the two. %I Virginia Tech %C Blacksburg %9 Technical Report %0 Conference Paper %B Large Data Analysis and Visualization (LDAV), 2015 IEEE 5th Symposium on %D 2015 %T Bandlimited OLAP cubes for interactive big data visualization %A Reach, Caleb %A North, Chris %X Visualizations backed by data cubes can scale to massive datasets while remaining interactive. However, the use of data cubes introduces artifacts, causing these visualizations to appear noisy at best and deceptive at worst. Moreover, data cubes highly constrain the space of possible visualizations. For example, a histogram backed by a data cube is constrained to have a bin width that is a multiple of the data cube bin size. Similarly, for dynamic queries backed by data cubes, query extents must be aligned with bin boundaries. We present bandlimited OLAP (online analytical processing) cubes (BLOCs), a technique that uses established tools from digital signal processing to generate interactive visualizations of very large datasets. Based on kernel density plots and Gaussian filtering, BLOCs suppress the artifacts that occur in data cubes and allow for a continuous range of zoom/pan positions and continuous dynamic queries. %B Large Data Analysis and Visualization (LDAV), 2015 IEEE 5th Symposium on %I IEEE %C Chicago, IL, USA %P 107-114 %8 10/2015 %R 10.1109/LDAV.2015.7348078 %0 Conference Paper %B IEEE International Symposium on Big Data Visual Analytics %D 2015 %T Big Text Visual Analytics in Sensemaking %A Lauren Bradel %A Nathan Wycoff %A House, Leanna %A North, Chris %B IEEE International Symposium on Big Data Visual Analytics %P 8 pages %8 09/2015 %0 Report %D 2015 %T Bringing Interactive Visual Analytics to the Classroom for Developing EDA Skills %A Self, Jessica Zeitz %A Self, Nathan %A House, Leanna %A Jane Robertson Evia %A Leman, Scotland %A North, Chris %K dimension reduction %K education %K multidimensional scaling %K multivariate analysis %K Visual Analytics %X This paper addresses the use of visual analytics in education for teaching what we call cognitive dimensionality (CD) and other EDA skills. We present the concept of CD to characterize students' capacity for making dimensionally complex insights from data. Using this concept, we build a vocabulary and methodology to support a student’s progression in terms of growth from low cognitive dimensionality (LCD) to high cognitive dimensionality (HCD). Crucially, students do not need high-level math skills to develop HCD. Rather, we use our own tool called Andromeda that enables human-computer interaction with a common, easy to interpret visualization method called Weighted Multidimensional Scaling (WMDS) to promote the idea of making high-dimensional insights. In this paper, we present Andromeda and report findings from a series of classroom assignments to 18 graduate students. These assignments progress from spreadsheet manipulations to statistical software such as R and finally to the use of Andromeda. In parallel with the assignments, we saw students' CD begin low and improve. %I Virginia Tech %C Blacksburg %9 Technical Report %0 Thesis %B Computer Science %D 2015 %T Designing Display Ecologies for Visual Analysis %A Chung, Haeyong %B Computer Science %V Ph.D. %8 02/2015 %0 Report %D 2015 %T Designing for Interactive Dimension Reduction Visual Analytics Tools to Explore High-Dimensional Data %A Self, Jessica Zeitz %A Hu, Xinran %A House, Leanna %A Leman, Scotland %A North, Chris %X Exploring high-dimensional data is challenging. As the number of dimensions in datasets increases, the harder it becomes to discover patterns and develop insights. Dimension reduction algorithms, such as multidimensional scaling, support data explorations by reducing datasets to two dimensions for visualization. Because these algorithms rely on underlying parameterizations, they may be tweaked to assess the data from multiple perspectives. Alas, tweaking can be difficult for users without a strong knowledge base of the underlying algorithms. In this paper, we present principles for developing interactive visual analytic systems that enable users to tweak model parameters directly or indirectly so that they may explore high-dimensional data. To exemplify our principles, we introduce an application that implements interactive weighted multidimensional scaling (WMDS). Our application, Andromeda, allows for both parametric and object-level interaction to provide in-depth data exploration. In this paper, we describe the types of tasks and insights that users may gain with Andromeda. Also, the final version of Andromeda is the result of sequential improvements made to multiple designs that were critiqued by users. With each critique we uncovered design principles of effective, interactive, visual analytic tools. These design principles focus on three main areas: (1) layout, (2) semantically visualizing parameters, and (3) designing the communication between the interface and the algorithm. %I Virginia Tech %C Blacksburg %9 Technical Report %0 Conference Paper %B Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on %D 2015 %T Four considerations for supporting visual analysis in display ecologies %A Chung, Haeyong %A North, Chris %A Joshi, Sarang %A Chen, Jian %X The current proliferation of large displays and mobile devices presents a number of exciting opportunities for visual analytics and information visualization. The display ecology enables multiple displays to function in concert within a broader technological environment to accomplish visual analysis tasks. Based on a comprehensive survey of multi-display systems from a variety of fields, we propose four key considerations for visual analysis in display ecologies: 1) Display Composition, 2) Information Coordination/Transfer, 3) Information Connection, and 4) Display Membership. Different aspects of display ecologies stemming from these design considerations will enable users to transform and empower multiple displays as a display ecology for visual analysis. %B Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on %I IEEE %C Chicago, IL, USA %P 33-40 %8 10/2015 %R 10.1109/VAST.2015.7347628 %0 Journal Article %J IEEE Computer Graphics and Applications %D 2015 %T Semantic Interaction: Coupling Cognition and Computation through Usable Interactive Analytics %A Endert, Alex %A Chang, Remco %A North, Chris %A Zhou, Michelle %B IEEE Computer Graphics and Applications %P 6-11 %8 07/2015 %N July/August %0 Conference Paper %B Proceedings of the 2015 ACM International Workshop on Security and Privacy Analytics %D 2015 %T Visualizing Traffic Causality for Analyzing Network Anomalies %A Zhang, Hao %A Sun, Maoyuan %A Yao, Danfeng %A North, Chris %K anomaly detection %K information visualization %K network traffic analysis %K usable security %K visual locality %B Proceedings of the 2015 ACM International Workshop on Security and Privacy Analytics %S IWSPA '15 %I ACM %C New York, NY, USA %P 37–42 %@ 978-1-4503-3341-2 %U http://doi.acm.org/10.1145/2713579.2713583 %R 10.1145/2713579.2713583 %0 Journal Article %J Information Visualization %D 2014 %T Augmenting the educational curriculum with the Visual Analytics Science and Technology Challenge: Opportunities and pitfalls %A Rohrdantz, Christian %A Mansmann, Florian %A North, Chris %A Keim, Daniel A %X With its mission to move science into practice, the Visual Analytics Science and Technology Challenge has become an integrated part of the annual Visual Analytics Science and Technology Conference since its inception in 2006. In this article, we discuss how we can transfer this objective into a classroom setting by using the Visual Analytics Science and Technology Challenge datasets and by encouraging student submissions to the challenge. By means of Bloom’s Taxonomy of Educational Objectives for Knowledge-Based Goals, we show how the Visual Analytics Science and Technology Challenge enables the integration of additional learning objectives into two types of courses: a dedicated course that focuses on the contest participation and an integrated course that uses the contest data to emphasize practical course elements. The core contribution of this article is that we assess the opportunities and pitfalls that we experienced at the University of Konstanz in Germany and Virginia Tech in the United States when augmenting the educational curriculum with the Visual Analytics Science and Technology Challenge. %B Information Visualization %V 13 %P 313-325 %R 10.1177/1473871613481693 %0 Conference Paper %B VAST Challenge 2014 %D 2014 %T Event-Based Text Visual Analytics %A Wang, Ji %A Lauren Bradel %A North, Chris %K event extraction %K Semantic interaction %K sensemaking %K topic modelling %X We present an event-based approach for solving a directed sensemaking task in which we combine powerful information foraging tools with intuitive synthesis spaces to solve the VAST 2014 Mini-Challenge 1 (MC1). A combination of student-created and commericially available software are used to solve various aspects of the scenario. In addition to applying entitiy extraction and topic modelling, we enable the user to explore a large dataset using multi-model semantic interaction, which infers analytical reasoning from user actions to augment the data spatialization and determine what information should be presented and suggested to the user. Additionally, we visualize extracted topics using Tableau to construct a timeline of events surrounding the questions posed by the challen %B VAST Challenge 2014 %C Paris, France %0 Journal Article %J Visualization and Computer Graphics, IEEE Transactions on %D 2014 %T A Five-Level Design Framework for Bicluster Visualizations %A Sun, Maoyuan %A North, C. %A Ramakrishnan, N. %K