Characterizing the Quality of Insight by Interactions: A Case Study

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http://hdl.handle.net/10138/333450

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He , C , Micallef , L , He , L , Peddinti , G , Aittokallio , T & Jacucci , G 2021 , ' Characterizing the Quality of Insight by Interactions: A Case Study ' , IEEE Transactions on Visualization and Computer Graphics , vol. 27 , no. 8 , pp. 3410-3424 . https://doi.org/10.1109/TVCG.2020.2977634

Title: Characterizing the Quality of Insight by Interactions: A Case Study
Author: He, Chen; Micallef, Luana; He, Liye; Peddinti, Gopal; Aittokallio, Tero; Jacucci, Giulio
Contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Department of Computer Science
Date: 2021-08-01
Language: eng
Number of pages: 15
Belongs to series: IEEE Transactions on Visualization and Computer Graphics
ISSN: 1077-2626
URI: http://hdl.handle.net/10138/333450
Abstract: Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This article presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool—MediSyn—for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study’s implications, lessons learned, and future research opportunities.
Subject: ANALYTIC PROVENANCE
COMPUTATION
Cognitive science
DATA EXPLORATION
Data visualization
FRAMEWORK
INFORMATION
Insight
KNOWLEDGE
Market research
Pattern analysis
SENSEMAKING
Task analysis
Tools
USERS
VISUALIZATION
Visualization
entity
insight-based evaluation
interaction
interaction pattern
visualization
113 Computer and information sciences
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