Stream-Based Real World Information Integration Framework
Stream-Based Real World Information Integration Framework
复制标题
DOI：
10.1007/978-3-642-13965-9_6
复制
发表时间：
2010-09
期刊：
影响因子：
--
通讯作者：
H. Kitagawa;Yousuke Watanabe;H. Kawashima;Toshiyuki Amagasa
中科院分区：
文献类型：
--
作者：
H. Kitagawa;Yousuke Watanabe;H. Kawashima;Toshiyuki Amagasa
对于面向现实世界的应用程序，要轻松使用从多个无线传感器网络获得的传感器数据，数据管理基础设施是必需的。基础设施设计应该基于超越关系数据管理的新框架的理念，原因有两个：首先是数据的新鲜度。为了保持传感器数据的新鲜，基础设施应该有效地处理数据；这意味着传统的耗时事务处理方法是不合适的。二是功能的多样性。传感器数据应用的主要目的是检测事件；不幸的是，关系操作符对这一目的贡献不大。本章提供了一个直接支持高效处理和各种高级功能的框架。流处理是该框架的关键概念。针对数据流查询处理的效率要求，提出了一种多查询优化技术；我们还提出了一种高效的数据归档技术。为了满足功能需求，我们提出了几种事件检测技术，包括复杂事件处理、概率推理和连续媒体集成。
For real world oriented applications to easily use sensor data obtained frommultiple wireless sensor networks, a data management infrastructure is mandatory. The infrastructure design should be based on the philosophy of a novel framework beyond the relational data management for two reasons: First is the freshness of data. To keep sensor data fresh, an infrastructure should process data efficiently; this means conventional time consuming transaction processing methodology is inappropriate. Second is the diversity of functions. The primary purpose of sensor data applications is to detect events; unfortunately, relational operators contribute little toward this purpose. This chapter presents a framework that directly supports efficient processing and a variety of advanced functions. Stream processing is the key concept of the framework. Regarding the efficiency requirement, we present a multiple query optimization technique for query processing over data streams; we also present an efficient data archiving technique. To meet the functions requirement, we present several techniques on event detection, which include complex event processing, probabilistic reasoning, and continuous media integration.