bicluster visualizations %K Biclusters %K Bioinformatics %K Cluster approximation %K coordinated relationships %K data analysis %K Data mining %K data visualisation %K design framework %K five-level design framework %K interactive visual analytics %K navigation %K pattern clustering %K Visual Analytics %K visual analytics tools %X Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptibility and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools. %B Visualization and Computer Graphics, IEEE Transactions on %V 20 %P 1713-1722 %8 Dec %N 12 %R 10.1109/TVCG.2014.2346665 %0 Journal Article %J Journal of Intelligent Information Systems %D 2014 %T The human is the loop: new directions for visual analytics %A Endert, Alex %A Hossain, M. Shahriar %A Ramakrishnan, Naren %A North, Chris %A Fiaux, Patrick %A Andrews, Christopher %K clustering %K Semantic interaction %K Spatialization %K Storytelling %K Visual Analytics %X Visual analytics is the science of marrying interactive visualizations and analytic algorithms to support exploratory knowledge discovery in large datasets. We argue for a shift from a ‘human in the loop’ philosophy for visual analytics to a ‘human is the loop’ viewpoint, where the focus is on recognizing analysts’ work processes, and seamlessly fitting analytics into that existing interactive process. We survey a range of projects that provide visual analytic support contextually in the sensemaking loop, and outline a research agenda along with future challenges. %B Journal of Intelligent Information Systems %I Springer US %V 43 %P 411-435 %R 10.1007/s10844-014-0304-9 %0 Report %D 2014 %T Improving Students' Cognitive Dimensionality through Education with Object-Level Interaction %A Self, Jessica Zeitz %A Self, Nathan %A House, Leanna %A Leman, Scotland %A North, Chris %K multivariate data analysis %K object level interaction %K Visual Analytics %X This paper addresses the use of visual analytics techniques in education to advance students' cognitive dimensionality. Students naturally tend to characterize data in simplistic one dimensional terms using metrics such as mean, median, mode. Real- world data, however, is more complex and students need to learn to recognize and create high-dimensional arguments. Data exploration methods can encourage thinking beyond traditional one dimensional insights. In particular, visual analytics tools that afford object-level interaction (OLI) allow for generation of more complex insights, despite inexperience with multivariate data. With these tools, students’ insights are of higher complexity in terms of dimensionality and cardinality and built on more diverse interactions. We present the concept of cognitive dimensionality to characterize students' capacity for dimensionally complex insights. Using this concept, we build a vocabulary and methodology to support a student’s progression in terms of growth from low to high cognitive dimensionality. We report findings from a series of classroom assignments with increasingly complex analysis tools. These assignments progressed from spreadsheet manipulations to statistical software such as R and finally to an OLI application, Andromeda. Our findings suggest that students' cognitive dimensionality can be improved and further research on the impact of visual analytics tools on education for cognitive dimensionality is warranted. %I Virginia Tech %C Blacksburg %9 Technical Report %0 Conference Paper %B VAST Challenge 2014 %D 2014 %T Making Sense of Daily Life Data: From Commonalities To Anomalies %A Wang, Ji %A Peng Mi %A North, Chris %K Geo-visualization %K human information interaction %K Intelligence Analysis %K sense-makingloop %K visualanalysis %X We report the approach and results to solve the VAST 2014 Mini-Challenge 2 (MC2). Based on the commercial interactive visualization software Tableau, we follow the notional model of sensemaking loop for analysis of the massive multi-dimensional, multi-source and time-varying data sets in MC2. Our findings show that we can effectively identify the commonalities and anomalies to understand the GAStech employees' daily life. %B VAST Challenge 2014 %0 Conference Paper %B Proceedings of the 2014 Graphics Interface Conference %D 2014 %T ReCloud: Semantics-based Word Cloud Visualization of User Reviews %A Wang, Ji %A Zhao, Jian %A Guo, Sheng %A North, Chris %A Ramakrishnan, Naren %B Proceedings of the 2014 Graphics Interface Conference %S GI '14 %I Canadian Information Processing Society %C Toronto, Ont., Canada, Canada %P 151–158 %@ 978-1-4822-6003-8 %U http://dl.acm.org/citation.cfm?id=2619648.2619674 %0 Conference Paper %B Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems %D 2014 %T The Role of Interactive Biclusters in Sensemaking %A Sun, Maoyuan %A Lauren Bradel %A North, Chris L. %A Ramakrishnan, Naren %K biclustering %K Intelligence Analysis %K visual interaction %B Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems %S CHI '14 %I ACM %C New York, NY, USA %P 1559–1562 %@ 978-1-4503-2473-1 %U http://doi.acm.org/10.1145/2556288.2557337 %R 10.1145/2556288.2557337 %0 Journal Article %J Computer Graphics and Applications, IEEE %D 2014 %T Semantic Interaction for Visual Analytics: Toward Coupling Cognition and Computation %A Endert, Alex %K Alex Endert %K Analytical models %K Cognition %K computation %K Computational modeling %K computer graphics %K Data models %K Data visualization %K graphics %K human computer interaction %K human-computer interaction %K IN-SPIRE %K Semantic interaction %K Semantics %K Visual Analytics %K visualization %B Computer Graphics and Applications, IEEE %V 34 %P 8-15 %8 July %R 10.1109/MCG.2014.73 %0 Conference Paper %B IEEE Conference on Visual Analytics Science and Technology (VAST) %D 2014 %T StarSpire: Multi-Model Semantic Interaction for Text Analytics %A Lauren Bradel %A North, Chris %A House, Leanna %A Leman, Scotland %X Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. However, this technique is not inherently scalable past hundreds of text documents. To remedy this, we present the concept of multi-model semantic interaction, where semantic interactions can be used to steer multiple models at multiple levels of data scale, enabling users to tackle larger data problems. We also present an updated visualization pipeline model for generalized multi-model semantic interaction. To demonstrate multi-model semantic interaction, we introduce StarSPIRE, a visual text analytics prototype that transforms user interactions on documents into both small-scale display layout updates as well as large-scale relevancy-based document selection. %B IEEE Conference on Visual Analytics Science and Technology (VAST) %I IEEE %C Paris, France %P 1-10 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2014 %T A Survey of Software Frameworks for Cluster-Based Large High-Resolution Displays %A Chung, Haeyong %A Andrews, Christopher %A North, Chris %B IEEE Transactions on Visualization and Computer Graphics %I Institute of Electrical {&} Electronics Engineers ($łbrace$IEEE$\rbrace$) %V 20 %P 1158–1177 %8 8/2014 %R 10.1109/TVCG.2013.272 %0 Conference Paper %B KDD 2014 Workshop on Interactive Data Exploration and Analytics (IDEA) %D 2014 %T Toward Usable Interactive Analytics: Coupling Cognition and Computation %A Endert, Alex %A North, Chris %A Chang, Remco %A Zhou, Michelle %B KDD 2014 Workshop on Interactive Data Exploration and Analytics (IDEA) %U http://poloclub.gatech.edu/idea2014/papers/p52-endert.pdf %0 Conference Paper %B Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems %D 2014 %T Towards Crowd-based Customer Service: A Mixed-initiative Tool for Managing Q&A Sites %A Piccardi, Tiziano %A Convertino, Gregorio %A Zancanaro, Massimo %A Wang, Ji %A Archambeau, Cedric %K a %K crowdsourcing %K customer care %K mixed initiative %K q&\#38 %B Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems %S CHI '14 %I ACM %C New York, NY, USA %P 2725–2734 %@ 978-1-4503-2473-1 %U http://doi.acm.org/10.1145/2556288.2557202 %R 10.1145/2556288.2557202 %0 Journal Article %J Personal and Ubiquitous Computing %D 2014 %T VisPorter: facilitating information sharing for collaborative sensemaking on multiple displays %A Chung, Haeyong %A North, Chris %A Self, Jessica Zeitz %A Chu, Sharon %A Francis Quek %K collaborative sensemaking %K Display ecology %K multiple displays %K text analytics %K Visual Analytics %B Personal and Ubiquitous Computing %I Springer London %V 18 %P 1169–1186 %8 6/2014 %U http://dx.doi.org/10.1007/s00779-013-0727-2 %N 5 %R 10.1007/s00779-013-0727-2 %0 Conference Paper %B 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) %D 2013 %T Auto-Highlighter: Identifying Salient Sentences in Text %A Self, Jessica Zeitz %A Zeitz, Rebecca %A North, Chris %A Breitler, Alan L. %B 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) %I IEEE %C Seattle, WA, USA %P 260 - 262 %8 6/2013 %@ 978-1-4673-6214-6 %R 10.1109/ISI.2013.6578831 %0 Journal Article %J IEEE Computer Graphics and Applications %D 2013 %T Beyond Control Panels: Direct Manipulation for Visual Analytics %A Endert, Alex %A Lauren Bradel %A North, Chris %B IEEE Computer Graphics and Applications %V 33 %P 6 - 13 %8 07/2013 %N 4 %! IEEE Comput. Grap. Appl. %R 10.1109/MCG.2013.53 %0 Journal Article %J Computer %D 2013 %T Bixplorer: Visual Analytics with Biclusters %A Fiaux, Patrick %A Sun, Maoyuan %A Lauren Bradel %A North, Chris %A Ramakrishnan, Naren %A Endert, Alex %B Computer %V 46 %P 90 - 94 %8 08/2013 %N 8 %! Computer %R 10.1109/MC.2013.269 %0 Conference Paper %B Proceedings of the 2Nd ACM International Symposium on Pervasive Displays %D 2013 %T A Comparison of Two Display Models for Collaborative Sensemaking %A Chung, Haeyong %A Chu, Sharon Lynn %A