A Case Study Using Visualization Interaction Logs and Insight Metrics to Understand How Analysts Arrive at Insights

Guo, Hua; Gomez, Steven R.; Ziemkiewicz, Caroline; Laidlaw, David H.

doi:10.1109/tvcg.2015.2467613

Cited by 76 publications

(70 citation statements)

References 27 publications

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“…Interaction Sequences : Interaction sequences reveal temporal relationships between observed interactions. Guo et al [GGZL16] identify common sequences that lead to insights. Gotz and Wen [GW09] identify four common interaction sub‐sequences, which they use to generate visualization recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…Insight‐Based Evaluations : Insight‐based evaluations attempt to catalog “insights” generated through open‐ended visualization use [SND05, SNLD06, LH14,GGZL16,ZZZK18]. These methods collect qualitative data about the EVA process, which researchers code and analyze.…”

Section: Related Workmentioning

confidence: 99%

“…Formulation and Evolution of Goals : An oft‐stated goal of EVA is the production of new insights or observations from a given dataset ( insight generation ) [ED16, LTM18, jJKMG07, GGZL16, ZGC∗16, ZZZK18, LH14, FPDs12]. Lam et al [LTM18] describe the goal of exploration as “Discover[ing] Observation[s]”; however, this goal is vague in comparison to other visual analytic goals.…”

Section: Themes In the Eva Literaturementioning

confidence: 99%

“…EVA and Search : Terms like “query” [GZ08,LKS13,KJTN14, DPD14, KCZ14, SKL∗16], “browse” [LH14, GGZL16, BCS16], and “search” [KS12, WMA∗16b, PS08] are frequently associated with visual exploration. In EVA, users are often searching for novel observations in a dataset, which could inform or validate future hypotheses [Kei01,LH14,GZ08,RJPL16,AZL∗19,ZZZK18].…”

Section: Themes In the Eva Literaturementioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Themes In the Eva Literaturementioning

confidence: 99%

Section: Themes In the Eva Literaturementioning

confidence: 99%

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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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“…Authors have argued that better logging could help elucidate the sensemaking process [Guo et al 2016] and improve the analytics tools [Alspaugh et al 2014].…”

Section: Provenance and Sensemakingmentioning

confidence: 99%

Mining Redescriptions with Siren

Galbrun

Miettinen

2018

ACM Trans. Knowl. Discov. Data

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In many areas of science, scientists need to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. For example, in biology, an important task is to identify the bioclimatic constraints that allow some species to survive, that is, to describe geographical regions both in terms of the fauna that inhabits them and of their bioclimatic conditions. In data analysis, the task of automatically generating such alternative characterizations is called redescription mining.If a domain expert wants to use redescription mining in his research, merely being able to find redescriptions is not enough. He must also be able to understand the redescriptions found, adjust them to better match his domain knowledge, test alternative hypotheses with them, and guide the mining process towards results he considers interesting. To facilitate these goals, we introduce Siren, an interactive tool for mining and visualizing redescriptions.Siren allows to obtain redescriptions in an anytime fashion through efficient, distributed mining, to examine the results in various linked visualizations, to interact with the results either directly or via the visualizations, and to guide the mining algorithm toward specific redescriptions. In this paper, we explain the features of Siren and why they are useful for redescription mining. We also propose two novel redescription mining algorithms that improve the generalizability of the results compared to the existing ones.

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