TY - CONF T1 - Machine Learning from User Interaction for Visualization and Analytics: A Workshop-Generated Research Agenda T2 - Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS’19. Y1 - 2019 A1 - Wenskovitch, John A1 - Michelle Dowling A1 - Grose, Laura A1 - North, Chris A1 - Chang, Remco A1 - Endert, Alex A1 - Rogers, David JF - Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS’19. 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 - The human is the loop: new directions for visual analytics JF - Journal of Intelligent Information Systems Y1 - 2014 A1 - Endert, Alex A1 - Hossain, M. Shahriar A1 - Ramakrishnan, Naren A1 - North, Chris A1 - Fiaux, Patrick A1 - Andrews, Christopher KW - clustering KW - Semantic interaction KW - Spatialization KW - Storytelling KW - Visual Analytics AB - 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. PB - Springer US VL - 43 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 - Toward Usable Interactive Analytics: Coupling Cognition and Computation T2 - KDD 2014 Workshop on Interactive Data Exploration and Analytics (IDEA) Y1 - 2014 A1 - Endert, Alex A1 - North, Chris A1 - Chang, Remco A1 - Zhou, Michelle JF - KDD 2014 Workshop on Interactive Data Exploration and Analytics (IDEA) UR - http://poloclub.gatech.edu/idea2014/papers/p52-endert.pdf ER - TY - JOUR T1 - Beyond Control Panels: Direct Manipulation for Visual Analytics JF - IEEE Computer Graphics and Applications Y1 - 2013 A1 - Endert, Alex A1 - Lauren Bradel A1 - North, Chris VL - 33 IS - 4 JO - IEEE Comput. Grap. Appl. ER - TY - JOUR T1 - Bixplorer: Visual Analytics with Biclusters JF - Computer Y1 - 2013 A1 - Fiaux, Patrick A1 - Sun, Maoyuan A1 - Lauren Bradel A1 - North, Chris A1 - Ramakrishnan, Naren A1 - Endert, Alex VL - 46 IS - 8 JO - Computer ER - TY - CONF T1 - How analysts cognitively “connect the dots” T2 - 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) Y1 - 2013 A1 - Lauren Bradel A1 - Self, Jessica Zeitz A1 - Endert, Alex A1 - Hossain, M. Shahriar A1 - North, Chris A1 - Ramakrishnan, Naren JF - 2013 IEEE International Conference on Intelligence and Security Informatics (ISI) PB - IEEE CY - Seattle, WA, USA SN - 978-1-4673-6214-6 ER - TY - JOUR T1 - Large High Resolution Displays for Co-Located Collaborative Sensemaking: Display Usage and Territoriality JF - International Journal of Human-Computer Studies Y1 - 2013 A1 - Lauren Bradel A1 - Endert, Alex A1 - Koch, Kristen A1 - Andrews, Christopher A1 - North, Chris VL - 71 IS - 11 JO - International Journal of Human-Computer Studies ER - TY - JOUR T1 - Visual to Parametric Interaction (V2PI) JF - PLoS ONE Y1 - 2013 A1 - Leman, Scotland A1 - House, Leanna A1 - Maiti, Dipayan A1 - Endert, Alex A1 - North, Chris AB - 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. VL - 8 IS - 3 JO - PLoS ONE 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 - How spatial layout, interactivity, and persistent visibility affect learning with large displays T2 - Proceedings of the International Working Conference on Advanced Visual Interfaces Y1 - 2012 A1 - Ragan, Eric D. A1 - Endert, Alex A1 - Bowman, Doug A. A1 - Francis Quek KW - interactivity KW - large displays KW - learning KW - memory KW - use of space AB - 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. 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/10.1145/2254556.2254576 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 - CONF T1 - Analytic provenance: process+interaction+insight T2 - Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems Y1 - 2011 A1 - North, Chris A1 - Chang, Remco A1 - Endert, Alex A1 - Dou, Wenwen A1 - May, Richard A1 - Pike, Bill A1 - Fink, G. KW - analytic provenance KW - user interaction KW - Visual Analytics KW - visualization JF - Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems T3 - CHI EA '11 PB - ACM CY - New York, NY, USA SN - 978-1-4503-0268-5 UR - http://doi.acm.org/10.1145/1979742.1979570 ER - TY - CONF T1 - ChairMouse: leveraging natural chair rotation for cursor navigation on large, high-resolution displays T2 - Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems Y1 - 2011 A1 - Endert, Alex A1 - Fiaux, Patrick A1 - Chung, Haeyong A1 - Stewart, Michael A1 - Andrews, Christopher A1 - North, Chris KW - Embodied Interaction KW - interaction design KW - large display AB - 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. JF - Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems T3 - CHI EA '11 PB - ACM CY - New York, NY, USA SN - 978-1-4503-0268-5 UR - http://doi.acm.org/10.1145/1979742.1979628 ER - TY - CONF T1 - Co-located Collaborative Sensemaking on a Large High-Resolution Display with Multiple Input Devices T2 - INTERACT 2011 Y1 - 2011 A1 - Katherine Vogt A1 - Lauren Bradel A1 - Andrews, Christopher A1 - North, Chris A1 - Endert, Alex A1 - Duke Hutchings KW - co-located KW - CSCW KW - Large High Resolution Display KW - large