North, Chris %K collaborative sensemaking %K Display ecology %K multiple displays %K Visual Analytics %B Proceedings of the 2Nd ACM International Symposium on Pervasive Displays %S PerDis '13 %I ACM %C New York, NY, USA %P 37–42 %@ 978-1-4503-2096-2 %U http://doi.acm.org/10.1145/2491568.2491577 %R 10.1145/2491568.2491577 %0 Journal Article %J Journal of Computing and Information Science in Engineering %D 2013 %T Developing Large High-Resolution Display Visualizations of High-Fidelity Terrain Data %A Chung, Haeyong %A North, Chris %A Ferris, John %X The vehicle terrain measurement system (VTMS) allows highly detailed terrain modeling and vehicle simulations. Visualization of large-scale terrain datasets taken from VTMS provides better insights into the characteristics of the pavement or road surface. However, the resolution of these terrain datasets greatly exceeds the capability of traditional graphics displays and computer systems. Large high-resolution displays (LHRDs) enable visualization of large-scale VTMS datasets with high resolution, large physical size, scalable rendering performance, advanced interaction methods, and collaboration. This paper investigates beneficial factors, implementation issues, and case study applications of LHRDs for visualizing large, high-fidelity, terrain datasets from VTMS. Two prototype visualizations are designed and evaluated with automotive and pavement engineers to demonstrate effectiveness of LHRDs for multiscale tasks that involve understanding pavement surface details within the overall context of the terrain. %B Journal of Computing and Information Science in Engineering %V 13 %8 2013/07/22 %@ 1530-9827 %U http://dx.doi.org/10.1115/1.4024656 %N 3 %! Journal of Computing and Information Science in Engineering %& 034502 %0 Conference Paper %B CHI '13 Extended Abstracts on Human Factors in Computing Systems %D 2013 %T Fisheye Word Cloud for Temporal Sentiment Exploration %A Wang, Ji %A Dent, Kyle %A North, Chris %K sentiment analysis %K temporal twitter data analysis %K user study %K word cloud %B CHI '13 Extended Abstracts on Human Factors in Computing Systems %S CHI EA '13 %I ACM %C New York, NY, USA %P 1767–1772 %@ 978-1-4503-1952-2 %U http://doi.acm.org/10.1145/2468356.2468673 %R 10.1145/2468356.2468673 %0 Conference Paper %B 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) %D 2013 %T How analysts cognitively “connect the dots” %A Lauren Bradel %A Self, Jessica Zeitz %A Endert, Alex %A Hossain, M. Shahriar %A North, Chris %A Ramakrishnan, Naren %B 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) %I IEEE %C Seattle, WA, USA %P 24 - 26 %8 6/2013 %@ 978-1-4673-6214-6 %R 10.1109/ISI.2013.6578780 %0 Journal Article %J Computers & Graphics %D 2013 %T An image-space energy-saving visualization scheme for \{OLED\} displays %A Haidong Chen %A Wang, Ji %A Weifeng Chen %A Huamin Qu %A Wei Chen %K Illustrative visualization %B Computers & Graphics %V 38 %P 61 - 68 %U http://www.sciencedirect.com/science/article/pii/S0097849313001611 %R http://dx.doi.org/10.1016/j.cag.2013.10.020 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2013 %T The Impact of Physical Navigation on Spatial Organization for Sensemaking %A Andrews, Christopher %A North, Chris %B IEEE Transactions on Visualization and Computer Graphics %V 19 %P 2207 - 2216 %8 12/2013 %N 12 %! IEEE Trans. Visual. Comput. Graphics %R 10.1109/TVCG.2013.205 %0 Journal Article %J International Journal of Human-Computer Studies %D 2013 %T Large High Resolution Displays for Co-Located Collaborative Sensemaking: Display Usage and Territoriality %A Lauren Bradel %A Endert, Alex %A Koch, Kristen %A Andrews, Christopher %A North, Chris %B International Journal of Human-Computer Studies %V 71 %P 1078-1088 %8 11/2013 %N 11 %! International Journal of Human-Computer Studies %R 10.1016/j.ijhcs.2013.07.004 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2013 %T Semantics of Directly Manipulating Spatializations %A Hu, Xinran %A Lauren Bradel %A Maiti, Dipayan %A House, Leanna %A North, Chris %A Leman, Scotland %B IEEE Transactions on Visualization and Computer Graphics %V 19 %P 2052 - 2059 %8 12/2013 %N 12 %! IEEE Trans. Visual. Comput. Graphics %R 10.1109/TVCG.2013.188 %0 Journal Article %J PLoS ONE %D 2013 %T Visual to Parametric Interaction (V2PI) %A Leman, Scotland %A House, Leanna %A Maiti, Dipayan %A Endert, Alex %A North, Chris %X Typical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any information displayed. However, some visualizations mask meaningful data structures when model or algorithm constraints (e.g., parameter specifications) contradict information in the data. Yet, due to the linearity of the pipeline, users do not have a natural means to adjust the displays. In this paper, we present a framework for creating dynamic data displays that rely on both mechanistic data summaries and expert judgement. The key is that we develop both the theory and methods of a new human-data interaction to which we refer as “ Visual to Parametric Interaction” (V2PI). With V2PI, the pipeline becomes bi-directional in that users are embedded in the pipeline; users learn from visualizations and the visualizations adjust to expert judgement. We demonstrate the utility of V2PI and a bi-directional pipeline with two examples. %B PLoS ONE %V 8 %P e50474 %8 03/2013 %N 3 %! PLoS ONE %R 10.1371/journal.pone.0050474 %0 Conference Paper %B 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) %D 2012 %T Analyst's Workspace: An embodied sensemaking environment for large, high-resolution displays %A Andrews, Christopher %A North, Chris %X Distributed cognition and embodiment provide compelling models for how humans think and interact with the environment. Our examination of the use of large, high-resolution displays from an embodied perspective has lead directly to the development of a new sensemaking environment called Analyst's Workspace (AW). AW leverages the embodied resources made more accessible through the physical nature of the display to create a spatial workspace. By combining spatial layout of documents and other artifacts with an entity-centric, explorative investigative approach, AW aims to allow the analyst to externalize elements of the sensemaking process as a part of the investigation, integrated into the visual representations of the data itself. In this paper, we describe the various capabilities of AW and discuss the key principles and concepts underlying its design, emphasizing unique design principles for designing visual analytic tools for large, high-resolution displays. %B 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) %I IEEE %C Seattle, WA, USA %P 123 - 131 %@ 978-1-4673-4752-5 %R 10.1109/VAST.2012.6400559 %0 Conference Paper %B Proceedings of the International Working Conference on Advanced Visual Interfaces %D 2012 %T Designing large high-resolution display workspaces %A Endert, Alex %A Lauren Bradel %A Zeitz, Jessica %A Andrews, Christopher %A North, Chris %K large high-resolution displays %X Large, high-resolution displays have enormous potential to aid in scenarios beyond their current usage. Their current usages are primarily limited to presentations, visualization demonstrations, or conducting experiments. In this paper, we present a new usage for such systems: an everyday workspace. We discuss how seemingly small large-display design decisions can have significant impacts on users' perceptions of these workspaces, and thus the usage of the space. We describe the effects that various physical configurations have on the overall usability and perception of the display. We present conclusions on how to broaden the usage scenarios of large, high-resolution displays to enable frequent and effective usage as everyday workspaces while still allowing transformation to collaborative or presentation spaces. %B Proceedings of the International Working Conference on Advanced Visual Interfaces %S AVI '12 %I ACM %C New York, NY, USA %P 58–65 %@ 978-1-4503-1287-5 %U http://doi.acm.org/10.1145/2254556.2254570 %R 10.1145/2254556.2254570 %0 Conference Paper %B IEEE VAST 2012 (Extended Abstract) (Honorable Mention for Good Use of Coordinated Displays) %D 2012 %T Dynamic Analysis of Large Datasets with Animated and Correlated Views %A Yong Cao %A Reese Moore %A Peng Mi %A Endert, Alex %A North, Chris %A Randy Marchany %X In this paper, we introduce a GPU-accelerated visual analytics tool, AVIST. By adopting the in-situ visualization architecture on the GPUs, AVIST supports real-time data analysis and visualization of massive scale datasets, such as VAST 2012 Challenge dataset. The design objective of the tool is to identify temporal patterns from large and complex data. To achieve this goal, we introduce three unique features: automatic animation, disjunctive data filters, and time-synced visualization of multiple datasets. %B IEEE VAST 2012 (Extended Abstract) (Honorable Mention for Good Use of Coordinated Displays) %0 Unpublished Work %D 2012 %T {GreenVis : Energy-Saving Color Schemes for Sequential Data Visualization on OLED Displays} %A Wang, Ji %A Lin, Xiao %A North, Chris %K color scheme %K energy saving %K oled display %K optimization %I Department of Computer Science, Virginia Tech %C Blacskburg, VA %P 8 %U http://eprints.cs.vt.edu/archive/00001192/ %0 Conference Paper %B Proceedings of the International Working Conference on Advanced Visual Interfaces %D 2012 %T How spatial layout, interactivity, and persistent visibility affect learning with large displays %A Ragan, Eric D. %A Endert, Alex %A Bowman, Doug A. %A Francis Quek %K interactivity %K large displays %K learning %K memory %K use of space %X Visualizations often use spatial representations to aid understanding, but