BDD: Efficient and Scalable Collection, Analytics and Processing of Big Data for Disaster Applications

BDD：灾难应用大数据的高效且可扩展的收集、分析和处理

基本信息

批准号：
1461914
负责人：
Sanjay Madria
金额：
$ 30万
依托单位：
Missouri University of Science and Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-04-01 至 2020-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1461914&HistoricalAwards=false
关键词：
BDD Efficient Scalable Collection Analytics

项目摘要

The outcomes from this project is to assist human operators in their disaster management coordination and planning, such as directing a medical physician's team to their nearest cluster of affected people in a region to administer medications as necessary, or finding a safe route for evacuation of affected people. Sensor data integrated with microblogs such as Tweets help identifying some local events and people's sentiments, which are significantly useful in handling/understanding disaster situations. It will also benefit other applications such as real-time tracking of road/driving conditions in vehicular networks.This research is conducted jointly with Osaka University in Japan, to benefit both the universities in enhancing not only their knowledge but also to learn global perspective in solving important problems. The research team is designing schemes for dynamic and collaborative data compression and multi-streams compression of multi-dimensional sensor data with error correction and recovery for addressing the energy efficiency and bandwidth limitation issues. Compression schemes exploit temporal locality and delta compression to provide better bandwidth utilization. Different methods for measuring error are designed and compared for the compressibility and actual error for variations in methods of utilizing the error tolerance. In addition, the team is developing algorithms for highly scalable indexing schemes for efficient data retrieval involving mainly range queries, top-k query, ranked-based searches and snapshot queries for multi-dimensional sensor data from different data sources to address the issue of timely dissemination. Hilbert Curve based linearization technique integrated with an overlay network is designed to (1) map multidimensional attributes onto a single dimension while preserving its data locality, and (2) to create a balanced network by associating only one node with each leaf of the virtual tree and then partition the multidimensional search space into subspaces and assign each node to a unique subspace. This allows an overlay network to start from a predefined prefix to handle data skewness. This research is also designing a scheme for using microblog messages as social sensors for efficient integration with other sensor data. We are using machine-learning techniques to match each message with its associate location based on the characteristics of the message. The results will be validated and evaluated using the sensor cloud test-bed available at Missouri S&T.

该项目的结果是协助人类运营商进行灾难管理协调和计划，例如将医疗医师的团队指导到他们在该地区最近受影响的人群中，以根据需要进行药物治疗，或者找到安全撤离受影响人的途径。与微博集成的传感器数据，例如Tweets，有助于识别一些当地事件和人们的情感，这些事件和人们的情感在处理/理解灾难情况下非常有用。它还将受益于其他应用，例如对车辆网络中道路/驾驶条件的实时跟踪。这项研究与日本的大阪大学共同进行，以使两家大学受益，不仅可以增强其知识，而且还可以学习解决重要问题的全球观点。研究团队正在设计用于动态和协作数据压缩的方案，并设计具有错误校正和恢复的多维传感器数据的多流式压缩，以解决能源效率和带宽限制问题。压缩方案利用时间区域和增量压缩以提供更好的带宽利用率。设计和比较了不同的测量误差方法的方法，以使用误差耐性方法的可压缩性和实际误差。此外，该团队正在开发用于高度可扩展的索引方案的算法，以进行有效的数据检索，其中涉及范围查询，TOP-K查询，基于排名的基于排名的搜索和快照查询以及来自不同数据源的多维传感器数据的快照查询，以解决及时散发问题的问题。基于Hilbert曲线与覆盖网络集成的基于Hilbert曲线的线性化技术的设计旨在（1）将多维属性映射到单个维度上，同时保留其数据局部性，（2）通过将一个节点与虚拟树的每个叶子相关联，然后将一个节点与虚拟树的每个叶子关联，然后将多维搜索空间分配为每个Node supspace并分配每个Node subspace，并将其分配给独特的subspace。这允许覆盖网络从预定义的前缀开始以处理数据偏斜。这项研究还设计了一种计划，用于使用微博信息作为社会传感器，以与其他传感器数据有效整合。我们正在使用机器学习技术根据消息的特征将每个消息及其关联位置匹配。将使用密苏里州S＆T的传感器云测试床对结果进行验证和评估。