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• W2893011415 abstract "The goal of visual analytics is to create a symbiosis between human and computer by leveraging their unique strengths. While this model has demonstrated immense success, we are yet to realize the full potential of such a human-computer partnership. In a perfect collaborative mixed-initiative system, the computer must possess skills for learning and anticipating the users' needs. Addressing this gap, we propose a framework for inferring focus areas from passive observations of the user's actions, thereby allowing accurate predictions of future events. We evaluate this technique with a crime map and demonstrate that users' clicks appear in our prediction set 95% - 97% of the time. Further analysis shows that we can achieve high prediction accuracy typically after three clicks. Altogether, we show that passive observations of interaction data can reveal valuable information that will allow the system to learn and anticipate future events, laying the foundation for next-generation tools." @default.
• W2893011415 created "2018-10-05" @default.
• W2893011415 creator A5005783140 @default.
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• W2893011415 date "2018-09-25" @default.
• W2893011415 modified "2023-10-18" @default.
• W2893011415 title "Learning and Anticipating Future Actions During Exploratory Data Analysis" @default.
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• W2893011415 doi "https://doi.org/10.48550/arxiv.1809.09664" @default.
• W2893011415 hasPublicationYear "2018" @default.
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