Continuous Outlier Monitoring on Uncertain Data Streams

IMR OpenIR

	Continuous Outlier Monitoring on Uncertain Data Streams
其他题名	Continuous Outlier Monitoring on Uncertain Data Streams
	Cao KeYan 1; Wang GuoRen 1; Han DongHong 1; Ding GuoHui 2; Wang AiXia 1; Shi LingXu 3
	2014
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
卷号	29 期号:3 页码:436-448
摘要	Outlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. We propose Continuous Uncertain Outlier Detection (CUOD), which can quickly determine the nature of the uncertain elements by pruning to improve the efficiency. Furthermore, we propose a pruning approach Probability Pruning for Continuous Uncertain Outlier Detection (PCUOD) to reduce the detection cost. It is an estimated outlier probability method which can effectively reduce the amount of calculations. The cost of PCUOD incremental algorithm can satisfy the demand of uncertain data streams. Finally, a new method for parameter variable queries to CUOD is proposed, enabling the concurrent execution of different queries. To the best of our knowledge, this paper is the first work to perform outlier detection on uncertain data streams which can handle parameter variable queries simultaneously. Our methods are verified using both real data and synthetic data. The results show that they are able to reduce the required storage and running time.
其他摘要	Outlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. We propose Continuous Uncertain Outlier Detection (CUOD), which can quickly determine the nature of the uncertain elements by pruning to improve the efficiency. Furthermore, we propose a pruning approach - Probability Pruning for Continuous Uncertain Outlier Detection (PCUOD) to reduce the detection cost. It is an estimated outlier probability method which can effectively reduce the amount of calculations. The cost of PCUOD incremental algorithm can satisfy the demand of uncertain data streams. Finally, a new method for parameter variable queries to CUOD is proposed, enabling the concurrent execution of different queries. To the best of our knowledge, this paper is the first work to perform outlier detection on uncertain data streams which can handle parameter variable queries simultaneously. Our methods are verified using both real data and synthetic data. The results show that they are able to reduce the required storage and running time.
关键词	PROBABILISTIC DATA outlier detection uncertain data stream data mining parameter variable query
收录类别	CSCD
语种	英语
资助项目	[National Natural Science Foundation of China] ; [National High Technology Research and Development 863 Program of China] ; [National Basic Research 973 Program of China]
CSCD记录号	CSCD:5130627
引用统计	被引频次：5[CSCD] [CSCD记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/155277
专题	中国科学院金属研究所
作者单位	1.东北大学 2.中国科学院金属研究所 3.Logist Engn University Peoples Liberat Army, Dept Command Informat Syst Engn, Chongqing 400311, Peoples R China
推荐引用方式 GB/T 7714	Cao KeYan,Wang GuoRen,Han DongHong,et al. Continuous Outlier Monitoring on Uncertain Data Streams[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2014,29(3):436-448.
APA	Cao KeYan,Wang GuoRen,Han DongHong,Ding GuoHui,Wang AiXia,&Shi LingXu.(2014).Continuous Outlier Monitoring on Uncertain Data Streams.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,29(3),436-448.
MLA	Cao KeYan,et al."Continuous Outlier Monitoring on Uncertain Data Streams".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 29.3(2014):436-448.