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

该项目的成果是协助人类操作员进行灾害管理协调和规划，例如指导医生团队前往某个地区最近的受影响人群，在必要时给予药物治疗，或为受影响人群寻找安全的疏散路线。传感器数据与微博（如推文）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。这项研究是与日本大坂大学合作进行的，不仅有助于提高两所大学的知识水平，而且有助于学习解决重要问题的全球视角。该研究团队正在设计多维传感器数据的动态和协作数据压缩以及多流压缩的方案，并进行纠错和恢复，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，并对误差容限利用方法的可压缩性和实际误差进行了比较。此外，该团队正在开发用于高效数据检索的高度可扩展索引方案的算法，主要涉及范围查询，top-k查询，基于排名的搜索和来自不同数据源的多维传感器数据的快照查询，以解决及时传播的问题。基于Hilbert曲线的线性化技术与覆盖网络相结合，旨在（1）将多维属性映射到单个维度上，同时保持其数据局部性，以及（2）通过仅将一个节点与虚拟树的每个叶子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，并将每个节点分配到唯一的子空间。这允许覆盖网络从预定义的前缀开始以处理数据偏斜。本研究也设计一个方案，使用微博消息作为社会传感器与其他传感器数据的有效整合。我们正在使用机器学习技术，根据消息的特征将每条消息与其关联位置进行匹配。将使用密苏里州ST的传感器云测试台对结果进行验证和评估。

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.