TY - CONF T1 - SAGE3: Smart Amplified Group Environment T2 - Gateways 2023 Y1 - 2023 A1 - Roderick Tabalba A1 - Nurit Kirshenbaum A1 - Jesse Harden A1 - Christman, Elizabeth A1 - Mahdi Belcaid A1 - North, Chris A1 - Jason Leigh A1 - et al. JF - Gateways 2023 ER - TY - CONF T1 - Spaces to Think: A Comparison of Small, Large, and Immersive Displays for the Sensemaking Process T2 - IEEE International Symposium on Mixed and Augmented Reality (ISMAR) Y1 - 2023 A1 - Lee Lisle A1 - Kylie Davidson A1 - Leonardo Pavanatto Soares A1 - Tahmid, Ibrahim A. A1 - North, Chris A1 - Bowman, Doug A. JF - IEEE International Symposium on Mixed and Augmented Reality (ISMAR) ER - TY - JOUR T1 - A Survey on Event-Based News Narrative Extraction JF - ACM Computing Surveys Y1 - 2023 A1 - Norambuena, Brian Felipe Keith A1 - Tanu Mitra A1 - North, Chris VL - 55 IS - 14s ER - TY - CONF T1 - Semantic Explanation of Interactive Dimensionality Reduction T2 - IEEE Visualization Conference (VIS) Y1 - 2021 A1 - Yali Bian A1 - North, Chris A1 - Eric Krokos A1 - Sarah Joseph JF - IEEE Visualization Conference (VIS) ER - TY - CONF T1 - Sensemaking Strategies with Immersive Space to Think T2 - IEEE Virtual Reality and 3D User Interfaces (VR) Y1 - 2021 A1 - Lee Lisle A1 - Kylie Davidson A1 - Ed Gitre A1 - North, Chris A1 - Doug Bowman JF - IEEE Virtual Reality and 3D User Interfaces (VR) ER - TY - CONF T1 - The Smart Amplified Group Environment T2 - 4th Workshop on Immersive Analytics at ACM CHI 2020 Y1 - 2020 A1 - Nurit Kirshenbaum A1 - Dylan Kobayashi A1 - Mahdi Belcaid A1 - Jason Leigh A1 - Luc Renambot A1 - Andrew Burks A1 - Krishna Bharadwaj A1 - Lance Long A1 - Maxine Brown A1 - Jason Haga A1 - North, Chris JF - 4th Workshop on Immersive Analytics at ACM CHI 2020 ER - TY - CONF T1 - Simultaneous Interaction with Dimension Reduction and Clustering Projections T2 - Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion Y1 - 2019 A1 - Wenskovitch, John A1 - Michelle Dowling A1 - North, Chris KW - clustering KW - dimension reduction KW - interaction KW - Visual Analytics JF - Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion T3 - IUI '19 PB - ACM CY - New York, NY, USA SN - 978-1-4503-6673-1 UR - http://doi.acm.org/10.1145/3308557.3308718 ER - TY - CONF T1 - SIRIUS: Dual, Symmetric, Interactive Dimension Reductions T2 - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) Y1 - 2018 A1 - Michelle Dowling A1 - Wenskovitch, John A1 - J.T. Fry A1 - Leman, Scotland A1 - House, Leanna A1 - North, Chris KW - attribute projection KW - dimension reduction KW - exploratory data analysis KW - observation projection KW - Semantic interaction AB - 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. JF - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) ER - TY - JOUR T1 - Smooth, Efficient, and Interruptible Zooming and Panning JF - IEEE Transactions on Visualization & Computer Graphics Y1 - 2018 A1 - Reach, Caleb A1 - North, Chris ER - TY - JOUR T1 - Semantic Interaction: Coupling Cognition and Computation through Usable Interactive Analytics JF - IEEE Computer Graphics and Applications Y1 - 2015 A1 - Endert, Alex A1 - Chang, Remco A1 - North, Chris A1 - Zhou, Michelle IS - July/August ER - TY - JOUR T1 - Semantic Interaction for Visual Analytics: Toward Coupling Cognition and Computation JF - Computer Graphics and Applications, IEEE Y1 - 2014 A1 - Endert, Alex KW - Alex Endert KW - Analytical models KW - Cognition KW - computation KW - Computational modeling KW - computer graphics KW - Data models KW - Data visualization KW - graphics KW - human computer interaction KW - human-computer interaction KW - IN-SPIRE KW - Semantic interaction KW - Semantics KW - Visual Analytics KW - visualization VL - 34 ER - TY - CONF T1 - StarSpire: Multi-Model Semantic Interaction for Text Analytics T2 - IEEE Conference on Visual Analytics Science and Technology (VAST) Y1 - 2014 A1 - Lauren Bradel A1 - North, Chris A1 - House, Leanna A1 - Leman, Scotland AB - 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. JF - IEEE Conference on Visual Analytics Science and Technology (VAST) PB - IEEE CY - Paris, France ER - TY - JOUR T1 - A Survey of Software Frameworks for Cluster-Based Large High-Resolution Displays JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2014 A1 - Chung, Haeyong A1 - Andrews, Christopher A1 - North, Chris PB - Institute of Electrical {&} Electronics Engineers ($łbrace$IEEE$\rbrace$) VL - 20 ER - TY - JOUR T1 - Semantics of Directly Manipulating Spatializations JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2013 A1 - Hu, Xinran A1 - Lauren Bradel A1 - Maiti, Dipayan A1 - House, Leanna A1 - North, Chris A1 - Leman, Scotland VL - 19 IS - 12 JO - IEEE Trans. Visual. Comput. Graphics ER - TY - JOUR T1 - Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2012 A1 - Endert, Alex A1 - Fiaux, Patrick A1 - North, Chris AB - 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. VL - 18 IS - 12 JO - IEEE Trans. Visual. Comput. Graphics ER - TY - CONF