RAPID: Automating Emergency Data and Metadata Management to Support Effective Short Term and Long Term Disaster Recovery Efforts

RAPID：自动化应急数据和元数据管理，支持有效的短期和长期灾难恢复工作

• 批准号: 1138666
• 负责人: Calton Pu
• 金额: $ 5万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1138666&HistoricalAwards=false
• 关键词: RAPID; Automating; Emergency; Data; Metadata

Proposal #: CNS 11-38666PI(s): Pu, CaltonInstitution: Georgia Institute of TechnologyTitle: RAPID: Automating Emergency Data and Metadata Management to Support Effective Short and Long Term Disaster Recovery EffortsProject Proposed:This RAPID project, collecting, processing, and disseminating appropriate sensor data, aims to contribute to an effective recovery. The work addresses the challenges of sensor data flood during an emergency, through integration, evaluation, and enhancement of current data management tools, particularly with respect to meta-data. Automation of data and meta-data collection, processing, and dissemination are expected to alleviate the time pressure on human operators. The fundamental tools support quality information dimensions such as provenance, timeliness, security, privacy, and confidentiality, enabling an appropriate interpretation of the sensor data in the long term. For the short term, the tools are expected to help relief the workers as data producers and consumers; for the long term, they will provide high quality information for disaster recovery decision support systems. Additionally, the cloud-based system architecture and implementation of the CERCS cluster of Open Cirrus provide high availability and ease of access for recovery efforts in Japan as well as for researchers worldwide. The integration of techniques from several information dimensions (e.g., data provenance, surety, and privacy) and the application of code generation techniques to automate the data and metadata management tools constitute the intellectual merit of the proposed research. New challenges will be encountered in the potential interferences among the quality of information dimensions. It is also a new challenge to apply code generation techniques in the adaptation of software tools to accommodate changes imposed by environmental damages and contextual as well as cultural differences among countries.The investigator collaborates with Prof. Masaru Kitsuregawa from the University of Tokyo, Japan, a leading researcher in data management. He is the first database researcher from Asia to win the ACM SOGMOD Innovation Award (2009). In addition to a letter of support and biographical sketches of the Japanese collaborator, a support letter has been submitted by Intel to OISE, CISE and Engineering. Intel has offered access to the Intel Open Cirrus cluster to conduct the research.Broader Impacts: The proposed tools should contribute to improve both the quantity and quality of data being collected by a variety of sensors, thus improving the effectiveness of short and long term decision making. For example, measured radiation levels in agricultural products can serve as an indication of spreading radioactive contaminations that complement the direct readings of radiation in soil samples. The project enables informed decisions based on precise interpretation of real sensor data that may improve the quality of life at both human and social levels, while reducing costs. The project will also contribute in graduate student education.

提案号：CNS 11- 386666 pi (s): Pu， calton机构：Georgia Institute of technology标题：RAPID：自动化应急数据和元数据管理以支持有效的短期和长期灾难恢复工作项目建议：该RAPID项目收集、处理和传播适当的传感器数据，旨在促进有效的恢复。通过整合、评估和增强当前数据管理工具，特别是元数据管理工具，这项工作解决了紧急情况下传感器数据泛滥的挑战。数据和元数据收集、处理和传播的自动化有望减轻操作员的时间压力。基本工具支持质量信息维度，如来源、及时性、安全性、隐私性和机密性，从而能够长期对传感器数据进行适当的解释。在短期内，这些工具有望帮助缓解作为数据生产者和消费者的工人；从长远来看，它们将为灾难恢复决策支持系统提供高质量的信息。此外，基于云的系统架构和开放卷云CERCS集群的实现为日本以及全球的研究人员提供了高可用性和易于访问的恢复工作。集成来自多个信息维度的技术（例如，数据来源、保证和隐私）以及应用代码生成技术来自动化数据和元数据管理工具，构成了本研究的智力价值。信息维度质量之间的潜在干扰将面临新的挑战。将代码生成技术应用于软件工具的调整，以适应环境破坏和各国之间的背景和文化差异所造成的变化，这也是一项新的挑战。研究者与日本东京大学的Masaru Kitsuregawa教授合作，他是数据管理领域的领先研究人员。他是亚洲第一位获得ACM SOGMOD创新奖（2009年）的数据库研究人员。除了一封支持信和日本合作者的个人简介外，英特尔还向OISE、CISE和Engineering提交了一封支持信。英特尔已经提供访问英特尔开放卷云集群来进行研究。更广泛的影响：拟议的工具应有助于提高各种传感器收集的数据的数量和质量，从而提高短期和长期决策的有效性。例如，测量农产品中的辐射水平可以作为放射性污染扩散的指示，补充土壤样品中辐射的直接读数。该项目可以根据对真实传感器数据的精确解释做出明智的决策，从而提高人类和社会的生活质量，同时降低成本。该项目还将有助于研究生教育。