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Article title SEARCH FOR ANOMALIES IN TIME SERIES TECHNOLOGICAL DATABASES
Authors A.N. Shabelnikov, V.A. Shabelnikov
Section SECTION V. MODELING OF COMPLEX SYSTEMS
Month, Year 04, 2008 @en
Index UDC 519.007
DOI
Abstract The version of common methodology for knowledge discovery in time series databases, illustrated with the samples of anomaly detection is considered in this paper. The knowledge discovery process involves data refining and filtering as well as features generation and association rules set extraction, that can be used for anomaly detection and further time series behavior forecasting. The method is based on the use of information-theoretic ap- proach of knowledge discovery comprising the ideas of time series linguistic approximation.

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Keywords knowledge discovery, databases, time series, association rules, linguistic approximation.
References 1. Батыршин И.З. Основные операции нечеткой логики и их обобщение. – Казань: Отечество, 2001. – 102 с.
2. A. Ultsch. Knowledge discovery, lecture notes, 2003a. German.
3. Bakshi B.R. and Stephanopoulos G. Representation of process trends - IV. Induction of realtime patterns from operating data for diagnosis and supervisory control. Computers & Chemical Engineering, 18(4):303-332, 1994.
4. Colomer J., Melendez J., and Gamero F. Pattern recognition based on episodes and DTW. Application to diagnosis of a level control system. In Proceedings 16th International Workshop on Qualitative Reasoning (QR'02), pages 37-43, 2002.
5. Keogh E., Chakrabarti K., Pazzani M. J., and Mehrotra S.. Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems, 3(3): 263-286, 2001b
6. Agrawal R. and Srikant R.. Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, pages 487 – 499, 1994.
7. Daw C.S., Finney C. E. A, and E. R. Tracy. A review of symbolic analysis of experimental data. Review of Scienti_c Instruments, 74(2):916-930, 2003.
8. Last M., Klein Y., and Kandel A. Knowledge discovery in time series databases. IEEE Transactions on Systems, Man, and Cybernetics, 31(1):160-169, 2001
9. Keogh E., Lonardi S., and Chiu B. Finding surprising patterns in a time series database in linear time and space. In D. Hand, D. Keim, and R. Ng, editors, Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'02),
pages 550-556. ACM Press, 2002.

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