it is unclear what properties of a spatial information presentation are most important to effectively support cognitive processing. This research explores how spatial layout and view control impact learning and investigates the role of persistent visibility when working with large displays. We performed a controlled experiment with a learning activity involving memory and comprehension of a visually represented story. We compared performance between a slideshow-type presentation on a single monitor and a spatially distributed presentation among multiple monitors. We also varied the method of view control (automatic vs. interactive). Additionally, to separate effects due to location or persistent visibility with a spatially distributed layout, we controlled whether all story images could always be seen or if only one image could be viewed at a time. With the distributed layouts, participants maintained better memory of the associated locations where information was presented. However, learning scores were significantly better for the slideshow presentation than for the distributed layout when only one image could be viewed at a time. %B Proceedings of the International Working Conference on Advanced Visual Interfaces %S AVI '12 %I ACM %C New York, NY, USA %P 91–98 %@ 978-1-4503-1287-5 %U http://doi.acm.org/10.1145/2254556.2254576 %R 10.1145/2254556.2254576 %0 Conference Paper %B VAST Challenge 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) %D 2012 %T Pixel-oriented Treemap for multiple displays %A Chung, Haeyong %A Cho, Yong Ju %A Self, Jessica Zeitz %A North, Chris %K large display %K multiple displays %K physical navigation %K pixel-oriented visualization %K treemap %X We have developed a Pixel-oriented Treemap visualization intended for use on multiple displays with collaborating users. It visualizes the health and status of about a million devices with a Treemap layout. In this paper we describe how we found useful pieces of the VAST 2012 Challenge MC1dataset and discuss how users interacted with this visualization during the analysis. %B VAST Challenge 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) %I IEEE %C Seattle, WA, USA %P 289 - 290 %@ 978-1-4673-4752-5 %R 10.1109/VAST.2012.6400512 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2012 %T Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering %A Endert, Alex %A Fiaux, Patrick %A North, Chris %X Visual analytic tools aim to support the cognitively demanding task of sensemaking. Their success often depends on the ability to leverage capabilities of mathematical models, visualization, and human intuition through flexible, usable, and expressive interactions. Spatially clustering data is one effective metaphor for users to explore similarity and relationships between information, adjusting the weighting of dimensions or characteristics of the dataset to observe the change in the spatial layout. Semantic interaction is an approach to user interaction in such spatializations that couples these parametric modifications of the clustering model with users' analytic operations on the data (e.g., direct document movement in the spatialization, highlighting text, search, etc.). In this paper, we present results of a user study exploring the ability of semantic interaction in a visual analytic prototype, ForceSPIRE, to support sensemaking. We found that semantic interaction captures the analytical reasoning of the user through keyword weighting, and aids the user in co-creating a spatialization based on the user's reasoning and intuition. %B IEEE Transactions on Visualization and Computer Graphics %V 18 %P 2879 - 2888 %8 12/2012 %N 12 %! IEEE Trans. Visual. Comput. Graphics %R 10.1109/TVCG.2012.260 %0 Conference Paper %B Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems %D 2012 %T Semantic interaction for visual text analytics %A Endert, Alex %A Fiaux, Patrick %A North, Chris %K interaction %K Visual Analytics %K visualization %X Visual analytics emphasizes sensemaking of large, complex datasets through interactively exploring visualizations generated by statistical models. For example, dimensionality reduction methods use various similarity metrics to visualize textual document collections in a spatial metaphor, where similarities between documents are approximately represented through their relative spatial distances to each other in a 2D layout. This metaphor is designed to mimic analysts' mental models of the document collection and support their analytic processes, such as clustering similar documents together. However, in current methods, users must interact with such visualizations using controls external to the visual metaphor, such as sliders, menus, or text fields, to directly control underlying model parameters that they do not understand and that do not relate to their analytic process occurring within the visual metaphor. In this paper, we present the opportunity for a new design space for visual analytic interaction, called semantic interaction, which seeks to enable analysts to spatially interact with such models directly within the visual metaphor using interactions that derive from their analytic process, such as searching, highlighting, annotating, and repositioning documents. Further, we demonstrate how semantic interactions can be implemented using machine learning techniques in a visual analytic tool, called ForceSPIRE, for interactive analysis of textual data within a spatial visualization. Analysts can express their expert domain knowledge about the documents by simply moving them, which guides the underlying model to improve the overall layout, taking the user's feedback into account. %B Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems %S CHI '12 %I ACM %C New York, NY, USA %P 473–482 %@ 978-1-4503-1015-4 %U http://doi.acm.org/10.1145/2207676.2207741 %R 10.1145/2207676.2207741 %0 Conference Paper %B Proceedings of the International Working Conference on Advanced Visual Interfaces %D 2012 %T The semantics of clustering: analysis of user-generated spatializations of text documents %A Endert, Alex %A Fox, Seth %A Maiti, Dipayan %A Leman, Scotland %A North, Chris %K clustering %K text analytics %K Visual Analytics %K visualization %X Analyzing complex textual datasets consists of identifying connections and relationships within the data based on users' intuition and domain expertise. In a spatial workspace, users can do so implicitly by spatially arranging documents into clusters to convey similarity or relationships. Algorithms exist that spatialize and cluster such information mathematically based on similarity metrics. However, analysts often find inconsistencies in these generated clusters based on their expertise. Therefore, to support sensemaking, layouts must be co-created by the user and the model. In this paper, we present the results of a study observing individual users performing a sensemaking task in a spatial workspace. We examine the users' interactions during their analytic process, and also the clusters the users manually created. We found that specific interactions can act as valuable indicators of important structure within a dataset. Further, we analyze and characterize the structure of the user-generated clusters to identify useful metrics to guide future algorithms. Through a deeper understanding of how users spatially cluster information, we can inform the design of interactive algorithms to generate more meaningful spatializations for text analysis tasks, to better respond to user interactions during the analytics process, and ultimately to allow analysts to more rapidly gain insight. %B Proceedings of the International Working Conference on Advanced Visual Interfaces %S AVI '12 %I ACM %C New York, NY, USA %P 555–562 %@ 978-1-4503-1287-5 %U http://doi.acm.org/10.1145/2254556.2254660 %R 10.1145/2254556.2254660 %0 Conference Paper %B Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on %D 2011 %T Analyst's workspace: Protecting vastopolis %A Andrews, C. %A Hossain, M.S. %A Gad, S. %A Ramakrishnan, N. %A North, C. %K analyst workspace %K Bioterrorism %K Browsers %K computer displays %K data analysis %K data visualisation %K high-resolution displays %K Intelligence Analysis %K large %K Marine animals %K Rivers %K sensemaking environment %K space %K VAST 2011 mini-challenge #3 %K Vastopolis protection %K Visual Analytics %X Analyst's Workspace is a sensemaking environment designed specifically for use of large, high-resolution displays. It employs a spatial workspace to integrate foraging and synthesis activities into a unified process. In this paper we describe how Analyst's Workspace solved the VAST 2011 mini-challenge #3 and discuss some of the unique features of the environment. %B Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on %P 323-324 %8 Oct %R 10.1109/VAST.2011.6102495 %0 Conference Paper %B Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems %D 2011 %T Analytic provenance: process+interaction+insight %A North, Chris %A Chang, Remco %A Endert, Alex %A Dou, Wenwen %A May, Richard %A Pike, Bill %A Fink, G. %K analytic provenance %K user interaction %K Visual Analytics %K visualization %B Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems %S CHI EA '11 %I ACM %C New York, NY, USA %P 33–36 %@ 978-1-4503-0268-5 %G eng %U http://doi.acm.org/10.1145/1979742.1979570 %R http://doi.acm.org/10.1145/1979742.1979570 %0 Conference Paper %B Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems %D 2011 %T ChairMouse: leveraging natural chair rotation for cursor navigation on large, high-resolution displays %A Endert, Alex %A Fiaux, Patrick %A Chung, Haeyong %A Stewart, Michael %A Andrews, Christopher %A North, Chris %K Embodied