III: Visual Analytics for Steering Large-Scale Distributed Data Mining Applications

该项目的主要目标是提供一种新的框架和软件，使用户能够更有效地理解和应用在大型分布式数据环境中发现的信息。该方法是提供经过测试的新颖数据分析技术，由集成了专业知识和人类用户洞察力的框架提供支持。人类用户的技能、能力和经验与分布式计算的纯粹处理能力的新组合提供了非凡的协同信息处理潜力——称为交互式自动化。该框架明确地将用户置于设计、执行和分析处理模式的中心，允许他们在这些上下文之间流畅地切换，以及对复杂、分布式和动态数据集的运行时处理做出反应和指导。第二个目标是在执法领域提供一套权威、准确、匿名和公开的地面真相数据。目前还没有这样的地面真实数据集，目前的分析工具是使用专有的、机密的或其他封闭的数据集进行评估的。系统模块的稳定版本、文档、相关出版物和可用性研究结果可在项目网站（www.dimacs.rutgers.edu）上获得。这项工作的更广泛影响在于我们的区域合作伙伴（执法、医学和教育领域），以及通过在学术界和工业界传播数据集和软件而产生的广泛影响。此外，数据集的可用性将为执法数据分析工具的客观、比较和科学分析提供基础，从而支持相关的研究工作。

The primary goal of this project is to provide a novel framework and software that will empower users to more effectively understand and apply information discovered in large distributed data environments. The approach is to provide tested and novel data analytics techniques, supported by a framework that integrates expertise and insight of human users. The novel combination of the skills, abilities and experience of human users with the sheer processing power of distributed computation provides an extraordinary synergistic information-processing potential -- termed Interactive Automation. This framework places the user explicitly in the center of the Design, Execution, and Analysis processing modes, allowing them to switch between these contexts fluidly as well as react and guide the runtime processing of complex, distributed and dynamic datasets. A secondary goal is to provide an authoritative, accurate, anonymized and openly available set of ground truth data in the law enforcement domain. At present, no such ground truth dataset currently exists, and currently analytics tools are evaluated using proprietary, confidential or otherwise closed datasets. Stable releases of the system modules, documentation, related publications and results from usability studies are available at the project website (www.dimacs.rutgers.edu). The broader impacts of this work lie regionally with our partners (in law enforcement, medicine and education), and broadly through dissemination of the dataset and software in academia and industry. In addition, the availability of the dataset will support related research efforts by providing a foundation for objective, comparative and scientific analysis of law enforcement data analytics tools.