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

该项目的成果是帮助人类操作员进行灾害管理协调和规划，例如将医疗队引导到一个地区最近的受影响人群群，在必要时给予药物治疗，或找到一条安全的路线来疏散受影响的人。传感器数据与推特等微博相结合，有助于识别一些当地事件和人们的情绪，这在处理/了解灾害情况方面非常有用。它还将有利于其他应用，如实时跟踪车辆网络中的道路/驾驶状况。这项研究是与日本大阪大学联合进行的，不仅有利于两所大学提高知识，还有助于学习解决重要问题的全球视野。研究小组正在设计多维传感器数据的动态和协作数据压缩方案，以及具有纠错和恢复功能的多流压缩方案，以解决能源效率和带宽限制问题。压缩方案利用时间局部性和增量压缩来提供更好的带宽利用率。设计了不同的误差测量方法，并对不同误差利用方法的压缩性和实际误差进行了比较。此外，该小组正在开发高度可伸缩的索引方案的算法，以实现高效的数据检索，主要涉及对来自不同数据源的多维传感器数据的范围查询、Top-k查询、基于排名的搜索和快照查询，以解决及时传播的问题。基于希尔伯特曲线的线性化技术与覆盖网络相结合，旨在(1)将多维属性映射到一维上，同时保持其数据局部性；(2)通过仅将一个节点与虚拟树的每一叶相关联来创建平衡网络，然后将多维搜索空间划分为子空间，并将每个节点分配给唯一的子空间。这允许覆盖网络从预定义的前缀开始处理数据偏斜。这项研究还设计了一种方案，将微博消息用作社交传感器，以便与其他传感器数据进行有效集成。我们正在使用机器学习技术，根据消息的特征将每条消息与其关联位置进行匹配。结果将使用密苏里S公司提供的传感器云试验台进行验证和评估。

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.