Interaction %K interaction design %K large display %X Large, high-resolution displays lead to more spatially based approaches. In such environments, the cursor (and hence the physical mouse) is the primary means of interaction. However, usability issues occur when standard mouse interaction is applied to workstations with large size and high pixel density. Previous studies show users navigate physically when interacting with information on large displays by rotating their chair. ChairMouse captures this natural chair movement and translates it into large-scale cursor movement while still maintaining standard mouse usage for local cursor movement. ChairMouse supports both active and passive use, reducing tedious mouse interactions by leveraging physical chair action. %B Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems %S CHI EA '11 %I ACM %C New York, NY, USA %P 571–580 %@ 978-1-4503-0268-5 %U http://doi.acm.org/10.1145/1979742.1979628 %R http://doi.acm.org/10.1145/1979742.1979628 %0 Conference Paper %B INTERACT 2011 %D 2011 %T Co-located Collaborative Sensemaking on a Large High-Resolution Display with Multiple Input Devices %A Katherine Vogt %A Lauren Bradel %A Andrews, Christopher %A North, Chris %A Endert, Alex %A Duke Hutchings %K co-located %K CSCW %K Large High Resolution Display %K large high-resolution display %K sensemaking %K Visual Analytics %B INTERACT 2011 %C Lisbon, Portugal %V 6947 %P 589 - 604 %@ 978-3-642-23771-3 %R 10.1007/978-3-642-23771-3_44 %0 Journal Article %J Information Visualization %D 2011 %T A comparison of benchmark task and insight evaluation methods for information visualization %A North, Chris %A Saraiya, Purvi %A Duca, Karen %B Information Visualization %V 10 %P 162 - 181 %8 07/2011 %N 3 %! Information Visualization %R 10.1177/1473871611415989 %0 Conference Paper %B Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems %D 2011 %T The effects of spatial layout and view control on cognitive processing %A Ragan, Eric D. %A Endert, Alex %A Bowman, Doug A. %A Francis Quek %K information processing %K interactivity %K learning %K spatial memory %K visualization %B Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems %S CHI EA '11 %I ACM %C New York, NY, USA %P 2005–2010 %@ 978-1-4503-0268-5 %G eng %U http://doi.acm.org/10.1145/1979742.1979921 %R http://doi.acm.org/10.1145/1979742.1979921 %0 Conference Paper %B AAAI'11, Workshop on Scalable Integration of Analytics and Visualization (WS-11-17) %D 2011 %T Helping Intelligence Analysts Make Connections %A Hossain, M. Shahriar %A Andrews, Christopher %A Ramakrishnan, Naren %A North, Chris %B AAAI'11, Workshop on Scalable Integration of Analytics and Visualization (WS-11-17) %P 22-31 %0 Journal Article %J Information Visualization %D 2011 %T Information visualization on large, high-resolution displays: Issues, challenges, and opportunities %A Andrews, Christopher %A Endert, Alex %A Yost, Beth %A North, Chris %X Larger, higher-resolution displays are becoming accessible to a greater number of users as display technologies decrease in cost and software for the displays improves. The additional pixels are especially useful for information visualization where scalability has typically been limited by the number of pixels available on a display. But how will visualizations for larger displays need to fundamentally differ from visualizations on desktop displays? Are the basic visualization design principles different? With this potentially new design paradigm comes questions such as whether the relative effectiveness of various graphical encodings are different on large displays, which visualizations and datasets benefit the most, and how interaction with visualizations on large, high-resolution displays will need to change. As we explore these possibilities, we shift away from the technical limitations of scalability imposed by traditional displays (e.g. number of pixels) to studying the human abilities that emerge when these limitations are removed. There is much potential for information visualizations to benefit from large, high-resolution displays, but this potential will only be realized through understanding the interaction between visualization design, perception, interaction techniques, and the display technology. In this paper we present critical design issues and outline some of the challenges and future opportunities for designing visualizations for large, high-resolution displays. We hope that these issues, challenges, and opportunities will provide guidance for future research in this area. %B Information Visualization %V 10 %P 341-355 %U http://ivi.sagepub.com/content/10/4/341.abstract %R 10.1177/1473871611415997 %0 Conference Paper %B Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on %D 2011 %T Observation-level interaction with statistical models for visual analytics %A Endert, Alex %A Chao Han %A Maiti, Dipayan %A House, Leanna %A Leman, Scotland %A North, Chris %K data analysis %K data interactive visual exploration %K data visualisation %K exploratory interaction %K expressive interaction %K generative topographic mapping %K multidimensional scaling %K observation-level interaction %K parameter adjustments %K principal component analysis %K probabilistic principal component analysis %K probability %K sensemaking process %K statistical models %K Visual Analytics %X In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus #x201C;observation #x201D;) in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools. %B Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on %P 121 -130 %8 oct. %R 10.1109/VAST.2011.6102449 %0 Unpublished Work %D 2011 %T Space for Two to Think: Large, High-Resolution Displays for Co-located Collaborative Sensemaking %A Lauren Bradel %A Andrews, Christopher %A Endert, Alex %A Katherine Vogt %A Duke Hutchings %A North, Chris %K collaborative sensemaking %K high-resolution displays %K large %K Large High Resolution Display %K single display groupware %K Visual Analytics %B Technical Report TR-11-11 %I Computer Science, Virginia Tech %0 Conference Paper %B Proceedings of the 8th International Symposium on Visualization for Cyber Security %D 2011 %T Supporting the cyber analytic process using visual history on large displays %A Singh, Ankit %A Lauren Bradel %A Endert, Alex %A Kincaid, Robert %A Andrews, Christopher %A North, Chris %K interaction styles %K large high-resolution displays %K prototyping %K screen design %K user-centered design %B Proceedings of the 8th International Symposium on Visualization for Cyber Security %S VizSec '11 %I ACM %C New York, NY, USA %P 3:1–3:8 %@ 978-1-4503-0679-9 %U http://doi.acm.org/10.1145/2016904.2016907 %R 10.1145/2016904.2016907 %0 Conference Paper %B IEEE Workshop on Interactive Visual Text Analytics for Decision Making at VisWeek 2011 %D 2011 %T Unifying the Sensemaking Loop with Semantic Interaction %A Endert, Alex %A Fiaux, Patrick %A North, Chris %K Visual Analytics %B IEEE Workshop on Interactive Visual Text Analytics for Decision Making at VisWeek 2011 %C Providence, RI %8 10/2011 %0 Conference Paper %B Proceedings of Graphics Interface 2011 %D 2011 %T Visual encodings that support physical navigation on large displays %A Endert, Alex %A Andrews, Christopher %A Lee, Yueh Hua %A North, Chris %K aggregation %K high-resolution display %K information visualization %K large %K perceptual scalability %B Proceedings of Graphics Interface 2011 %S GI '11 %I Canadian Human-Computer Communications Society %C School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada %P 103–110 %@ 978-1-4503-0693-5 %G eng %U http://dl.acm.org/citation.cfm?id=1992917.1992935 %0 Conference Paper %B the 13th International Symposium on Electronic Theses and Dissertations (ETD' 10) %D 2010 %T The Effect of Presenting Long Documents with Large High-Resolution Displays on Comprehension of Content and User Experience %A Yang, S. %A Chung, Haeyong %A North, Chris %A Fox, Edward A. %K Large High Resolution Display %B the 13th International Symposium on Electronic Theses and Dissertations (ETD' 10) %C Austin, TX %8 06/2010 %0 Conference Paper %B CHI '10: Proceedings of the 28th international conference on Human factors in computing systems %D 2010 %T Space to think: large high-resolution displays for sensemaking %A Andrews, Christopher %A Endert, Alex %A North, Chris %K LHRD %B CHI '10: Proceedings of the 28th international conference on Human factors in computing systems %I ACM %C New York, NY, USA %P 55–64 %@ 978-1-60558-929-9 %R http://doi.acm.org/10.1145/1753326.1753336 %0 Conference Paper %B Collaborative Technologies and Systems (CTS), 2010 International Symposium on %D 2010 %T Towards efficient collaboration in cyber security %A Hui, P. %A Bruce, J. %A Fink, G. %A Gregory, M. %A Best, D. %A McGrath, L. %A Endert, Alex %K collaboration %K cyber security analysts %K groupware %K security bulletins %K security of data %B Collaborative Technologies and Systems (CTS), 2010 International Symposium on %P 489 -498 %R 10.1109/CTS.2010.5478473 %0 Conference Paper %B IEEE VAST Conference %D 2010 %T VizCept: Supporting Synchronous Collaboration for Constructing Visualizations in Intelligence Analysis %A Chung, Haeyong %A Yang, S. %A Massjouni, N. %A Andrews, Christopher %A Kanna, R. %A North, Chris %K Visual Analytics %B IEEE VAST Conference %C Salt Lake City, Utah %8 10/2010 %0 Conference Paper %B Computational Science and Engineering, 2009. CSE '09. International Conference on %D 2009 %T Co-located Many-Player Gaming on Large High-Resolution Displays %A Machaj, D. %A Andrews, Christopher %A North, Chris %K colocated many-player gaming %K computer games %K human computer interaction %K interactive devices %K large high-resolution displays %K multiplayer