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A Process-driven View on Summative Evaluation of Visual Analytics Solutions
Many evaluation methods have been applied to assess the usefulness of vi...
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A Survey on Synchrophasor Data Quality and Cybersecurity Challenges, and Evaluation of their Interdependencies
Synchrophasor devices guarantee situation awareness for real-time monito...
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Counting methods introduced into the bibliometric research literature 1970-2018: A review
The present review of bibliometric counting methods investigates 1) the ...
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Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks
The success of deep neural networks in many real-world applications is l...
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Two Dimensions for Organizing Immersive Analytics: Toward a Taxonomy for Facet and Position
As immersive analytics continues to grow as a discipline, so too should ...
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Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting
Language generation tasks that seek to mimic human ability to use langua...
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STOAViz: Visualizing Saturated Thickness of Ogallala Aquifer
In this paper, we introduce STOAViz, a visual analytics tool for analyzi...
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The Validity, Generalizability and Feasibility of Summative Evaluation Methods in Visual Analytics
Many evaluation methods have been used to assess the usefulness of Visual Analytics (VA) solutions. These methods stem from a variety of origins with different assumptions and goals, which cause confusion about their proofing capabilities. Moreover, the lack of discussion about the evaluation processes may limit our potential to develop new evaluation methods specialized for VA. In this paper, we present an analysis of evaluation methods that have been used to summatively evaluate VA solutions. We provide a survey and taxonomy of the evaluation methods that have appeared in the VAST literature in the past two years. We then analyze these methods in terms of validity and generalizability of their findings, as well as the feasibility of using them. We propose a new metric called summative quality to compare evaluation methods according to their ability to prove usefulness, and make recommendations for selecting evaluation methods based on their summative quality in the VA domain.
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