%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 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 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 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 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 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 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 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 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 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 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 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 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 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 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