gaming %B Computational Science and Engineering, 2009. CSE '09. International Conference on %V 4 %P 697 -704 %R 10.1109/CSE.2009.65 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2009 %T A Comparison of User-Generated and Automatic Graph Layouts %A Dwyer, Tim %A Lee, Bongshin %A Fisher, Danyel %A Quinn, Kori Inkpen %A Isenberg, Petra %A Robertson, George %A North, Chris %B IEEE Transactions on Visualization and Computer Graphics %I IEEE Educational Activities Department %C Piscataway, NJ, USA %V 15 %P 961–968 %G eng %R http://dx.doi.org/10.1109/TVCG.2009.109 %0 Conference Paper %B 3DUI '09: Proceedings of the 2009 IEEE Symposium on 3D User Interfaces %D 2009 %T A multiscale interaction technique for large, high-resolution displays %A Peck, Sarah M. %A North, Chris %A Bowman, Doug A. %B 3DUI '09: Proceedings of the 2009 IEEE Symposium on 3D User Interfaces %I IEEE Computer Society %C Washington, DC, USA %P 31–38 %@ 978-1-4244-3965-2 %G eng %R http://dx.doi.org/10.1109/3DUI.2009.4811202 %0 Conference Paper %B IEEE VAST 2009 (Extended Abstract) (Awarded Special Contributions to the VAST Challenge Contest) %D 2009 %T Professional Analysts using a Large, High-Resolution Display %A Endert, Alex %A Andrews, Christopher %A North, Chris %K Large High Resolution Display %K Visual Analytics %B IEEE VAST 2009 (Extended Abstract) (Awarded Special Contributions to the VAST Challenge Contest) %0 Journal Article %J Human–Computer Interaction %D 2009 %T Shaping the Display of the Future: The Effects of Display Size and Curvature on User Performance and Insights %A Shupp, Lauren %A Andrews, Christopher %A Dickey-Kurdziolek, Margaret %A Yost, Beth %A North, Chris %K Large High Resolution Display %B Human–Computer Interaction %V 24 %N 1 %0 Conference Paper %B INTERACT '09: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction %D 2009 %T Understanding Multi-touch Manipulation for Surface Computing %A North, Chris %A Dwyer, Tim %A Lee, Bongshin %A Fisher, Danyel %A Isenberg, Petra %A Robertson, George %A Inkpen, Kori %B INTERACT '09: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction %I Springer-Verlag %C Berlin, Heidelberg %P 236–249 %@ 978-3-642-03657-6 %G eng %R http://dx.doi.org/10.1007/978-3-642-03658-3_31 %0 Conference Paper %B Visual Analytics Science and Technology, 2009. IEEE VAST 2009. %D 2009 %T VAST contest dataset use in education %A Whiting, M.A. %A North, Chris %A Endert, Alex %A Scholtz, J. %A Haack, J. %A Varley, C. %A Thomas, J. %K data visualisation %K education %K educational technology %K evaluation metrics %K IEEE visual analytics science and technology %K information analysis %K information analysts %K VAST %K Visual Analytics %B Visual Analytics Science and Technology, 2009. IEEE VAST 2009. %P 115 -122 %R 10.1109/VAST.2009.5333245 %0 Conference Paper %B Visualization for Cyber Security, 2009. VizSec 2009. 6th International Workshop on %D 2009 %T Visualizing cyber security: Usable workspaces %A Fink, G. %A North, Chris %A Endert, Alex %A Rose, S. %K cyber analytics work environment %K cyber security visualization %K data visualisation %K digital infrastructures %K information foraging %K Large High Resolution Display %K security of data %K usability evaluation %K usable workspaces %K Visual Analytics %B Visualization for Cyber Security, 2009. VizSec 2009. 6th International Workshop on %P 45 -56 %R 10.1109/VIZSEC.2009.5375542 %0 Conference Paper %B GI '08: Proceedings of graphics interface 2008 %D 2008 %T The effects of peripheral vision and physical navigation on large scale visualization %A Ball, Robert %A North, Chris %K geospatial %K LHRD %K multidimensional %K physical navigation %B GI '08: Proceedings of graphics interface 2008 %I Canadian Information Processing Society %C Toronto, Ont., Canada, Canada %P 9–16 %@ 978-1-56881-423-0 %G eng %0 Journal Article %J Adv. Eng. Softw. %D 2008 %T Unification of problem solving environment implementation layers with XML-based specifications %A Shu, Jiang %A Watson, Layne T. %A Ramakrishnan, Naren %A Kamke, Frederick A. %A North, Chris %K databases %K problem solving %K XML %B Adv. Eng. Softw. %I Elsevier Science Ltd. %C Oxford, UK, UK %V 39 %P 189–201 %G eng %R http://dx.doi.org/10.1016/j.advengsoft.2007.02.005 %0 Journal Article %D 2008 %T The Value of Information Visualization %A Fekete, Jean-Daniel %A Wijk, Jarke J. %A Stasko, John T. %A North, Chris %K information visualization %I Springer-Verlag %C Berlin, Heidelberg %P 1–18 %@ 978-3-540-70955-8 %G eng %R http://dx.doi.org/10.1007/978-3-540-70956-5_1 %0 Conference Paper %B CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems %D 2007 %T Beyond visual acuity: the perceptual scalability of information visualizations for large displays %A Yost, Beth %A Haciahmetoglu, Yonca %A North, Chris %K information visualization %K LHRD %K Visual Acuity %B CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems %I ACM %C New York, NY, USA %P 101–110 %@ 978-1-59593-593-9 %G eng %R http://doi.acm.org/10.1145/1240624.1240639 %0 Conference Paper %B ACM-SE 45: Proceedings of the 45th annual southeast regional conference %D 2007 %T High-resolution displays enhancing geo-temporal data visualizations %A Booker, John %A Buennemeyer, Timothy %A Sabri, Andrew %A North, Chris %K geospatial %K information visualization %K Intelligence Analysis %K LHRD %B ACM-SE 45: Proceedings of the 45th annual southeast regional conference %I ACM %C New York, NY, USA %P 443–448 %@ 978-1-59593-629-5 %G eng %R http://doi.acm.org/10.1145/1233341.1233421 %0 Journal Article %J Interact. Comput. %D 2007 %T High-resolution gaming: Interfaces, notifications, and the user experience %A Sabri, Andrew %A Ball, Robert %A Fabian, Alain %A Bhatia, Saurabh %A North, Chris %K Games %K LHRD %K Notifications %K User Interfaces %B Interact. Comput. %I Elsevier Science Inc. %C New York, NY, USA %V 19 %P 151–166 %G eng %R http://dx.doi.org/10.1016/j.intcom.2006.08.002 %0 Conference Paper %B CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems %D 2007 %T Move to improve: promoting physical navigation to increase user performance with large displays %A Ball, Robert %A North, Chris %A Bowman, Doug A. %K Embodied Interaction %K LHRD %K physical navigation %K Virtual Navigation %B CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems %I ACM %C New York, NY, USA %P 191–200 %@ 978-1-59593-593-9 %G eng %R http://doi.acm.org/10.1145/1240624.1240656 %0 Journal Article %J Computers & Graphics %D 2007 %T Realizing embodied interaction for visual analytics through large displays %A Ball, Robert %A North, Chris %K Embodied Interaction %K LHRD %K Space Scale %K Visual Analytics %B Computers & Graphics %I Pergamon Press, Inc. %C Elmsford, NY, USA %V 31 %P 380–400 %G eng %R http://dx.doi.org/10.1016/j.cag.2007.01.029 %0 Journal Article %J International Journal of Human-Computer Interaction %D 2007 %T Reflections on Human-Computer Interaction: A special Issue in Honor of Ben Shneiderman's 60th Birthday %A Plaisant, Catherine %A North, Chris %K human computer interaction %B International Journal of Human-Computer Interaction %V 23 %P 195-204 %8 12/2007 %U http://www.cs.umd.edu/hcil/ben60/ %N 3 %R http://dx.doi.org/10.1080/10447310701702766 %0 Journal Article %J Information Visualization %D 2007 %T Workshop report: information visualization-human-centered issues in visual representation, interaction, and evalution %A Kerren, Andreas %A Stasko, John T. %A Fekete, Jean-Daniel %A North, Chris %K Evaluation %K Human-Centered %K information visualization %K Visual Analytics %B Information Visualization %I Palgrave Macmillan %V 6 %P 189–196 %G eng %R http://doi.acm.org/10.1145/1375939.1375941 %0 Conference Paper %B ACM British HCI - Workshop on Visualization & Interaction %D 2006 %T Applying Embodied Interaction and Usability Engineering to Visualization on Large Displays %A Ball, Robert %A Michael DellaNoce %A Ni, Tao %A Francis Quek %A North, Chris %K Embodied Interaction %K information visualization %K LHRD %B ACM British HCI - Workshop on Visualization & Interaction %8 10/2006 %0 Conference Paper %B LISA '06: Proceedings of the 20th conference on Large Installation System Administration %D 2006 %T Bridging the host-network divide: survey, taxonomy, and solution %A Fink, G. %A Duggirala, Vyas %A Correa, Ricardo %A North, Chris %K information visualization %K network security %B LISA '06: Proceedings of the 20th conference on Large Installation System Administration %I USENIX Association %C Berkeley, CA, USA %P 20–20 %G eng %0 Conference Paper %B GI '06: Proceedings of Graphics Interface 2006 %D 2006 %T Evaluation of viewport size and curvature of large, high-resolution displays %A Shupp, Lauren %A Ball, Robert %A Yost, Beth %A Booker, John %A North, Chris %K Evaluation %K LHRD %B GI '06: Proceedings of Graphics Interface 2006 %I Canadian Information Processing Society %C Toronto, Ont., Canada, Canada %P 123–130 %@ 1-56881-308-2 %G eng %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2006 %T An Insight-Based Longitudinal Study of Visual Analytics %A Saraiya, Purvi %A North, Chris %A Lam, Vy %A Duca, Karen %K Evaluation %K GUI %K information visualization %B IEEE Transactions on Visualization and Computer Graphics %I IEEE Educational Activities Department %C Piscataway, NJ, USA %V 12 %P 1511–1522 %G eng %R http://dx.doi.org/10.1109/TVCG.2006.85 %0 Conference Paper %B World Conference on Educational Multimedia/Hypermedia and Educational Telecommunications (ED-MEDIA '06) %D 2006 %T Making a Case for HCI: Exploring Benefits of Visualization for Case Studies %A Brandon Berry %A Laurian Hobby %A McCrickard, D. Scott %A North, Chris %A Manuel A. Pérez-Quiñones %K information visualization %B World Conference on Educational