Semantic Interaction for Visual Analytics

Semantic Interaction for Visual Analytics

Inferring Analytical Reasoning for Model Steering

Alex Endert
ISBN: 9781627054713 | PDF ISBN: 9781627052917
Copyright © 2016 | 99 Pages | Publication Date: September, 2016

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This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation. This methodology affords user interaction on visual data representations that are native to the domain of the data.

User interaction in visual analytics systems is critical to enabling visual data exploration. Interaction transforms people from mere viewers to active participants in the process of analyzing and understanding data. This discourse between people and data enables people to understand aspects of their data, such as structure, patterns, trends, outliers, and other properties that ultimately result in insight. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis.

The book provides a description of the principles of semantic interaction, providing design guidelines for the integration of semantic interaction into visual analytics, examples of existing technologies that leverage semantic interaction, and a discussion of how to evaluate these technologies. Semantic interaction has the potential to increase the effectiveness of visual analytic technologies and opens possibilities for a fundamentally new design space for user interaction in visual analytics systems.

Table of Contents

Spatializations for Sensemaking Using Visual Analytics
Semantic Interaction
Applications that Integrate Semantic Interaction
Evaluating Semantic Interaction
Discussion and Open Challenges
Author's Biography

About the Author(s)

Alex Endert, Georgia Tech
Alex Endert is an Assistant Professor in the School of Interactive Computing at Georgia Tech. He directs the Visual Analytics Lab, where he and his students explore novel user interaction
techniques for visual analytics. His lab often apply this fundamental research to applied domains including text analysis, intelligence analysis, cyber security, decision-making, and others. He is an active contributor to venues for human-computer interaction and information visualization (ACM CHI, IEEE VIS, IEEE TVCG, IEEE CG&A). He received his Ph.D. in Computer Science at Virginia Tech in 2012, advised by Dr. Chris North. In 2013, his work on Semantic Interaction was awarded the IEEE VGTC VPG Pioneers Group Doctoral Dissertation Award and the Virginia Tech Computer Science Best Dissertation Award.


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