中文摘要：

在数据流上监视是获得数据流的人物的一个有效方法。然而，为每数据流的可用资源是有限的，因此怎么使用有限资源处理无限的数据流的问题是一个开的质问问题。在这篇论文，我们采用小浪和滑动窗口方法设计多决定摘要数据结构，能与输入数据逐渐地被更新的多决定摘要树(MRST ) 和罐头支持削尖询问，范围查询，多点的询问并且保留询问的精确。我们使用合成数据和真实世界的数据评估我们的算法。实验的结果显示质问的效率和 MRST 的适应性超过了当前的算法，同时它的实现比其它简单。电子增补材料电子增补材料为在 http://dx.doi.org/10.1007/s11390-007-9025-7 的这篇文章是可得到的并且为授权的用户可存取。

英文摘要：

Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree （MRST） which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others.