【作者】 刘琴；

【导师】 金远平；

【作者基本信息】 东南大学 ， 计算机应用， 2006， 硕士

【摘要】 近年来,随着网络,电信和传感器技术的发展,数据库技术得到了广泛的应用。以传统的数据库理论为基础,发展出现了多种多样的新型数据库管理系统。数据流管理系统是目前热门的分支之一,因为它能处理现存的大量问题,如网络点击流,股市数据流等。数据流查询的特点是瞬时的、连续的和长时间的。正因为数据流模型在许多应用中扮演着重要的角色,基于数据流模型的查询处理技术也得到了广泛的研究。为了提高系统的查询速度,需要在中心节点优化调整过滤器的顺序,减少输入数据量。但目前过滤器的排序是依据过去的数据进行,并没考虑到现在及将来数据特点。本文将kalman预测滤波器引入具有自调节功能的过滤器(adaptive filter)模块中,即中心节点提前参考预测数据的特点,合理地制定出查询优化策略,提高查询优化算法的效率。并且在源点也设置了带有预测功能的kalman滤波器,增强了源点和中心结点的智能性,减少了网络传输的数据。同时,我们用消息机制触发更新数据,有效地控制了源点和中心结点预测数据的同步,保证双方结点数据的一致性。更多还原

【Abstract】 Database techniques have been widely used, with the development of network, telegraphy and sensor technology in recent years. On the basis of traditional database , many new kinds of database management system (DBMS) have been developed. Data stream management system (DSMS) is one of the hot research topic, because it can handle a lot of practical problems, such as network attacking data stream, stock data stream and so on. Query of data stream has the characteristic of being instantaneous, continual and long time .Just because the model of data stream plays an important part in many applications, much research has been done on the technology of data stream query .In order to increase the speedy of query, we need to adjust the order of filter , and reduce input data . But in existing approaches, the order of filters is based on past data , without considering present data and future data.In this paper, we introduce kalman filter to predict future data. So according to the predicted data , adaptive filters in center node can be optimized to improve query efficiency. Also, we install kalman filter in the source node, strengthen the intelligence of both nodes, and reduce data to be s transferred. Meantime, we use message to trigger update, so that we can synchronize the data of source node with the center node, to keep the data consistent.更多还原