high-resolution display KW - sensemaking KW - Visual Analytics JF - INTERACT 2011 CY - Lisbon, Portugal VL - 6947 SN - 978-3-642-23771-3 ER - TY - CONF T1 - The effects of spatial layout and view control on cognitive processing T2 - Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems Y1 - 2011 A1 - Ragan, Eric D. A1 - Endert, Alex A1 - Bowman, Doug A. A1 - Francis Quek KW - information processing KW - interactivity KW - learning KW - spatial memory KW - visualization JF - Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems T3 - CHI EA '11 PB - ACM CY - New York, NY, USA SN - 978-1-4503-0268-5 UR - http://doi.acm.org/10.1145/1979742.1979921 ER - TY - JOUR T1 - Information visualization on large, high-resolution displays: Issues, challenges, and opportunities JF - Information Visualization Y1 - 2011 A1 - Andrews, Christopher A1 - Endert, Alex A1 - Yost, Beth A1 - North, Chris AB - 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. VL - 10 UR - http://ivi.sagepub.com/content/10/4/341.abstract ER - TY - CONF T1 - Observation-level interaction with statistical models for visual analytics T2 - Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on Y1 - 2011 A1 - Endert, Alex A1 - Chao Han A1 - Maiti, Dipayan A1 - House, Leanna A1 - Leman, Scotland A1 - North, Chris KW - data analysis KW - data interactive visual exploration KW - data visualisation KW - exploratory interaction KW - expressive interaction KW - generative topographic mapping KW - multidimensional scaling KW - observation-level interaction KW - parameter adjustments KW - principal component analysis KW - probabilistic principal component analysis KW - probability KW - sensemaking process KW - statistical models KW - Visual Analytics AB - 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. JF - Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on 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 - Unifying the Sensemaking Loop with Semantic Interaction T2 - IEEE Workshop on Interactive Visual Text Analytics for Decision Making at VisWeek 2011 Y1 - 2011 A1 - Endert, Alex A1 - Fiaux, Patrick A1 - North, Chris KW - Visual Analytics JF - IEEE Workshop on Interactive Visual Text Analytics for Decision Making at VisWeek 2011 CY - Providence, RI ER - TY - CONF T1 - Visual encodings that support physical navigation on large displays T2 - Proceedings of Graphics Interface 2011 Y1 - 2011 A1 - Endert, Alex A1 - Andrews, Christopher A1 - Lee, Yueh Hua A1 - North, Chris KW - aggregation KW - high-resolution display KW - information visualization KW - large KW - perceptual scalability JF - Proceedings of Graphics Interface 2011 T3 - GI '11 PB - Canadian Human-Computer Communications Society CY - School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada SN - 978-1-4503-0693-5 UR - http://dl.acm.org/citation.cfm?id=1992917.1992935 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 - CONF T1 - Towards efficient collaboration in cyber security T2 - Collaborative Technologies and Systems (CTS), 2010 International Symposium on Y1 - 2010 A1 - Hui, P. A1 - Bruce, J. A1 - Fink, G. A1 - Gregory, M. A1 - Best, D. A1 - McGrath, L. A1 - Endert, Alex KW - collaboration KW - cyber security analysts KW - groupware KW - security bulletins KW - security of data JF - Collaborative Technologies and Systems (CTS), 2010 International Symposium on ER - TY - CONF T1 - Professional Analysts using a Large, High-Resolution Display T2 - IEEE VAST 2009 (Extended Abstract) (Awarded Special Contributions to the VAST Challenge Contest) Y1 - 2009 A1 - Endert, Alex A1 - Andrews, Christopher A1 - North, Chris KW - Large High Resolution Display KW - Visual Analytics JF - IEEE VAST 2009 (Extended Abstract) (Awarded Special Contributions to the VAST Challenge Contest) ER - TY - CONF T1 - VAST contest dataset use in education T2 - Visual Analytics Science and Technology, 2009. IEEE VAST 2009. Y1 - 2009 A1 - Whiting, M.A. A1 - North, Chris A1 - Endert, Alex A1 - Scholtz, J. A1 - Haack, J. A1 - Varley, C. A1 - Thomas, J. KW - data visualisation KW - education KW - educational technology KW - evaluation metrics KW - IEEE visual analytics science and technology KW - information analysis KW - information analysts KW - VAST KW - Visual Analytics JF - Visual Analytics Science and Technology, 2009. IEEE VAST 2009. ER - TY - CONF T1 - Visualizing cyber security: Usable workspaces T2 - Visualization for Cyber Security, 2009. VizSec 2009. 6th International Workshop on Y1 - 2009 A1 - Fink, G. A1 - North, Chris A1 - Endert, Alex A1 - Rose, S. KW - cyber analytics work environment KW - cyber security visualization KW - data visualisation KW - digital infrastructures KW - information foraging KW - Large High Resolution Display KW - security of data KW - usability evaluation KW - usable workspaces KW - Visual Analytics JF - Visualization for Cyber Security, 2009. VizSec 2009. 6th International Workshop on ER -