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

提案编号：CNS 11- 38666 PI：Pu，Calton机构：格鲁吉亚理工学院标题： 快速：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：这个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。这项工作通过整合、评估和增强当前的数据管理工具，特别是元数据，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人类操作员的时间压力。基本工具支持质量信息维度，如来源、及时性、安全性、隐私和保密性，从而能够长期对传感器数据进行适当的解释。从短期来看，这些工具有望帮助减轻作为数据生产者和消费者的工作人员的负担;从长期来看，它们将为灾后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Open Cirrus的CERCS集群的实施为日本的恢复工作以及全球研究人员提供了高可用性和易用性。从几个信息维度（例如，数据来源、监控和隐私）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所提议的研究的智力价值。信息质量维度之间的潜在干扰将面临新的挑战。在软件工具的改编中应用代码生成技术，以适应环境破坏和各国之间的背景和文化差异所带来的变化，也是一个新的挑战。研究人员与日本东京大学的Masaru Kitsuregawa教授合作，他是数据管理领域的领先研究员。他是亚洲第一位获得ACM SOGMOD创新奖（2009年）的数据库研究员。除了一封支持信和日本合作者的简历外，英特尔还向OISE、CISE和工程部提交了一封支持信。更广泛的影响：建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，农产品中测量到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。该项目能够根据对真实的传感器数据的精确解释做出明智的决策，这可能会提高人类和社会层面的生活质量，同时降低成本。该项目还将为研究生教育做出贡献。

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.