A Survey on Interaction Log Analysis for Evaluating Exploratory Visualizations

ElTayeby, Omar; Dou, Wenwen

doi:10.1145/2993901.2993912

Cited by 9 publications

(6 citation statements)

References 44 publications

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“…Gaining insight is considered one of the major purposes of visual data exploration [70]. Hence, understanding the user insight generation process when using visualization tools is an important goal of visualization research [20,50]. For instance, Yi et al [70] identified four categories of insight gaining processes through an extensive literature review: providing an overview, adjusting the level of abstraction and/or the range of selection, detecting patterns, and matching the user's mental model of the data.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing the Quality of Insight by Interactions: A Case Study

Micallef

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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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 paper 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.

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Section: Introductionmentioning

confidence: 99%

“…Interaction categorization based on actions also serves our purpose of using user intent to relate interaction and insight. Furthermore, ElTayeby and Dou [20] suggested an extra level between actions and sub-tasks that consists of patterns of multiple actions to support the reuse of analysis across different applications.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Quality of Insight by Interactions: A Case Study

Micallef

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…, interaction logs[ED16], and provenance tracking [LWPL11, CFS * 06, BCC * 05, SASF11]. We rely on built-in logging in Tableau[STH02] for analysis.Visualization Theory & Process Models: Our work is informed by models of the visual analysis process developed through exploratory studies, such as those by Isenberg et al [ITC08] and Grammel et al [GTS10].…”

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confidence: 99%

Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau

Battle

Heer

2019

Computer Graphics Forum

100

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Supporting exploratory visual analysis (EVA) is a central goal of visualization research, and yet our understanding of the process is arguably vague and piecemeal. We contribute a consistent definition of EVA through review of the relevant literature, and an empirical evaluation of existing assumptions regarding how analysts perform EVA using Tableau, a popular visual analysis tool. We present the results of a study where 27 Tableau users answered various analysis questions across 3 datasets. We measure task performance, identify recurring patterns across participants' analyses, and assess variance from task specificity and dataset. We find striking differences between existing assumptions and the collected data. Participants successfully completed a variety of tasks, with over 80% accuracy across focused tasks with measurably correct answers. The observed cadence of analyses is surprisingly slow compared to popular assumptions from the database community. We find significant overlap in analyses across participants, showing that EVA behaviors can be predictable. Furthermore, we find few structural differences between behavior graphs for open‐ended and more focused exploration tasks.

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“…In action-based tracking, detailed user interactions, e.g. mouse clicks and movements, are recorded in logs [6,7]. As a result, a detailed list of all actions performed in an application is generated.…”

Section: Introductionmentioning

confidence: 99%