Visual Analysis of Proximal Temporal Relationships of Social and Communicative Behaviors

Han, Yi; Rozga, Agata; Dimitrova, Nevena; Abowd, Gregory D.; Stasko, John

doi:10.1111/cgf.12617

Cited by 15 publications

(2 citation statements)

References 24 publications

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“…It helps experts and decision makers to improve their understanding and to better deal with ricks in the insurance industry. Visualization and visual analytics have been introduced both in academia and industry: 1) to provide a clear view of customers adverse behavior, transactions monitoring, premium fluctuations, and in complex everyday decision-making [19,11,39]; 2) to characterize data, user and task [27,10,45]; and 3) discovering imbalances and monitoring risk [14]. Whereas some contributions are domain-specific, e.g., visual animation is adopted to investigate the vast amounts of time-series dataset [53,7,46].…”

Section: Visualization For Claims and Risk Managementmentioning

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: Visualization For Claims and Risk Managementmentioning

confidence: 99%

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

et al. 2022

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“…Summarization of event sequences aims to use intuitive representations to reveal major progression patterns and featured groupings of the sequence entities. In many domains such as health informatics [7], [28], [38], [73], [74], [78], [89], social media [57], [76], and career design [36], [37], a variety of analytical tasks serve the purpose of generating summaries, including explicit summarization, inexplicit summarization, progressional analysis, and clustering.…”

Section: Visual Summarizationmentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

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Design Research on Visualization of Life Behavior and Rhythm

Chen³

2021

Design, User Experience, and Usability: UX Research and Design

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Visual Analysis of Proximal Temporal Relationships of Social and Communicative Behaviors

Cited by 15 publications

References 24 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

Visual Progression Analysis of Event Sequence Data

Design Research on Visualization of Life Behavior and Rhythm

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