Attribute outlier detection over data streams

Hui Cao; Yongluan Zhou; Lidan Shou; Gang Chen

doi:10.1007/978-3-642-12098-5_17

Attribute outlier detection over data streams

Hui Cao, Yongluan Zhou, Lidan Shou, Gang Chen

6 Citations (Scopus)

Abstract

Outlier detection is widely used in many data stream application, such as network intrusion detection, fraud detection, etc. However, most existing algorithms focused on detecting class outliers and there is little work on detecting attribute outliers, which considers the correlation or relevance among the data items. In this paper we study the problem of detecting attribute outliers within the sliding windows over data streams. An efficient algorithm is proposed to perform exact outlier detection. The algorithm relies on an efficient data structure, which stores only the necessary information and can perform updates incurred by data arrival and expiration with minimum cost. To address the problem of limited memory, we also present an approximate algorithm, which selectively drops data within the current window and at the same time maintains a maximum error bound. Extensive experiments are conducted and the results show that our algorithms are efficient and effective.

Original language	English
Title of host publication	Database Systems for Advanced Applications : 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part II
Editors	Hiroyuki Kitagawa, Yoshiharu Ishikawa, Qing Li, Chiemi Watanabe
Number of pages	15
Publisher	Springer
Publication date	2010
Pages	216-230
ISBN (Print)	978-3-642-12097-8
ISBN (Electronic)	978-3-642-12098-5
DOIs	https://doi.org/10.1007/978-3-642-12098-5_17
Publication status	Published - 2010
Externally published	Yes
Event	15th International Conference on Database Systems for Advanced Applications - Tsukuba, Japan Duration: 1 Apr 2010 → 4 Apr 2010 Conference number: 15

Conference

Conference	15th International Conference on Database Systems for Advanced Applications
Number	15
Country/Territory	Japan
City	Tsukuba
Period	01/04/2010 → 04/04/2010

Series	Lecture notes in computer science
Volume	5982
ISSN	0302-9743

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10.1007/978-3-642-12098-5_17

Cite this

Cao, H., Zhou, Y., Shou, L., & Chen, G. (2010). Attribute outlier detection over data streams. In H. Kitagawa, Y. Ishikawa, Q. Li, & C. Watanabe (Eds.), Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part II (pp. 216-230). Springer. Lecture notes in computer science Vol. 5982 https://doi.org/10.1007/978-3-642-12098-5_17

Attribute outlier detection over data streams. / Cao, Hui; Zhou, Yongluan; Shou, Lidan et al.

Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part II. ed. / Hiroyuki Kitagawa; Yoshiharu Ishikawa; Qing Li; Chiemi Watanabe. Springer, 2010. p. 216-230 (Lecture notes in computer science, Vol. 5982).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Cao, H, Zhou, Y, Shou, L & Chen, G 2010, Attribute outlier detection over data streams. in H Kitagawa, Y Ishikawa, Q Li & C Watanabe (eds), Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part II. Springer, Lecture notes in computer science, vol. 5982, pp. 216-230, 15th International Conference on Database Systems for Advanced Applications, Tsukuba, Japan, 01/04/2010. https://doi.org/10.1007/978-3-642-12098-5_17

Cao H, Zhou Y, Shou L, Chen G. Attribute outlier detection over data streams. In Kitagawa H, Ishikawa Y, Li Q, Watanabe C, editors, Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part II. Springer. 2010. p. 216-230. (Lecture notes in computer science, Vol. 5982). doi: 10.1007/978-3-642-12098-5_17

Cao, Hui ; Zhou, Yongluan ; Shou, Lidan et al. / Attribute outlier detection over data streams. Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part II. editor / Hiroyuki Kitagawa ; Yoshiharu Ishikawa ; Qing Li ; Chiemi Watanabe. Springer, 2010. pp. 216-230 (Lecture notes in computer science, Vol. 5982).

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N2 - Outlier detection is widely used in many data stream application, such as network intrusion detection, fraud detection, etc. However, most existing algorithms focused on detecting class outliers and there is little work on detecting attribute outliers, which considers the correlation or relevance among the data items. In this paper we study the problem of detecting attribute outliers within the sliding windows over data streams. An efficient algorithm is proposed to perform exact outlier detection. The algorithm relies on an efficient data structure, which stores only the necessary information and can perform updates incurred by data arrival and expiration with minimum cost. To address the problem of limited memory, we also present an approximate algorithm, which selectively drops data within the current window and at the same time maintains a maximum error bound. Extensive experiments are conducted and the results show that our algorithms are efficient and effective.

AB - Outlier detection is widely used in many data stream application, such as network intrusion detection, fraud detection, etc. However, most existing algorithms focused on detecting class outliers and there is little work on detecting attribute outliers, which considers the correlation or relevance among the data items. In this paper we study the problem of detecting attribute outliers within the sliding windows over data streams. An efficient algorithm is proposed to perform exact outlier detection. The algorithm relies on an efficient data structure, which stores only the necessary information and can perform updates incurred by data arrival and expiration with minimum cost. To address the problem of limited memory, we also present an approximate algorithm, which selectively drops data within the current window and at the same time maintains a maximum error bound. Extensive experiments are conducted and the results show that our algorithms are efficient and effective.

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EP - 230

BT - Database Systems for Advanced Applications

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Attribute outlier detection over data streams

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