Definition
Anomaly detection generally refers to the process of automatically detecting events or behaviors which deviate from those considered normal. It is an unsupervised process, and can thus detect anomalies which have not been previously encountered. It is based on estimating a model of typical behavior from past observations and consequently comparing current observations against this model. It can be performed either on a single stream or among multiple streams. Anomaly detection encompasses outlier detection as well as change detection and therefore is closely related to forecasting and clustering methods.
Historical Background
Anomaly detection in streams has close connections to traditional outlier detection, as well as to change detection. The former is a common and widely studied topic in statistics [11]. The latter emerged in the context of statistical monitoring and control for continuous processes and the widely used CUSUM algorithm was proposed as early as 1954 [9]....
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Recommended Reading
Aggarwal CC, Han J, Wang J, and Yu PS. A Framework for clustering evolving data streams. In: Proceeding of the 29th International Conference on Very Large Data Bases, 2003, p. 81–92.
Aggarwal CC, Yu PS. A survey of synopsis construction in data streams. In: Data streams: models and algorithms. New York: Springer; 2007.
Cormode G, Muthukrishnan S. What’s new: finding significant differences in network data streams. IEEE/ACM Trans Netw. 2005;13(6):1219–32.
Guha S, Meyerson A, Mishra N, Motwani R, O’Callaghan L. Clustering data streams: theory and practice. IEEE Trans Knowl Data Eng. 2003;15(3):515–28.
Hulten G, Spencer L, and Domingos P. Mining time-changing data streams. In: Proceeding of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, p. 97–106.
Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv. 1999;31(3):264–323.
Kleinberg J. Bursty and hierarchical structure in streams. In: Proceeding of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, p. 91–101.
Lee W, Stolfo SJ, Mok KW. Adaptive intrusion detection: a data mining approach. Artif Intell Rev. 2000;14(6):533–67.
Page ES. Continuous inspection schemes. Biometrika. 1954;41(1):100–15.
Papadimitriou S, Sun J, and Faloutsos C. Streaming pattern discovery in multiple time-series. In: Proceeding of the 31st International Conference on Very Large Data Bases, 2005, p. 697–708.
Peter JR, Annick ML. Robust regression and outlier detection. New York: Wiley; 1987.
Wang H, Fan W, Yu PS, and Han J. Mining concept-drifting data streams using ensemble classifiers. In: Proceeding of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, p. 226–35.
Zhu Y and Shasha D. StatStream: statistical monitoring of thousands of data streams in real time. In: Proceeding of the 28th International Conference on Very Large Data Bases, 2002, p. 358–69.
Zhu Y and Shasha D. Efficient elastic burst detection in data streams. In: Proceeding of 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, p. 336–45.
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Papadimitriou, S. (2016). Anomaly Detection on Streams. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_18-2
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DOI: https://doi.org/10.1007/978-1-4899-7993-3_18-2
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