Interactive Visual Analytics for Knowledge Integration and Decision Intelligence

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https://hdl.handle.net/10125/102490

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    Pair Analytics in a Visual Analytics Context
    (2023-01-03) Fisher, Brian; Kasik, David
    This case study details the development of “pair analytics” as practical approach to applied analysis and as a scientific research method. The hybrid research project itself was part of a larger research program approved for the Canadian government for their offset program and supported by Federal and Provincial research internships. As a real-world analysis approach, the pair analysis sessions conduced actionable causal chain analysis of aircraft safety. As a scientific method, pair analytics advanced our knowledge of the cognitive science of interpersonal communication in Joint Activities. The paper describes how aerospace researchers and cognitive scientists were able to design a research approach that met constraints from both areas. It concludes with discussion of the implications of this work for highly integrated basic and responsive research in other areas of visualization and analytics.
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    A Mental Workload Estimation Model for Visualization Using EEG
    (2023-01-03) Yim, Soobin; Yoon, Chanyong; Yoo, Sangbong; Jang, Yun
    Various visualization design guides have been proposed and evaluated through quantitative methods that compare the response accuracy and time for completing visualization tasks. However, accuracy and time do not always represent the mental workload. Since quantitative approaches do not fully mirror mental workload, questionnaires and biosignals have been employed to measure mental workload in visualization assessments. The EEG as biosignal is one of the indicators frequently utilized to measure mental workload. Nevertheless, many studies have not applied the EEG for mental workload measurement in the visualization evaluation. In this work, we study the EEG to measure mental workload for visualization evaluation. We examine whether there is a difference in mental workload for the visualization designs suggested by the previously proposed visualization design guides. Besides, we propose a mental workload estimation model using EEG data specialized for each individual to evaluate visualization designs.
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