PolyVis: Cross-Device Framework for Collaborative Visual Data Analysis

Alsaiari, Abeer; Johnson, Andrew; Nishimoto, Arthur

doi:10.1109/smc.2019.8914209

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Figure 1 illustrates how our system allow the IMs to express their questions and intents more freely to get insights for exploring of 26, 817 policyholder's large database of insurance claims and risk management [30]. Additionally, as a solution to this, speech interaction for the conversation and also touch interaction can be supported by a visualization, which allow the IMs to follow up on the current status of policyholders for handling a risky situation [49,6,42,33]. Then, we gathered 169 questions from the teams of IMs and found that our VAS correctly answers 69% of all the questions.…”

Section: Introductionmentioning

confidence: 99%

Natural language interactions enhanced by data visualization to explore insurance claims and manage risk

et al. 2022

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Analysis of claims and risk management is the key task to avoid frauds and to provide risk management in the life insurance industry. Though visualization plays a fundamental role in supporting analysis tasks of the business domain, exploring user behaviour remains a challenging task. The prevalence of natural language interactions aided with data visualization has become quite the norm. With the increasing demand of visualization tools and varying level of user expertise, it comes as no surprise the use of natural languages interface. However, the design of visual analytics tools aided with natural language interfaces (NLIs) for risk management and claim analysis requires thorough task analysis and domain expertise. In this work, we investigate an alternative approach through a natural language interaction based interactive visualization such as chart, pie, and histogram, which can be applied for analyzing insurance claims and risk management. We design a new visual analytics solution (VAS) named (InsCRMVis 1 ) aided with NLIs. We present an expert evaluation

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Section: Introductionmentioning

confidence: 99%

Natural language interactions enhanced by data visualization to explore insurance claims and manage risk

et al. 2022

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Interactive Visualization on Large High‐Resolution Displays: A Survey

Belkacem,

Tominski,

Médoc

et al. 2024

Computer Graphics Forum

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In the past few years, large high‐resolution displays (LHRDs) have attracted considerable attention from researchers, industries and application areas that increasingly rely on data‐driven decision‐making. An up‐to‐date survey on the use of LHRDs for interactive data visualization seems warranted to summarize how new solutions meet the characteristics and requirements of LHRDs and take advantage of their unique benefits. In this survey, we start by defining LHRDs and outlining the consequence of LHRD environments on interactive visualizations in terms of more pixels, space, users and devices. Then, we review related literature along the four axes of visualization, interaction, evaluation studies and applications. With these four axes, our survey provides a unique perspective and covers a broad range of aspects being relevant when developing interactive visual data analysis solutions for LHRDs. We conclude this survey by reflecting on a number of opportunities for future research to help the community take up the still‐open challenges of interactive visualization on LHRDs.

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A Systematic Literature Review of User Evaluation in Immersive Analytics

Friedl‐Knirsch,

Pointecker,

Pfistermüller

et al. 2024

Computer Graphics Forum

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User evaluation is a common and useful tool for systematically generating knowledge and validating novel approaches in the domain of Immersive Analytics. Since this research domain centres around users, user evaluation is of extraordinary relevance. Additionally, Immersive Analytics is an interdisciplinary field of research where different communities bring in their own methodologies. It is vital to investigate and synchronise these different approaches with the long‐term goal to reach a shared evaluation framework. While there have been several studies focusing on Immersive Analytics as a whole or on certain aspects of the domain, this is the first systematic review of the state of evaluation methodology in Immersive Analytics. The main objective of this systematic literature review is to illustrate methodologies and research areas that are still underrepresented in user studies by identifying current practice in user evaluation in the domain of Immersive Analytics in coherence with the PRISMA protocol. (see https://www.acm.org/publications/class-2012)

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PolyVis: Cross-Device Framework for Collaborative Visual Data Analysis

Cited by 4 publications

References 35 publications

Natural language interactions enhanced by data visualization to explore insurance claims and manage risk

Natural language interactions enhanced by data visualization to explore insurance claims and manage risk

Interactive Visualization on Large High‐Resolution Displays: A Survey

A Systematic Literature Review of User Evaluation in Immersive Analytics

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