Version 1
: Received: 30 April 2024 / Approved: 30 April 2024 / Online: 30 April 2024 (15:29:01 CEST)
How to cite:
Jeong, D. H.; Jeong, B. K.; Ji, S. Y. Understanding Semantic User Interactions in Visualization. Preprints2024, 2024042019. https://doi.org/10.20944/preprints202404.2019.v1
Jeong, D. H.; Jeong, B. K.; Ji, S. Y. Understanding Semantic User Interactions in Visualization. Preprints 2024, 2024042019. https://doi.org/10.20944/preprints202404.2019.v1
Jeong, D. H.; Jeong, B. K.; Ji, S. Y. Understanding Semantic User Interactions in Visualization. Preprints2024, 2024042019. https://doi.org/10.20944/preprints202404.2019.v1
APA Style
Jeong, D. H., Jeong, B. K., & Ji, S. Y. (2024). Understanding Semantic User Interactions in Visualization. Preprints. https://doi.org/10.20944/preprints202404.2019.v1
Chicago/Turabian Style
Jeong, D. H., Bong Keun Jeong and Soo Yeon Ji. 2024 "Understanding Semantic User Interactions in Visualization" Preprints. https://doi.org/10.20944/preprints202404.2019.v1
Abstract
In the field of visualization, understanding users’ analytical process is important to determine the effectiveness of a designed visualization application. To understand the users’ analytical process, numerous studies have been performed to capture and analyze user interactions. Although many studies have emphasized the importance of analyzing user interactions to understand users’ analytical reasoning processes, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that bridges this gap by correlating semantic user interactions with analysis decisions through an interactive wire transaction analysis system and a visual state transition matrix. With the designed analysis system, interactive analysis can be performed to evaluate financial fraud in wire transactions. It also allows the mapping of captured user interactions and analytical decisions back onto the visualization, revealing users’ distinct results. The visual state transition matrix further aids to help understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to assess the effectiveness of our approach in understanding analysts’ strategies by connecting them to their decisions. From the study, we observed an average of 72% accuracy in clearly classifying the semantic user interactions. For classifying individual decisions, we observed an average of 70% classification accuracy. This emphasizes the importance of capturing semantic user interactions to understand users’ analytical processes. Overall, it is determined that the proposed approach not only enhances the understanding of analytical behaviors but also offers a robust approach for evaluating user interactions in visualization tools.
Keywords
Visual Analytics; Semantic User Interactions; Machine Learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
