@article {DOI268, title = {Semantic Interaction: Coupling Cognition and Computation through Usable Interactive Analytics}, journal = {IEEE Computer Graphics and Applications}, year = {2015}, month = {07/2015}, pages = {6-11}, author = {Endert, Alex and Chang, Remco and North, Chris and Zhou, Michelle} } @article {6855271, title = {Semantic Interaction for Visual Analytics: Toward Coupling Cognition and Computation}, journal = {Computer Graphics and Applications, IEEE}, volume = {34}, number = {4}, year = {2014}, month = {July}, pages = {8-15}, keywords = {Alex Endert, Analytical models, Cognition, computation, Computational modeling, computer graphics, Data models, Data visualization, graphics, human computer interaction, human-computer interaction, IN-SPIRE, Semantic interaction, Semantics, Visual Analytics, visualization}, issn = {0272-1716}, doi = {10.1109/MCG.2014.73}, author = {Endert, Alex} } @article {DOI10.1109/TVCG.2012.260, title = {Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering}, journal = {IEEE Transactions on Visualization and Computer Graphics}, volume = {18}, year = {2012}, month = {12/2012}, pages = {2879 - 2888}, abstract = {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{\textquoteright} 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{\textquoteright}s reasoning and intuition.}, issn = {1077-2626}, doi = {10.1109/TVCG.2012.260}, author = {Endert, Alex and Fiaux, Patrick and North, Chris} } @conference {Endert:2012:SIV:2207676.2207741, title = {Semantic interaction for visual text analytics}, booktitle = {Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems}, series = {CHI {\textquoteright}12}, year = {2012}, pages = {473{\textendash}482}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {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{\textquoteright} 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{\textquoteright}s feedback into account.}, keywords = {interaction, Visual Analytics, visualization}, isbn = {978-1-4503-1015-4}, doi = {10.1145/2207676.2207741}, url = {http://doi.acm.org/10.1145/2207676.2207741}, author = {Endert, Alex and Fiaux, Patrick and North, Chris} } @conference {Endert:2012:SCA:2254556.2254660, title = {The semantics of clustering: analysis of user-generated spatializations of text documents}, booktitle = {Proceedings of the International Working Conference on Advanced Visual Interfaces}, series = {AVI {\textquoteright}12}, year = {2012}, pages = {555{\textendash}562}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {Analyzing complex textual datasets consists of identifying connections and relationships within the data based on users{\textquoteright} 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{\textquoteright} 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.}, keywords = {clustering, text analytics, Visual Analytics, visualization}, isbn = {978-1-4503-1287-5}, doi = {10.1145/2254556.2254660}, url = {http://doi.acm.org/10.1145/2254556.2254660}, author = {Endert, Alex and Fox, Seth and Maiti, Dipayan and Leman, Scotland and North, Chris} } @unpublished {120, title = {Space for Two to Think: Large, High-Resolution Displays for Co-located Collaborative Sensemaking}, journal = {Technical Report TR-11-11}, year = {2011}, publisher = {Computer Science, Virginia Tech}, keywords = {collaborative sensemaking, high-resolution displays, large, Large High Resolution Display, single display groupware, Visual Analytics}, author = {Lauren Bradel and Andrews, Christopher and Endert, Alex and Katherine Vogt and Duke Hutchings and North, Chris} } @conference {Singh:2011:SCA:2016904.2016907, title = {Supporting the cyber analytic process using visual history on large displays}, booktitle = {Proceedings of the 8th International Symposium on Visualization for Cyber Security}, series = {VizSec {\textquoteright}11}, year = {2011}, pages = {3:1{\textendash}3:8}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {interaction styles, large high-resolution displays, prototyping, screen design, user-centered design}, isbn = {978-1-4503-0679-9}, doi = {10.1145/2016904.2016907}, url = {http://doi.acm.org/10.1145/2016904.2016907}, author = {Singh, Ankit and Lauren Bradel and Endert, Alex and Kincaid, Robert and Andrews, Christopher and North, Chris} } @conference {1753336, title = {Space to think: large high-resolution displays for sensemaking}, booktitle = {CHI {\textquoteright}10: Proceedings of the 28th international conference on Human factors in computing systems}, year = {2010}, pages = {55{\textendash}64}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {LHRD}, isbn = {978-1-60558-929-9}, doi = {http://doi.acm.org/10.1145/1753326.1753336}, author = {Andrews, Christopher and Endert, Alex and North, Chris} }