Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitor...Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitoring of environment, building infrastructures and human health. Many researchers view the sensor networks as databases, and the monitoring tasks are performed as subscriptions, queries, and alert. However, this point is not precise. First, databases can only deal with well-formed data types, with well-defined schema for their interpretation, while the raw data collected by the sensor networks, in most cases, do not fit to this requirement. Second, sensor networks have to deal with very dynamic targets, environment and resources, while databases are more static. In order to fill this gap between sensor networks and databases, we propose a novel approach, referred to as 'spatiotemporal data stream segmentation', or 'stream segmentation' for short, to address the dynamic nature and deal with 'raw' data of sensor networks. Stream segmentation is defined using Bayesian Networks in the context of sensor networks, and two application examples are given to demonstrate the usefulness of the approach.展开更多
设计了基于 PDM(i MAN)的 CAPP系统的构架 ,着重研究了 CAPP与 i MAN的集成、工艺数据的数据库存储与 JAVA本地动态连接库调用等关键技术。在此基础上开发了基于 i MAN的 CAPP系统—— i MAN CAPP,通过 PDM(i MAN)这一 CAD/CAPP/CAM的...设计了基于 PDM(i MAN)的 CAPP系统的构架 ,着重研究了 CAPP与 i MAN的集成、工艺数据的数据库存储与 JAVA本地动态连接库调用等关键技术。在此基础上开发了基于 i MAN的 CAPP系统—— i MAN CAPP,通过 PDM(i MAN)这一 CAD/CAPP/CAM的集成平台 ,不仅可以实现对工艺设计过程的管理 ,还可以与同样构造在PDM(i MAN)平台上的其它应用系统紧密集成。该系统已在企业运行 ,取得了满意的效果。展开更多
文摘Sensor networks provide means to link people with real world by processing data in real time collected from real-world and routing the query results to the right people. Application examples include continuous monitoring of environment, building infrastructures and human health. Many researchers view the sensor networks as databases, and the monitoring tasks are performed as subscriptions, queries, and alert. However, this point is not precise. First, databases can only deal with well-formed data types, with well-defined schema for their interpretation, while the raw data collected by the sensor networks, in most cases, do not fit to this requirement. Second, sensor networks have to deal with very dynamic targets, environment and resources, while databases are more static. In order to fill this gap between sensor networks and databases, we propose a novel approach, referred to as 'spatiotemporal data stream segmentation', or 'stream segmentation' for short, to address the dynamic nature and deal with 'raw' data of sensor networks. Stream segmentation is defined using Bayesian Networks in the context of sensor networks, and two application examples are given to demonstrate the usefulness of the approach.
