An Exploratory Study on Visual Exploration of Model Simulations by Multiple Types of Experts

Boukhelifa, Nadia; Bezerianos, Anastasia; Trelea, Ioan-Cristian; Perrot, Nathalie; Lutton, Évelyne

doi:10.1145/3290605.3300874

Cited by 11 publications

(37 citation statements)

References 48 publications

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“…In the context of model-based business intelligence, analysts vary input model parameters to explore alternative what-if scenarios [11,32]. Similarly, Boukhelifa et al [3] find that alternative analysis scenarios branch out from previous research questions and hypotheses in collaborative model sensemaking. In the context of intelligence analysis, analysts often weigh alternative explanations and competing hypotheses [17,23].…”

Section: Various Concepts Around Alternativesmentioning

confidence: 99%

“…As such, data workers explore different alternatives: they generate and compare multiple schemas [33], formulate solutions by combining parts from different exploration paths [24], and deal with uncertainty that could arise throughout analysis [4]. Moreover, multiple types of data worker need to collaborate to perform more complex analytic tasks [3,22].…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Role of Alternatives in Data Analysis Practices

Liu

Boukhelifa

Eagan

2019

IEEE Trans. Visual. Comput. Graphics

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Data workers are people who perform data analysis activities as a part of their daily work but do not formally identify as data scientists. They come from various domains and often need to explore diverse sets of hypotheses and theories, a variety of data sources, algorithms, methods, tools, and visual designs. Taken together, we call these alternatives. To better understand and characterize the role of alternatives in their analyses, we conducted semi-structured interviews with 12 data workers with different types of expertise. We conducted four types of analyses to understand 1) why data workers explore alternatives; 2) the different notions of alternatives and how they fit into the sensemaking process; 3) the high-level processes around alternatives; and 4) their strategies to generate, explore, and manage those alternatives. We find that participants' diverse levels of domain and computational expertise, experience with different tools, and collaboration within their broader context play an important role in how they explore these alternatives. These findings call out the need for more attention towards a deeper understanding of alternatives and the need for better tools to facilitate the exploration, interpretation, and management of alternatives. Drawing upon these analyses and findings, we present a framework based on participants' 1) degree of attention, 2) abstraction level, and 3) analytic processes. We show how this framework can help understand how data workers consider such alternatives in their analyses and how tool designers might create tools to better support them.

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Section: Various Concepts Around Alternativesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding the Role of Alternatives in Data Analysis Practices

Liu

Boukhelifa

Eagan

2019

IEEE Trans. Visual. Comput. Graphics

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“…This body of work has inspired later studies that looked at different aspects of sensemaking. In the context of EDA, recent work looked at how analysts manage uncertainty [7]; how they explore various types of alternatives [19]; and how groups deal with trade-offs [5] and build common ground [14]. The results of this body of work are often conceptual models, task taxonomies and design recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…This could provide opportunities to review and share insights, but importantly, it can potentially improve user exploration practices and strategies [10]. In previous work [5] we conducted an observational study with domain experts from agronomy, who explored complex model simulation datasets using an existing visualization tool [13,9]. Their main exploration task was open ended, but the goal was to explore alternative model tradeoffs (e.g., between the amount of fertilisers supplied and the quantity of wheat crop yield).…”

Section: Introductionmentioning

confidence: 99%

“…However, despite the history widgets provided, experts appear to loose track of their analysis steps and the many alternative trade-off scenarios they were trying to com-pare: "What was the basis of the reflection here? In fact, we seem to go faster than we have time to note down" (a participant from study [5]). Moreover, our domain experts were not able to build a clear mental model of past exploration, nor did they have any means to evaluate whether they had a robust or exhaustive exploration strategy [26].…”

Section: Introductionmentioning

confidence: 99%

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How Domain Experts Structure Their Exploratory Data Analysis: Towards a Machine-Learned Storyline

Barczewski¹,

Bezerianos

Boukhelifa

2020

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

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Exploratory data analysis is an open-ended iterative process, where the goal is to discover new insights. Much of the work to characterise this exploration stems from qualitative research resulting in rich findings, task taxonomies, and conceptual models. In this work, we propose a machinelearning approach where the structure of an exploratory analysis session is automatically learned. Our method, based on Hidden-Markov Models, automatically builds a storyline of past exploration from log data events, that shows key analysis scenarios and the transitions between analysts' hypotheses and research questions. Compared to a clustering method, this approach yields higher accuracy for detecting transitions between analysis scenarios. We argue for incorporating provenance views in exploratory data analysis systems that show, at minimum, the structure and intermediate results of past exploration. Besides helping the reproducibility of the different analyses and their results, this can encourage analysts to reflect upon and ultimately adapt their exploration strategies.

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Objective Evaluation of Subjective Metrics for Interactive Decision-Making Tasks by Non-experts

Laurillau¹,

Coutaz²,

Nguyen³

et al. 2021

Human-Computer Interaction – INTERACT 2021

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This work addresses the evaluation of interaction techniques for decision-making tasks performed by non-experts in the context of multi-objective optimization problems. Such tasks require making trade-offs between antagonistic criteria, according to individual subjective preferences. Evaluating such techniques is made difficult by the subjective nature of such tasks, as well as by a lack of rigorous methods for assessment. Our primary contributions to this problem are two-fold: (1) a set of subjective metrics including decision accuracy, choice satisfaction, and incentives to explore; (2) the use of a pragmatic approach to map these subjective metrics onto objective quantitative measures. To illustrate how this subjective-objective mapping can be performed in a pragmatic manner, we have conducted an experiment involving 177 participants to objectively measure and compare two multi-slider interaction techniques for decisionmaking tasks performed by non-experts in the context of domestic energy management. The results of this evaluation constitute a secondary contribution.

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An Exploratory Study on Visual Exploration of Model Simulations by Multiple Types of Experts

Cited by 11 publications

References 48 publications

Understanding the Role of Alternatives in Data Analysis Practices

Understanding the Role of Alternatives in Data Analysis Practices

How Domain Experts Structure Their Exploratory Data Analysis: Towards a Machine-Learned Storyline

Objective Evaluation of Subjective Metrics for Interactive Decision-Making Tasks by Non-experts

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