Weighted Random Sampling Based Hierarchical Amnesic Synopses for Data Streams

摘要：

Maintaining a synopsis structure dynamically from data stream is vital for a variety of streaming data applications, such as approximate query or data mining. In many cases, the significance of data item in streams decays with age: this item perhaps conveys critical information first, but, as time goes by, it gets less and less important until it eventually becomes useless. This characteristic is termed amnesic. Random Sampling is often used in construction of synopsis for streaming data. This paper proposed a Weighted Random Sampling based Hierarchical Amnesic Synopses which includes the amnesic characteristic of data stream in the generation of synopsis. The construction methods for weighted random sampling with and without replacement are discussed. We experimentally evaluate the proposed synopsis structure.

关键词： data streams:sampling:synopses:amnesic

作者: CHEN Hua-Hui LIAO Kang-Li

作者单位: College of Information Science and Engineering Ningbo University Ningbo ,China

会议类型: 国际会议

会议名称: The 5th International Conference on Computer Science & Education(第五届国际计算机新技术与教育学术研讨会 ICCSE10)

会议地点: 合肥

会议语种:英文

页码: 1375-1379

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）