DIVA: Data Intensive Visual Analytics - Provenance and Uncertainty in Human Terrain Analysis

DIVA：数据密集型可视化分析 - 人类地形分析中的起源和不确定性

• 批准号: EP/J020443/1
• 负责人: Joseph Wood
• 金额: $ 21.96万
• 依托单位: City, University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ020443%2F1
• 关键词: DIVA; Data; Intensive; Visual; Analytics

Data Intensive Visual Analytics can help address the data deluge by helping decision makers to rapidly reach informed and effective decisions in a range of situations.This exploratory project will apply DIVA to defence and security applications in close collaboration with DSTL. It will investigate visual methods for effectively utilising the kinds of dynamic and uncertain data that are emerging from multiple and frequently conflicting sources. Methods will be developed to store, communicate and use metadata about (potentially conflicting, uncertain and messy) data origins, quality and analytical process. They will be transferable and apply at operational and strategic levels.We draw together a team of UK academics with complimentary expertise. Contributors from Middlesex University and City University London have growing international reputations for developing innovative and applied visual analytics solutions and the theoretical work that supports this activity. Contributors from Loughborough University offer experience of information management and analysis in real time, harsh environments and the military context. The team will work closely to establish and evaluate the potential for DIVA in the area of Human Terrain Analysis.The programme of work is designed to ensure close engagement between academics and DSTL colleagues. Short bursts of concerted activity focusing around a series of participatory design workshops will result in rapid development and evaluation. These intense periods of coordinated co-located activity will stimulate subsequent reflection and respond to feedback involving DSTL in an iterative process. A continuous bridging presence over a 12 month elapsed period (one researcher working at two sites) will support and consolidate this work. These efforts will address critical research issues faced by the emerging academic VA community: * How can we best support analysts with information about data uncertainty and provenance? These factors underlie analytic approaches in data intensive systems yet many issues remain unresolved. * How can we capture, annotate and explain the analytic process? Doing so will enable us to reproduce the analytic process and support communication and collaborative analysis. * How do VA approaches apply in critical applications areas? Close collaboration with DSTL will ensure that academic developments are grounded in and informed by an applications domain that is vital to national security.The planned activity will produce schemas, methods and prototypes that address these questions, support analytical work and demonstrate DIVA potential in the military context.The results are likely to have application impact across MOD and in wider disciplines to which VA is being increasingly applied, including significant data intensive areas in science, industry and government. Findings will be communicated widely through national and international academic conferences, social media, press releases and at DSTL networking events. Software and functionality developed will be made available through a Creative Commons licence. Along with the knowledge derived through the planned research, this will be used by the UK Visual Analytics Community.The project offers significant value, using existing skills, equipment and technology, and has low start-up costs. No recruitment is necessary with all participants employed in dynamic and successful research groups at the three participating institutions: City University London (lead), Middlesex University and Loughborough University. The programme of activity involves 24 months of research time over 12 months elapsed time and fits in well with the schedules and workloads of world class researchers operating in the international arena. All are committed to the work plan, which will contribute to institutional objectives in all cases and is supported by the US National Visual Analytics Centre.

数据密集型可视化分析可以帮助决策者在各种情况下快速做出明智且有效的决策，从而帮助解决数据泛滥的问题。这个探索性项目将与 DSTL 密切合作，将 DIVA 应用到国防和安全应用程序中。它将研究有效利用从多个且经常冲突的来源中出现的动态和不确定数据的可视化方法。将开发方法来存储、交流和使用有关（潜在冲突、不确定和混乱）数据来源、质量和分析过程的元数据。它们可以在运营和战略层面上进行转移和应用。我们汇集了一支由具有互补专业知识的英国学者组成的团队。来自密德萨斯大学和伦敦城市大学的贡献者在开发创新和应用视觉分析解决方案以及支持这项活动的理论工作方面享有越来越高的国际声誉。拉夫堡大学的贡献者提供了实时、恶劣环境和军事背景下的信息管理和分析经验。该团队将密切合作，确定和评估 DIVA 在人类地形分析领域的潜力。该工作计划旨在确保学者和 DSTL 同事之间的密切接触。围绕一系列参与式设计研讨会的短期协调活动将导致快速开发和评估。这些协调一致的活动的紧张时期将激发后续反思并对涉及 DSTL 的迭代过程中的反馈做出响应。 12 个月内的持续桥接存在（一名研究人员在两个地点工作）将支持和巩固这项工作。这些努力将解决新兴学术 VA 社区面临的关键研究问题： * 我们如何才能最好地为分析师提供有关数据不确定性和来源的信息？这些因素是数据密集型系统中分析方法的基础，但许多问题仍未解决。 * 我们如何捕捉、注释和解释分析过程？ 这样做将使我们能够重现分析过程并支持沟通和协作分析。 * VA 方法如何应用于关键应用领域？与 DSTL 的密切合作将确保学术发展扎根于对国家安全至关重要的应用领域并为其提供信息。计划的活动将产生解决这些问题的模式、方法和原型，支持分析工作并展示 DIVA 在军事中的潜力结果可能会对 MOD 以及 VA 越来越多地应用的更广泛学科产生应用影响，包括科学、工业和政府中的重要数据密集型领域。研究结果将通过国内和国际学术会议、社交媒体、新闻稿和 DSTL 网络活动广泛传播。开发的软件和功能将通过知识共享许可证提供。连同通过计划研究获得的知识，英国视觉分析社区将使用这些知识。该项目利用现有技能、设备和技术提供了巨大的价值，并且启动成本较低。伦敦城市大学（牵头）、米德尔塞克斯大学和拉夫堡大学这三个参与机构的所有参与者都受雇于充满活力且成功的研究小组，因此无需招募任何参与者。该活动计划涉及 12 个月的 24 个月的研究时间，非常适合在国际舞台上工作的世界级研究人员的日程安排和工作量。所有人都致力于工作计划，这将有助于在所有情况下实现机构目标，并得到美国国家视觉分析中心的支持。