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›› 2014, Vol. 29 ›› Issue (3): 436-448.

Special Issue: Data Management and Data Mining

• Data Management and Data Mining •

### Continuous Outlier Monitoring on Uncertain Data Streams

Ke-Yan Cao1 (曹科研), Guo-Ren Wang1, 2 (王国仁), Senior Member, CCF Dong-Hong Han1, 2 (韩东红), Member, CCF, Guo-Hui Ding3 (丁国辉), Ai-Xia Wang1 (王爱侠) and Ling-Xu Shi4 (石凌旭)

1. 1 College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
2 Key Laboratory of Medical Image Computing, Ministry of Education, Northeastern University, Shenyang 110819, China;
3 College of Computer, Shenyang Aerospace University, Shenyang 110819, China;
4 Department of Command Information System Engineering, Logistic Engineering University of People's Liberation Army Chongqing 400311, China
• Received:2013-07-02 Revised:2014-04-04 Online:2014-05-05 Published:2014-05-05
• About author:Ke-Yan Cao is a Ph.D. candidate at Northeastern University, Shenyang. Her research interests include data mining, uncertain data management and data stream management.
• Supported by:

The work is supported by the National Natural Science Foundation of China under Grant Nos. 61025007, 61328202, 61173029, 61100024, 61332006, and 61073063, the National High Technology Research and Development 863 Program of China under Grant No. 2012AA011004, and the National Basic Research 973 Program of China under Grant No. 2011CB302200-G.

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 effciency. 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.

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