III: Distributed Stream Integration

III：分布式流集成

• 批准号: 0713267
• 负责人: Zachary Ives
• 金额: --
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713267&HistoricalAwards=false
• 关键词: III; Distributed; Stream; Integration

Declarative, database-style models for programming distributed applications are becoming widely adopted, in a variety of realms ranging from sensors to publish-subscribe to network state management. They free the developerto define high-level queries for the specific data of interest,without regard to details about data sources, communications protocols,or synchronization.As this approach to programming gains momentum, there is increasing needto abstract low-level stream data source variations away under a uniform representation, i.e., a view; and to integrate, i.e., conjoin, different types of stream data from large numbers of sources. Such tasks involvemuch more distributed communication and coordination than in traditionaldistributed databases or even data stream management systems. It becomes essential to do in-network computation of the query, and to optimize the processing of each stream (or few streams) separately, in a way that considers the topology of the network.This proposal develops the technologies to support integration of data streams, including languages for stream schema mappings, focusing on issues relatingto combining distributed messages and maintaining timing information;techniques for rapidly establishing query computation paths through a network,for sets of data stream elements that need to be joined and aggregated together; offline and adaptive, network-aware query optimization techniques fordistributed computation in the network. These techniques will scaleacross widely heterogeneous (sensor, wireless, andconventional) networks, and will be evaluated in environmental monitoringapplications.The intellectual merit is the development of new techniques for performingqueries across large, highly distributed networks of stream-producing sources;this increases understanding of the adaptive query processing space when accesscosts to data items are non-uniform and query processing requires distributedcommunication, and the trade-offs with respect to offline versus adaptiveoptimization and relative to optimization granularity. The broader impactincludes the development of distributed stream integration capabilities thatcan directly address a number of emerging and well-known challenges in the network and environmental monitoring domains. The educational component includes the training of two PhD students, and the teaching of stream data integration in graduate and advanced undergraduate courses.Project URL: http://www.cis.upenn.edu/~zives/stream-integration/

用于编程分布式应用程序的声明式数据库风格的模型正在被广泛采用，从传感器到网络状态管理的各种领域。 它们解放了开发人员为感兴趣的特定数据定义高级查询，而不考虑有关数据源、通信协议或同步的细节。随着这种编程方法的发展，越来越需要将低级流数据源变体抽象到统一的表示下，即，一个视图;并整合，即，将来自大量源的不同类型的流数据结合起来。 这样的任务比传统的分布式数据库甚至数据流管理系统涉及更多的分布式通信和协调。必须对查询进行网络内计算，并优化每个流的处理该方案开发了支持数据流集成的技术，包括流模式映射语言，重点关注与组合分布式消息和维护定时信息有关的问题;用于通过网络快速建立查询计算路径的技术，用于需要连接和聚合在一起的数据流元素集;离线和自适应，网络感知查询优化技术，用于网络中的分布式计算。These techniques技术will scale规模across横过wide广泛heterogeneous异构（传感器、无线和传统）网络，并将在环境监测应用中进行评估。其智力价值是开发新技术，用于在大型、高度分布式的流产生源网络中执行查询;这增加了当对数据项的访问成本不一致并且查询处理需要分布式通信时对自适应查询处理空间的理解，以及关于离线优化与自适应优化以及关于优化粒度的权衡。 更广泛的影响包括分布式数据流集成能力的发展，这些能力可以直接解决网络和环境监测领域中一些新兴和众所周知的挑战。 教育部分包括两名博士生的培训，以及研究生和高级本科课程中的流数据集成教学。项目URL：http://www.cis.upenn.edu/~zives/stream-integration/