T1 - Semantic interaction for visual text analytics T2 - Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems Y1 - 2012 A1 - Endert, Alex A1 - Fiaux, Patrick A1 - North, Chris KW - interaction KW - Visual Analytics KW - visualization AB - 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. JF - Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems T3 - CHI '12 PB - ACM CY - New York, NY, USA SN - 978-1-4503-1015-4 UR - http://doi.acm.org/10.1145/2207676.2207741 ER - TY - CONF T1 - The semantics of clustering: analysis of user-generated spatializations of text documents T2 - Proceedings of the International Working Conference on Advanced Visual Interfaces Y1 - 2012 A1 - Endert, Alex A1 - Fox, Seth A1 - Maiti, Dipayan A1 - Leman, Scotland A1 - North, Chris KW - clustering KW - text analytics KW - Visual Analytics KW - visualization AB - 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. JF - Proceedings of the International Working Conference on Advanced Visual Interfaces T3 - AVI '12 PB - ACM CY - New York, NY, USA SN - 978-1-4503-1287-5 UR - http://doi.acm.org/10.1145/2254556.2254660 ER - TY - UNPB T1 - Space for Two to Think: Large, High-Resolution Displays for Co-located Collaborative Sensemaking Y1 - 2011 A1 - Lauren Bradel A1 - Andrews, Christopher A1 - Endert, Alex A1 - Katherine Vogt A1 - Duke Hutchings A1 - North, Chris KW - collaborative sensemaking KW - high-resolution displays KW - large KW - Large High Resolution Display KW - single display groupware KW - Visual Analytics JF - Technical Report TR-11-11 PB - Computer Science, Virginia Tech ER - TY - CONF T1 - Supporting the cyber analytic process using visual history on large displays T2 - Proceedings of the 8th International Symposium on Visualization for Cyber Security Y1 - 2011 A1 - Singh, Ankit A1 - Lauren Bradel A1 - Endert, Alex A1 - Kincaid, Robert A1 - Andrews, Christopher A1 - North, Chris KW - interaction styles KW - large high-resolution displays KW - prototyping KW - screen design KW - user-centered design JF - Proceedings of the 8th International Symposium on Visualization for Cyber Security T3 - VizSec '11 PB - ACM CY - New York, NY, USA SN - 978-1-4503-0679-9 UR - http://doi.acm.org/10.1145/2016904.2016907 ER - TY - CONF T1 - Space to think: large high-resolution displays for sensemaking T2 - CHI '10: Proceedings of the 28th international conference on Human factors in computing systems Y1 - 2010 A1 - Andrews, Christopher A1 - Endert, Alex A1 - North, Chris KW - LHRD JF - CHI '10: Proceedings of the 28th international conference on Human factors in computing systems PB - ACM CY - New York, NY, USA SN - 978-1-60558-929-9 ER - TY - JOUR T1 - Shaping the Display of the Future: The Effects of Display Size and Curvature on User Performance and Insights JF - Human–Computer Interaction Y1 - 2009 A1 - Shupp, Lauren A1 - Andrews, Christopher A1 - Dickey-Kurdziolek, Margaret A1 - Yost, Beth A1 - North, Chris KW - Large High Resolution Display VL - 24 IS - 1 ER - TY - CONF T1 - A Survey of Large High-Resolution Display Technologies, Techniques, and Applications T2 - Proceedings of the IEEE conference on Virtual Reality Y1 - 2006 A1 - Ni, Tao A1 - Schmidt, Greg S. A1 - Staadt, Oliver G. A1 - Livingston, Mark A. A1 - Ball, Robert A1 - May, Richard KW - Evaluation KW - information visualization KW - LHRD KW - User Interfaces JF - Proceedings of the IEEE conference on Virtual Reality T3 - VR '06 PB - IEEE Computer Society CY - Washington, DC, USA SN - 1-4244-0224-7 UR - http://dx.doi.org/10.1109/VR.2006.20 ER - TY - CONF T1 - Single complex glyphs versus multiple simple glyphs T2 - CHI '05: CHI '05 extended abstracts on Human factors in computing systems Y1 - 2005 A1 - Yost, Beth A1 - North, Chris JF - CHI '05: CHI '05 extended abstracts on Human factors in computing systems PB - ACM CY - New York, NY, USA SN - 1-59593-002-7 ER - TY - CONF T1 - Secondary task display attributes: optimizing visualizations for cognitive task suitability and interference avoidance T2 - VISSYM '02: Proceedings of the symposium on Data Visualisation 2002 Y1 - 2002 A1 - Chewar, C. M. A1 - McCrickard, D. Scott A1 - Ndiwalana, Ali A1 - North, Chris A1 - Pryor, Jon A1 - Tessendorf, David JF - VISSYM '02: Proceedings of the symposium on Data Visualisation 2002 PB - Eurographics Association CY - Aire-la-Ville, Switzerland, Switzerland SN - 1-58113-536-X ER - TY - CONF T1 - Snap-together visualization: a user interface for coordinating visualizations via relational schemata T2 - AVI '00: Proceedings of the working conference on Advanced visual interfaces Y1 - 2000 A1 - North, Chris A1 - Shneiderman, Ben JF - AVI '00: Proceedings of the working conference on Advanced visual interfaces PB - ACM CY - New York, NY, USA SN - 1-58113-252-2 ER - TY - JOUR T1 - Snap-together visualization: can users construct and operate coordinated visualizations? JF - Int. J. Hum.-Comput. Stud. Y1 - 2000 A1 - North, Chris A1 - Shneiderman, Ben PB - Academic Press, Inc. CY - Duluth, MN, USA VL - 53 ER -