Multimedia/Hypermedia and Educational Telecommunications (ED-MEDIA '06) %C Orlando FL %8 06/2006 %U http://www.cs.vt.edu/node/1328 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2006 %T The Perceptual Scalability of Visualization %A Yost, Beth %A North, Chris %K Evaluation %K information visualization %K LHRD %B IEEE Transactions on Visualization and Computer Graphics %I IEEE Educational Activities Department %C Piscataway, NJ, USA %V 12 %P 837–844 %G eng %R http://dx.doi.org/10.1109/TVCG.2006.184 %0 Conference Paper %B Proceedings of the IEEE conference on Virtual Reality %D 2006 %T A Survey of Large High-Resolution Display Technologies, Techniques, and Applications %A Ni, Tao %A Schmidt, Greg S. %A Staadt, Oliver G. %A Livingston, Mark A. %A Ball, Robert %A May, Richard %K Evaluation %K information visualization %K LHRD %K User Interfaces %B Proceedings of the IEEE conference on Virtual Reality %S VR '06 %I IEEE Computer Society %C Washington, DC, USA %P 223–236 %@ 1-4244-0224-7 %U http://dx.doi.org/10.1109/VR.2006.20 %R http://dx.doi.org/10.1109/VR.2006.20 %0 Conference Proceedings %B IEEE Visualization 2006 %D 2006 %T Is There Science in Visualization? %A T. J. Jankun-Kelly %A Robert Kosara %A Gordon Kindlmann %A North, Chris %A Colin Ware %A E. Wes Bethel %K Evaluation %K information visualization %B IEEE Visualization 2006 %8 2006 %0 Journal Article %J IEEE Comput. Graph. Appl. %D 2006 %T Toward Measuring Visualization Insight %A North, Chris %K Evaluation %K information visualization %B IEEE Comput. Graph. Appl. %I IEEE Computer Society Press %C Los Alamitos, CA, USA %V 26 %P 6–9 %G eng %R http://dx.doi.org/10.1109/MCG.2006.70 %0 Conference Paper %B ACM-SE 44: Proceedings of the 44th annual Southeast regional conference %D 2006 %T Vizability: a tool for usability engineering process improvement through the visualization of usability problem data %A Pyla, Pardha S. %A Howarth, Jonathan R. %A Catanzaro, Chris %A North, Chris %K Evaluation %K Usability %B ACM-SE 44: Proceedings of the 44th annual Southeast regional conference %I ACM %C New York, NY, USA %P 620–625 %@ 1-59593-315-8 %G eng %R http://doi.acm.org/10.1145/1185448.1185584 %0 Conference Paper %B The Tenth IFIP International Conference on Human-Computer Interaction (INTERACT 2005) %D 2005 %T An Analysis of User Behavior on High-Resolution Tiled Displays %A Ball, Robert %A North, Chris %K Large High Resolution Display %K User Behavior %X

The use of multiple monitors for personal desktop computing is becoming more prevalent as the price of display technology decreases. The use of two monitors for a single desktop has been shown to have performance improvement in several studies. However, few studies have been performed with more than three monitors. As a result, we report an observational analysis of the use of a large tiled display containing nine monitors (in a 3x3 matrix). The total resolution of the large display is 3840x3072, for a total of 11,796,480 pixels. Over the course of six months we observed the behavior and actions of five users who used the display extensively as a desktop. We relate our observations, provide feedback concerning common usage of how people do and do not use the display, provide common scenarios and results of interviews, and give a series of design recommendations and guidelines for future designers of applications for high-resolution, tiled displays.

%B The Tenth IFIP International Conference on Human-Computer Interaction (INTERACT 2005) %I Springer Berlin / Heidelberg %8 09/2005 %U http://dx.doi.org/10.1007/11555261_30 %R 10.1007/11555261_30 %0 Journal Article %J Information Visualization %D 2005 %T Bioinformatics visualization: introduction to the special issue %A North, Chris %A Rhyne, Theresa Marie %A Duca, Karen %B Information Visualization %I Palgrave Macmillan %V 4 %P 147–148 %G eng %R http://dx.doi.org/10.1057/palgrave.ivs.9500103 %0 Conference Paper %B CHI '05: CHI '05 extended abstracts on Human factors in computing systems %D 2005 %T Effects of tiled high-resolution display on basic visualization and navigation tasks %A Ball, Robert %A North, Chris %B CHI '05: CHI '05 extended abstracts on Human factors in computing systems %I ACM %C New York, NY, USA %P 1196–1199 %@ 1-59593-002-7 %G eng %R http://doi.acm.org/10.1145/1056808.1056875 %0 Conference Paper %B IASTED International Conference on Human-Computer Interaction %D 2005 %T Evaluating the Benefits of Tiled Displays for Navigating Maps %A Ball, Robert %A Michael Varghese %A Bill Carstensen %A E. Dana Cox %A Chris Fierer %A Matthew Peterson %A North, Chris %K high resolution %K Large High Resolution Display %K maps %K navigation %K Usability %B IASTED International Conference on Human-Computer Interaction %8 11/2005 %0 Book Section %B Handbook of Human Factors and Ergonomics %D 2005 %T Information Visualization %A North, Chris %K information visualization %B Handbook of Human Factors and Ergonomics %7 3rd Edition %I John Wiley & Sons %C New York %P pg. 1222-1246 %& Information Visualization %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2005 %T An Insight-Based Methodology for Evaluating Bioinformatics Visualizations %A Saraiya, Purvi %A North, Chris %A Duca, Karen %B IEEE Transactions on Visualization and Computer Graphics %I IEEE Educational Activities Department %C Piscataway, NJ, USA %V 11 %P 443–456 %G eng %R http://dx.doi.org/10.1109/TVCG.2005.53 %0 Conference Paper %B IV '05: Proceedings of the Ninth International Conference on Information Visualisation %D 2005 %T Learnability of Interactive Coordinated-View Visualizations %A Krishnamoorthy, Sujatha %A North, Chris %B IV '05: Proceedings of the Ninth International Conference on Information Visualisation %I IEEE Computer Society %C Washington, DC, USA %P 306–311 %@ 0-7695-2397-8 %G eng %R http://dx.doi.org/10.1109/IV.2005.70 %0 Conference Paper %B VIZSEC '05: Proceedings of the IEEE Workshops on Visualization for Computer Security %D 2005 %T Root Polar Layout of Internet Address Data for Security Administration %A Fink, G. %A North, Chris %B VIZSEC '05: Proceedings of the IEEE Workshops on Visualization for Computer Security %I IEEE Computer Society %C Washington, DC, USA %P 7 %@ 0-7803-9477-1 %G eng %R http://dx.doi.org/10.1109/VIZSEC.2005.23 %0 Conference Paper %B CHI '05: CHI '05 extended abstracts on Human factors in computing systems %D 2005 %T Single complex glyphs versus multiple simple glyphs %A Yost, Beth %A North, Chris %B CHI '05: CHI '05 extended abstracts on Human factors in computing systems %I ACM %C New York, NY, USA %P 1889–1892 %@ 1-59593-002-7 %G eng %R http://doi.acm.org/10.1145/1056808.1057048 %0 Conference Paper %B Compendium of IEEE Symposium on Information Visualization (InfoVis 2005) %D 2005 %T Tracking User Navigation and Performance on High-Resolution Displays using a Dynamic Real-Time Strategy Game %A Ball, Robert %A Sabri, Andrew %A Michael Varghese %A North, Chris %K game %K Large High Resolution Display %K navigation %K performance %B Compendium of IEEE Symposium on Information Visualization (InfoVis 2005) %8 10/2005 %0 Conference Paper %B VIZSEC '05: Proceedings of the IEEE Workshops on Visualization for Computer Security %D 2005 %T Visual Correlation of Host Processes and Network Traffic %A Fink, G. %A Muessig, Paul %A North, Chris %B VIZSEC '05: Proceedings of the IEEE Workshops on Visualization for Computer Security %I IEEE Computer Society %C Washington, DC, USA %P 2 %@ 0-7803-9477-1 %G eng %R http://dx.doi.org/10.1109/VIZSEC.2005.18 %0 Conference Paper %B INFOVIS '05: Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization %D 2005 %T Visualization of Graphs with Associated Timeseries Data %A Saraiya, Purvi %A Lee, Peter %A North, Chris %B INFOVIS '05: Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization %I IEEE Computer Society %C Washington, DC, USA %P 30 %@ 0-7803-9464-x %G eng %R http://dx.doi.org/10.1109/INFOVIS.2005.37 %0 Journal Article %J Information Visualization %D 2005 %T Visualizing biological pathways: requirements analysis, systems evaluation and research agenda %A Saraiya, Purvi %A North, Chris %A Duca, Karen %B Information Visualization %I Palgrave Macmillan %V 4 %P 191–205 %G eng %R http://dx.doi.org/10.1057/palgrave.ivs.9500102 %0 Conference Paper %B SIGCSE '04: Proceedings of the 35th SIGCSE technical symposium on Computer science education %D 2004 %T Effective features of algorithm visualizations %A Saraiya, Purvi %A Shaffer, Clifford A. %A McCrickard, D. Scott %A North, Chris %B SIGCSE '04: Proceedings of the 35th SIGCSE technical symposium on Computer science education %I ACM %C New York, NY, USA %P 382–386 %@ 1-58113-798-2 %G eng %R http://doi.acm.org/10.1145/971300.971432 %0 Conference Paper %B INFOVIS '04: Proceedings of the IEEE Symposium on Information Visualization %D 2004 %T An Evaluation of Microarray Visualization Tools for Biological Insight %A Saraiya, Purvi %A North, Chris %A Duca, Karen %B INFOVIS '04: Proceedings of the IEEE Symposium on Information Visualization %I IEEE Computer Society %C Washington, DC, USA %P 1–8 %@ 0-7803-8779-3 %G eng %R http://dx.doi.org/10.1109/INFOVIS.2004.5 %0 Conference Paper %B VizSEC/DMSEC '04: Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security %D 2004 %T Home-centric visualization of network traffic for security administration %A Ball, Robert %A Fink, G. %A North, Chris %B VizSEC/DMSEC '04: Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security %I ACM %C New York, NY, USA %P 55–64 %@ 1-58113-974-8 %G eng %R http://doi.acm.org/10.1145/1029208.1029217 %0 Conference Paper %B Web3D '04: Proceedings of the ninth international conference on 3D Web technology %D 2004 %T PathSim visualizer: an Information-Rich Virtual Environment