Guidance in the human–machine analytics process

Collins, Christopher; Andrienko, Natalia; Schreck, Tobias; Yang, Jing; Choo, Jaegul; Engelke, Ulrich; Jena, Amit Kumar; Dwyer, Tim

doi:10.1016/j.visinf.2018.09.003

Cited by 72 publications

(59 citation statements)

References 70 publications

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“…We summarize the findings and contributions of our work by listing a set of qualities and research directions that we consider decisive for the development of comprehensive guidance mechanisms in visual analysis. As mentioned earlier, the expected goal of each guidance approach is to be effective [CAS∗18, CGM∗17]. This means that user and system guidance should concur to reach the established analysis objective.…”

Section: Discussion Future Research and Limitationsmentioning

confidence: 99%

“…Layout and Form . 2D position, spatial grouping, and marks are properties that our eyes perceive faster and that attract our attention [CAS∗18]. Guidance approaches use, for instance, links to signal relations or the positioning of elements to suggest the user that a (hopefully) better layout can be obtained.…”

Section: System Guidance To Human Activitiesmentioning

confidence: 99%

“…Guidance is for its nature a mixed‐initiative process: It comprises (1) the assistance a system gives to a user, and (2) to the feedback the user provides to the system in order to steer the analysis process and to achieve a common goal. In the context of mixed‐initiative data analysis, Ceneda et al [CGM∗17] describe what input data source the guidance process can exploit, what are the possible outcomes of this human‐computer collaboration and the different degrees of guidance the users may need [CAS∗18]. Although all ingredients ( What ?)…”

Section: Introductionmentioning

confidence: 99%

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A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective

Ceneda

Gschwandtner

Miksch

2019

Computer Graphics Forum

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Visual data analysis can be envisioned as a collaboration of the user and the computational system with the aim of completing a given task. Pursuing an effective system‐user integration, in which the system actively helps the user to reach his/her analysis goal has been focus of visualization research for quite some time. However, this problem is still largely unsolved. As a result, users might be overwhelmed by powerful but complex visual analysis systems which also limits their ability to produce insightful results. In this context, guidance is a promising step towards enabling an effective mixed‐initiative collaboration to promote the visual analysis. However, the way how guidance should be put into practice is still to be unravelled. Thus, we conducted a comprehensive literature research and provide an overview of how guidance is tackled by different approaches in visual analysis systems. We distinguish between guidance that is provided by the system to support the user, and guidance that is provided by the user to support the system. By identifying open problems, we highlight promising research directions and point to missing factors that are needed to enable the envisioned human‐computer collaboration, and thus, promote a more effective visual data analysis.

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Section: Discussion Future Research and Limitationsmentioning

confidence: 99%

Section: System Guidance To Human Activitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective

Ceneda

Gschwandtner

Miksch

2019

Computer Graphics Forum

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“…In the present work we use semantic interaction to enable users to modify a word2vec word embedding space [41] by modifying the groupings of words into concepts. As there may be many ways to adjust the semantic space, we provide suggested interactions as a form of guidance [9].…”

Section: Semantic Interactionmentioning

confidence: 99%

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

El‐Assady

Kehlbeck

Collins

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Guided relevance feedback for the targeted refinement of incoherent areas in the Semantic Concept Space. This user guidance component tours through the space and highlights potentially uncertain areas, suggesting a recommended action for refinement.Abstract-We present a framework that allows users to incorporate the semantics of their domain knowledge for topic model refinement while remaining model-agnostic. Our approach enables users to (1) understand the semantic space of the model, (2) identify regions of potential conflicts and problems, and (3) readjust the semantic relation of concepts based on their understanding, directly influencing the topic modeling. These tasks are supported by an interactive visual analytics workspace that uses word-embedding projections to define concept regions which can then be refined. The user-refined concepts are independent of a particular document collection and can be transferred to related corpora. All user interactions within the concept space directly affect the semantic relations of the underlying vector space model, which, in turn, change the topic modeling. In addition to direct manipulation, our system guides the users' decisionmaking process through recommended interactions that point out potential improvements. This targeted refinement aims at minimizing the feedback required for an efficient human-in-the-loop process. We confirm the improvements achieved through our approach in two user studies that show topic model quality improvements through our visual knowledge externalization and learning process.

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“…Guidance systems [Collins et al 2018] and recommender systems are emerging topics in VA and both demonstrated potential in supporting analysts. Using eye tracking we can potentially infer what people focus on [Just and Carpenter 1980].…”

Section: Related Workmentioning

confidence: 99%

Eye tracking support for visual analytics systems

Silva

Blascheck

Jianu

et al. 2019

Proceedings of the 11th ACM Symposium on Eye Tracking Research &Amp; Applications

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Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complex datasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches. Therefore, we discuss foundations for eye tracking support in VA systems. We first review and discuss the structure and range of typical VA systems. Based on a widely used VA model, we present five comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used to systematically explore how concrete VA systems could be extended with eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and application opportunities, and classify them into research themes. In a call for action, we map the road for future research to broaden the use of eye tracking and advance visual analytics. CCS CONCEPTS • Human-centered computing → Visual analytics; Interactive systems and tools; Visualization theory, concepts and paradigms;

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Guidance in the human–machine analytics process

Cited by 72 publications

References 70 publications

A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective

A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

Eye tracking support for visual analytics systems

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