VizAssist: an interactive user assistant for visual data mining

Bouali, Fatma; Guettala, Abdelheq Et-Tahir; Venturini, Gilles

doi:10.1007/s00371-015-1132-9

Cited by 51 publications

(30 citation statements)

References 36 publications

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“…Two distinct types of suggestions are available: refinement suggestions, which improve the current design, and brainstorming suggestions, which change the style. Bouali et al [BGV16] designed a system providing suggestions of proper visual mappings to the user. The user can choose and select the most promising one and provide weights of the most appropriate data columns to be included in the final visualization.…”

Section: System Guidance To Human Activitiesmentioning

confidence: 99%

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: System Guidance To Human Activitiesmentioning

confidence: 99%

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

Ceneda

Gschwandtner

Miksch

2019

Computer Graphics Forum

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“…Depending on the purpose of recommendations, interfaces may vary to some degree, but overall, recent visualization systems tend to use similar recommendation interfaces. In terms of layout, most systems use a gallery‐based layout either showing multiple recommendations at once for easy comparison between alternatives [WMA*16, WQM*17, Exc18, MHS07, WW10, VRM*15, vdEvW13, BGV16, GW09, KHPA12, SKC*11, DW14, EB11] or a single recommendation while enabling easy exploration of alternatives [JLLS17, SS05]. For representing individual recommendations, previews hold a dominant position [Exc18, WQM*17, WMA*16, VRM*15, KHPA12, GW09, EB11], while simple textual descriptions are sometimes used with the preview [MHS07, JLLS17, WW10, vdEvW13, SKC*11, DW14, SS05].…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, textual descriptions are used to provide additional information such as chart types (e.g., Bar Chart) [EB11, GW09, BGV16, Exc18], data fields used in recommended visualizations (e.g., “IMDB Rating vs Rotten Tomatoes Rating”) [KHPA12, VRM*15, WQM*17, WMA*16], or more details about when to use a specific type of visualization [Exc18] or what it is [WQM*17, WMA*16]. Based on an exploratory study with InfoVis novices, Grammel et al [GTS10] claim that, to help users better understand recommendations, more in‐depth explanations about the recommendations should be provided, including the advantages and disadvantages of using them.…”

Section: Related Workmentioning

confidence: 99%

“…Preview, the most widely used method to represent visualization recommendations in existing tools [WMA*16, WQM*17, Exc18, MHS07, WW10, VRM*15, vdEvW13, BGV16, GW09, KHPA12, SKC*11, DW14, EB11] (Figure A), shows the visualization result where the suggested visual mapping is applied over the current visualization. By showing the suggested visualization result in advance, users might easily presume and compare the usefulness of recommendations as illustrated by Grammel et al [GTS10].…”

Section: Prototype Designmentioning

confidence: 99%

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Toward Understanding Representation Methods in Visualization Recommendations through Scatterplot Construction Tasks

L’Yi

Chang

Shin

et al. 2019

Computer Graphics Forum

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Most visualization recommendation systems predominantly rely on graphical previews to describe alternative visual encodings. However, since InfoVis novices are not familiar with visual representations (e.g., interpretation barriers [GTS10]), novices might have difficulty understanding and choosing recommended visual encodings. As an initial step toward understanding effective representation methods for visualization recommendations, we investigate the effectiveness of three representation methods (i.e., previews, animated transitions, and textual descriptions) under scatterplot construction tasks. Our results show how different representations individually and cooperatively help users understand and choose recommended visualizations, for example, by supporting their expect‐and‐confirm process. Based on our study results, we discuss design implications for visualization recommendation interfaces.

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“…Many existing tools feature recommendation modules that suggest designs as users manipulate data elements (e.g., Tableau's "Show Me" [40] and similar strategies in other tools [8,18,56,58,72]). In general, automation is meant to ease tasks that would be otherwise unnecessarily difficult, repetitive, or tedious.…”

Section: Automation and Agencymentioning

confidence: 99%

iVoLVER

Méndez

Nacenta

Vandenheste

2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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Previous research has identified trade-offs when it comes to designing visualization tools. While constructive "bottomup" tools promote a hands-on, user-driven design process that enables a deep understanding and control of the visual mapping, automated tools are more efficient and allow people to rapidly explore complex alternative designs, often at the cost of transparency. We investigate how to design visualization tools that support a user-driven, transparent design process while enabling efficiency and automation, through a series of design workshops that looked at how both visualization experts and novices approach this problem. Participants produced a variety of solutions that range from example-based approaches expanding constructive visualization to solutions in which the visualization tool infers solutions on behalf of the designer, e.g., based on data attributes. On a higher level, these findings highlight agency and granularity as dimensions that can guide the design of visualization tools in this space.

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VizAssist: an interactive user assistant for visual data mining

Abstract: International audienc

Cited by 51 publications

References 36 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

Toward Understanding Representation Methods in Visualization Recommendations through Scatterplot Construction Tasks

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