ModViz: A Modular and Extensible Architecture for Drill-Down and Visualization of Complex Data
ModViz: A Modular and Extensible Architecture for Drill-Down and Visualization of Complex Data
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ModViz：用于复杂数据的深入分析和可视化的模块化和可扩展架构
DOI：
10.1007/978-3-031-09850-5_16
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
David Rademacher, Jacob Valdez
中科院分区：
文献类型：
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作者：
David Rademacher, Jacob Valdez
对可能经常变化或实时变化的数据集的分析包括至少三个重要的同步组件：i）找出要推断的内容（目标），ii）对这些目标的分析或计算，以及iii）对可能需要深入和/或可视化的结果的理解。有相当多的研究对上述组件的前两个，而通过可视化理解可操作的推理还没有得到妥善解决。可视化是理解（特别是非专家）和推断需要采取的行动的重要一步。例如，对于Covid-19，了解地区（例如，在县或州一级），已经看到了一个高峰或倾向于在不久的将来高峰可能需要额外的行动，关于聚会，业务开放时间等。本文重点介绍了一个模块化的和可扩展的架构，可视化的基础和分析数据。本文提出了一个模块化的架构，仪表板的用户交互，可视化管理，支持基础数据的复杂分析。本文的贡献是：i）架构的可扩展性提供了灵活性，可以在不改变工作流程的情况下添加额外的分析，可视化和用户交互，ii）功能模块的解耦，以简化和加快不同团队的开发，以及iii）支持并发用户并解决显示响应时间的效率问题。本文使用多层网络（或MLN）进行分析。为了展示上述内容，我们提出了一个可视化仪表板的架构，termedCoWiz++（用于CovidWizard），并详细说明了如何基于Web的用户交互和显示组件与后端模块无缝接口。
Analysis of data sets that may be changing often or in real-time, consists of at least three important synchronized components:i)figuring out what to infer (objectives),ii)analysis or computation of those objectives, andiii)understanding of the results which may require drill-down and/or visualization. There is considerable research on the first two of the above components whereas understanding actionable inferences through visualization has not been addressed properly. Visualization is an important step towards both understanding (especially by non-experts) and inferring the actions that need to be taken. As an example, for Covid-19, knowing regions (say, at the county or state level) that have seen a spike or are prone to a spike in the near future may warrant additional actions with respect to gatherings, business opening hours, etc. This paper focuses on a modular and extensible architecture for visualization of base as well as analyzed data.This paper proposes a modular architecture of a dashboard for user interaction, visualization management, and support for complex analysis of base data. The contributions of this paper are: i) extensibility of the architecture providing flexibility to add additional analysis, visualizations, and user interactions without changing the workflow, ii) decoupling of the functional modules to ease and speed up development by different groups, and iii) supporting concurrent users and addressing efficiency issues for display response time. This paper uses Multilayer Networks (or MLNs) for analysis.To showcase the above, we present the architecture of a visualization dashboard, termedCoWiz++(forCovidWizard), and elaborate on how web-based user interaction and display components are interfaced seamlessly with the back-end modules.