Affiliation:
1. The University of Oklahoma, Norman OK
Abstract
In applications, such as sensor networks and power usage monitoring, data are in the form of streams, each of which is an infinite sequence of data points with explicit or implicit timestamps and has special characteristics, such as transiency, uncertainty, dynamic data distribution, multidimensionality, and dynamic relationship. These characteristics introduce new research issues that make outlier detection for stream data more challenging than that for regular (non-stream) data. This paper discusses those research issues for applications where data come from a single stream as well as multiple streams.
Publisher
Association for Computing Machinery (ACM)
Reference54 articles.
1. Aurora
2. R. Motwani J. Widom A. Arasu B. Babcock S. Babu M. Datar G. Manku C. Olston J. Rosenstein and R. Varma "Query Processing Resource Management and Approximation ina Data Stream Management System " Stanford InfoLab 2002. R. Motwani J. Widom A. Arasu B. Babcock S. Babu M. Datar G. Manku C. Olston J. Rosenstein and R. Varma "Query Processing Resource Management and Approximation ina Data Stream Management System " Stanford InfoLab 2002.
3. D. J. Abadi Y. Ahmad M. Balazinska U. ?etintemel M. Cherniack J.-H. Hwang W. Lindner A. Maskey A. Rasin E. Ryvkina N. Tatbul Y. Xing and S. B. Zdonik "The Design of the Borealis Stream Processing Engine " Innovative Data Systems Research 2005. D. J. Abadi Y. Ahmad M. Balazinska U. ?etintemel M. Cherniack J.-H. Hwang W. Lindner A. Maskey A. Rasin E. Ryvkina N. Tatbul Y. Xing and S. B. Zdonik "The Design of the Borealis Stream Processing Engine " Innovative Data Systems Research 2005.
4. SStreaMWare
Cited by
45 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献