TY - JOUR T1 - Different realities: a comparison of augmented and virtual reality for the sensemaking process JF - Frontiers in Virtual Reality Y1 - 2023 A1 - Lee Lisle A1 - Kylie Davidson A1 - Edward J.K. Gitre A1 - North, Chris A1 - Bowman, Doug A. VL - 4 ER - TY - JOUR T1 - Design guidelines for narrative maps in sensemaking tasks JF - Information Visualization Y1 - 2022 A1 - Brian Keith Norambuena A1 - Tanu Mitra A1 - North, Chris ER - TY - CONF T1 - DeepSI: Interactive Deep Learning for Semantic Interaction T2 - 26th International Conference on Intelligent User Interfaces (IUI ’21) Y1 - 2021 A1 - Yali Bian A1 - North, Chris JF - 26th International Conference on Intelligent User Interfaces (IUI ’21) ER - TY - CONF T1 - Do we still need physical monitors? An evaluation of the usability of AR virtual monitors for productivity work T2 - IEEE Virtual Reality and 3D User Interfaces (VR) Y1 - 2021 A1 - Leonardo Pavanatto Soares A1 - Doug Bowman A1 - North, Chris A1 - Carmen Badea A1 - Rich Stoakley JF - IEEE Virtual Reality and 3D User Interfaces (VR) ER - TY - CONF T1 - DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning T2 - Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS’19. Y1 - 2019 A1 - Yali Bian A1 - Wenskovitch, John A1 - North, Chris JF - Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS’19. ER - TY - JOUR T1 - Dropping the Baton?: Understanding Errors and Bottlenecks in a Crowdsourced Sensemaking Pipeline JF - Proc. ACM Hum.-Comput. Interact. Y1 - 2019 A1 - Li, Tianyi A1 - Manns, Chandler J. A1 - North, Chris A1 - Luther, Kurt KW - crowdsourcing KW - Intelligence Analysis KW - investigations KW - mysteries KW - sensemaking KW - text analytics AB - 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. PB - ACM CY - New York, NY, USA VL - 3 UR - http://doi.acm.org/10.1145/3359238 ER - TY - CONF T1 - Designing Usable Interactive Visual Analytics Tools for Dimension Reduction T2 - CHI 2016 Workshop on Human-Centered Machine Learning (HCML) Y1 - 2016 A1 - Self, Jessica Zeitz A1 - Hu, Xinran A1 - House, Leanna A1 - Leman, Scotland A1 - North, Chris JF - CHI 2016 Workshop on Human-Centered Machine Learning (HCML) ER - TY - THES T1 - Designing Display Ecologies for Visual Analysis T2 - Computer Science Y1 - 2015 A1 - Chung, Haeyong JF - Computer Science VL - Ph.D. ER - TY - RPRT T1 - Designing for Interactive Dimension Reduction Visual Analytics Tools to Explore High-Dimensional Data Y1 - 2015 A1 - Self, Jessica Zeitz A1 - Hu, Xinran A1 - House, Leanna A1 - Leman, Scotland A1 - North, Chris AB - 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. PB - Virginia Tech CY - Blacksburg ER - TY - JOUR T1 - Developing Large High-Resolution Display Visualizations of High-Fidelity Terrain Data JF - Journal of Computing and Information Science in Engineering Y1 - 2013 A1 - Chung, Haeyong A1 - North, Chris A1 - Ferris, John AB - 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. VL - 13 SN - 1530-9827 UR - http://dx.doi.org/10.1115/1.4024656 IS - 3 N1 - 10.1115/1.4024656 JO - Journal of Computing and Information Science in Engineering ER - TY - CONF T1 - Designing large high-resolution display workspaces T2 - Proceedings of the International Working Conference on Advanced Visual Interfaces Y1 - 2012 A1 - Endert, Alex A1 - Lauren Bradel A1 - Zeitz, Jessica A1 - Andrews, Christopher A1 - North, Chris KW - large high-resolution displays AB - 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. 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/10.1145/2254556.2254570 ER - TY - CONF T1 - Dynamic Analysis of Large Datasets with Animated and Correlated Views T2 - IEEE VAST 2012 (Extended Abstract) (Honorable Mention for Good Use of Coordinated Displays) Y1 - 2012 A1 - Yong Cao A1 - Reese Moore A1 - Peng Mi A1 - Endert, Alex A1 - North, Chris A1 - Randy Marchany AB - 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. JF - IEEE VAST 2012 (Extended Abstract) (Honorable Mention for Good Use of Coordinated Displays) ER - TY - CONF T1 - Dynamic query sliders vs. brushing histograms T2 - CHI '03: CHI '03 extended abstracts on Human factors in computing systems Y1 - 2003 A1 - Li, Qing A1 - Bao, Xiaofeng A1 - Song, Chen A1 - Zhang, Jinfei A1 - North, Chris JF - CHI '03: CHI '03 extended abstracts on Human factors in computing systems PB - ACM CY - New York, NY, USA SN - 1-58113-637-4 ER -