framework for systems biology %A Polys, Nicholas F. %A Bowman, Doug A. %A North, Chris %A Laubenbacher, Reinhard %A Duca, Karen %B Web3D '04: Proceedings of the ninth international conference on 3D Web technology %I ACM %C New York, NY, USA %P 7–14 %@ 1-58113-845-8 %G eng %R http://doi.acm.org/10.1145/985040.985042 %0 Conference Paper %B CHI '03: CHI '03 extended abstracts on Human factors in computing systems %D 2003 %T Dynamic query sliders vs. brushing histograms %A Li, Qing %A Bao, Xiaofeng %A Song, Chen %A Zhang, Jinfei %A North, Chris %B CHI '03: CHI '03 extended abstracts on Human factors in computing systems %I ACM %C New York, NY, USA %P 834–835 %@ 1-58113-637-4 %G eng %R http://doi.acm.org/10.1145/765891.766020 %0 Conference Paper %B CMV '03: Proceedings of the conference on Coordinated and Multiple Views In Exploratory Visualization %D 2003 %T Exploring Context Switching and Cognition in Dual-View Coordinated Visualizations %A Convertino, Gregorio %A Chen, Jian %A Yost, Beth %A Ryu, Young-Sam %A North, Chris %B CMV '03: Proceedings of the conference on Coordinated and Multiple Views In Exploratory Visualization %I IEEE Computer Society %C Washington, DC, USA %P 55 %@ 0-7695-2001-4 %G eng %0 Conference Paper %B CHI '03: CHI '03 extended abstracts on Human factors in computing systems %D 2003 %T Fusion: interactive coordination of diverse data, visualizations, and mining algorithms %A North, Chris %A Conklin, Nathan %A Indukuri, Kiran %A Saini, Varun %A Yu, Qiang %B CHI '03: CHI '03 extended abstracts on Human factors in computing systems %I ACM %C New York, NY, USA %P 626–627 %@ 1-58113-637-4 %G eng %R http://doi.acm.org/10.1145/765891.765897 %0 Conference Paper %B VRST '03: Proceedings of the ACM symposium on Virtual reality software and technology %D 2003 %T Information-rich virtual environments: theory, tools, and research agenda %A Bowman, Doug A. %A North, Chris %A Chen, Jian %A Polys, Nicholas F. %A Pyla, Pardha S. %A Yilmaz, Umur %B VRST '03: Proceedings of the ACM symposium on Virtual reality software and technology %I ACM %C New York, NY, USA %P 81–90 %@ 1-58113-569-6 %G eng %R http://doi.acm.org/10.1145/1008653.1008669 %0 Conference Paper %B CHI '02: CHI '02 extended abstracts on Human factors in computing systems %D 2002 %T Breakdown visualization: multiple foci polyarchies of values and attributes %A Prabhakar, Sandeep %A Conklin, Nathan %A North, Chris %A Thirunavukkarasu, Muthukumar %A Dandapani, Anusha %A Panchanathan, Ganesh %B CHI '02: CHI '02 extended abstracts on Human factors in computing systems %I ACM %C New York, NY, USA %P 800–801 %@ 1-58113-454-1 %G eng %R http://doi.acm.org/10.1145/506443.506604 %0 Conference Paper %B JCDL '02: Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries %D 2002 %T Enhancing the ENVISION interface for digital libraries %A Wang, Jun %A Agrawal, Abhishek %A Bazaza, Anil %A Angle, Supriya %A Fox, Edward A. %A North, Chris %B JCDL '02: Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries %I ACM %C New York, NY, USA %P 275–276 %@ 1-58113-513-0 %G eng %R http://doi.acm.org/10.1145/544220.544279 %0 Conference Paper %B VISSYM '02: Proceedings of the symposium on Data Visualisation 2002 %D 2002 %T An evaluation of information visualization in attention-limited environments %A Somervell, Jacob %A McCrickard, D. Scott %A North, Chris %A Shukla, Maulik %B VISSYM '02: Proceedings of the symposium on Data Visualisation 2002 %I Eurographics Association %C Aire-la-Ville, Switzerland, Switzerland %P 211–216 %@ 1-58113-536-X %G eng %0 Conference Paper %B VIS '02: Proceedings of the conference on Visualization '02 %D 2002 %T Evolving visual metaphors and dynamic tools for bioinformatics visualization %A Rhyne, Theresa Marie %A Dunning,Jr., Thomas H. %A Calapristi, Gus %A North, Chris %A Gresh, Donna %B VIS '02: Proceedings of the conference on Visualization '02 %I IEEE Computer Society %C Washington, DC, USA %P 579–582 %@ 0-7803-7498-3 %G eng %0 Conference Paper %B INFOVIS '02: Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02) %D 2002 %T Multiple Foci Drill-Down through Tuple and Attribute Aggregation Polyarchies in Tabular Data %A Conklin, Nathan %A Prabhakar, Sandeep %A North, Chris %B INFOVIS '02: Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02) %I IEEE Computer Society %C Washington, DC, USA %P 131 %@ 0-7695-1751-X %G eng %0 Conference Paper %B CHI '02: CHI '02 extended abstracts on Human factors in computing systems %D 2002 %T An ordering of secondary task display attributes %A Tessendorf, David %A Chewar, C. M. %A Ndiwalana, Ali %A Pryor, Jon %A McCrickard, D. Scott %A North, Chris %B CHI '02: CHI '02 extended abstracts on Human factors in computing systems %I ACM %C New York, NY, USA %P 600–601 %@ 1-58113-454-1 %G eng %R http://doi.acm.org/10.1145/506443.506503 %0 Conference Paper %B VIS '02: Proceedings of the conference on Visualization '02 %D 2002 %T A radial focus+context visualization for multi-dimensional functions %A Jayaraman, Sanjini %A North, Chris %B VIS '02: Proceedings of the conference on Visualization '02 %I IEEE Computer Society %C Washington, DC, USA %P 443–450 %@ 0-7803-7498-3 %G eng %0 Conference Paper %B VISSYM '02: Proceedings of the symposium on Data Visualisation 2002 %D 2002 %T Secondary task display attributes: optimizing visualizations for cognitive task suitability and interference avoidance %A Chewar, C. M. %A McCrickard, D. Scott %A Ndiwalana, Ali %A North, Chris %A Pryor, Jon %A Tessendorf, David %B VISSYM '02: Proceedings of the symposium on Data Visualisation 2002 %I Eurographics Association %C Aire-la-Ville, Switzerland, Switzerland %P 165–171 %@ 1-58113-536-X %G eng %0 Journal Article %J Information Visualization %D 2002 %T Visualization schemas and a web-based architecture for custom multiple-view visualization of multiple-table databases %A North, Chris %A Conklin, Nathan %A Indukuri, Kiran %A Saini, Varun %B Information Visualization %I Palgrave Macmillan %V 1 %P 211–228 %G eng %R http://dx.doi.org/10.1057/palgrave.ivs.9500020 %0 Conference Paper %B INFOVIS '02: Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02) %D 2002 %T Visualization Schemas for Flexible Information Visualization %A North, Chris %A Conklin, Nathan %A Saini, Varun %B INFOVIS '02: Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02) %I IEEE Computer Society %C Washington, DC, USA %P 15 %@ 0-7695-1751-X %G eng %0 Conference Paper %B CHI '01: CHI '01 extended abstracts on Human factors in computing systems %D 2001 %T Component-based, user-constructed, multiple-view visualization %A North, Chris %A Shneiderman, Ben %B CHI '01: CHI '01 extended abstracts on Human factors in computing systems %I ACM %C New York, NY, USA %P 201–202 %@ 1-58113-340-5 %G eng %R http://doi.acm.org/10.1145/634067.634188 %0 Conference Paper %B AVI '00: Proceedings of the working conference on Advanced visual interfaces %D 2000 %T Snap-together visualization: a user interface for coordinating visualizations via relational schemata %A North, Chris %A Shneiderman, Ben %B AVI '00: Proceedings of the working conference on Advanced visual interfaces %I ACM %C New York, NY, USA %P 128–135 %@ 1-58113-252-2 %G eng %R http://doi.acm.org/10.1145/345513.345282 %0 Journal Article %J Int. J. Hum.-Comput. Stud. %D 2000 %T Snap-together visualization: can users construct and operate coordinated visualizations? %A North, Chris %A Shneiderman, Ben %B Int. J. Hum.-Comput. Stud. %I Academic Press, Inc. %C Duluth, MN, USA %V 53 %P 715–739 %G eng %R http://dx.doi.org/10.1006/ijhc.2000.0418 %0 Thesis %D 2000 %T A user interface for coordinating visualizations based on relational schemata: snap-together visualization %A North, Chris %I University of Maryland at College Park %C College Park, MD, USA %@ 0-599-72609-1 %G eng %9 phd %0 Conference Paper %B NPIVM '99: Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management %D 1999 %T Temporal, geographical and categorical aggregations viewed through coordinated displays: a case study with highway incident data %A Fredrikson, Anna %A North, Chris %A Plaisant, Catherine %A Shneiderman, Ben %B NPIVM '99: Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management %I ACM %C New York, NY, USA %P 26–34 %@ 1-58113-254-9 %G eng %R http://doi.acm.org/10.1145/331770.331780 %0 Journal Article %D 1999 %T User controlled overviews of an image library: a case study of the visible human %A North, Chris %A Shneiderman, Ben %A Plaisant, Catherine %I Morgan Kaufmann Publishers Inc. %C San Francisco, CA, USA %P 570–578 %@ 1-55860-533-9 %G eng %0 Conference Paper %B CHI '98: CHI 98 conference summary on Human factors in computing systems %D 1998 %T Robust, end-user programmable, multiple-window coordination %A North, Chris %B CHI '98: CHI 98 conference summary on Human factors in computing systems %I ACM %C New York, NY, USA %P 60–61 %@ 1-58113-028-7 %G eng %R http://doi.acm.org/10.1145/286498.286529 %0 Conference Paper %B CHI '96: Conference companion on Human factors in computing systems %D 1996 %T Browsing anatomical image databases: a case study of the visible human %A North, Chris %A Korn, Flip %B CHI '96: Conference companion on Human factors in computing systems %I ACM %C New York, NY, USA %P 414–415 %@ 0-89791-832-0 %G eng %R http://doi.acm.org/10.1145/257089.257406 %0 Conference Paper %B DL '96: Proceedings of the first ACM international conference on Digital libraries %D 1996 %T User controlled overviews of an image library: a case study of the visible human %A North, Chris %A Shneiderman, Ben %A Plaisant, Catherine %B DL '96: Proceedings of the first ACM international conference on Digital libraries %I ACM %C New York, NY, USA %P 74–82 %@ 0-89791-830-4 %G eng %R http://doi.acm.org/10.1145/226931.226946