Abstract
Satellite health monitoring is a specialized task usually carried out by human experts. In this paper, we address the task of monitoring by defining it as an anomaly and event detection task cast in Case Based Reasoning framework. We discuss how each CBR step is achieved in a time series domain such as the Satellite health monitoring. In the process, we define the case structure in a time series domain, discuss measures of distance between cases and address other issues such as building initial Case Base and determining similarity threshold. We briefly describe the system that we have built, and end the paper with a discussion on possible extensions to current work.
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References
Aguilar, J., Bousson, K., Dousson, C., Ghallab, M., Guasch, A., Milne, R., Nicol, C., Quevedo, J., Trave-Massuyes, L.: Tiger: real-time situation assessment of dynamic systems. Intelligent Systems Engineering, 103–124 (1994)
Hunter, J., McIntosh, N.: Knowledge-based event detection in complex time series data. In: AIMDM:Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, Springer, Heidelberg (1999)
Yairi, T., Kato, Y., Hori, K.: Fault detection by mining association rules from house-keeping data. In: Proceedings of the 6th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Montreal, Canada (2001)
Das, G., Lin, K.I., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. In: Proceedings of the 4th Int’l Conference on Knowledge Discovery and Data Mining, New York, pp. 16–22 (1998)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD Conference (1993)
Lin, J., Keogh, E., Truppel, W.: Clustering of streaming time series is meaningless. In: Proceedings of the 8th ACM SIGMOD workshop on Research Issues in data mining and knowledeg discovery, San Diego, California (2003)
Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)
Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–59
David, B.L.: Cbr in context: The present and future. In: Leake, D. (ed.) Case-Based Reasoning: Experiences, Lessons and Future Directions (1996)
Daw, C., Finney, C., Tracy, E.: A review of symbolic analysis of experimental data (2001)
Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms, Chicago, IL, pp. 69–84 (1993)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time series databases. In: Proceedings of ACM SIGMOD Conference, Minneapolis (1994)
Keogh, E., Pazzani, M.: An enhaced representation of time series that allows fast and accurate classification, clustering and relevance feedback. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, pp. 239–241 (1998)
Berndt, D., Clifford, J.: Finding patterns in time series: A dynamic programming approach (1996)
Rafiei, D.: On similarity-based queries for time-series data. In: Proceedings of the 15th IEEE Intl. Conf. on Data Engineering, Sydney, Australia, pp. 410–417 (1999)
Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: A survey and empirical demonstration. In: ACM SIGKDD, Edmonton, Alberta, Canada (2002)
Kadous, M.W.: Learning comprehensible descriptions of multivariate time series. In: Proceedings of the ICML, Morgan Kaufmann, San Francisco (1999)
Keogh, E.: Exact indexing of dynamic time warping. In: Proceedings of the 28th VLDB Conference, HKN, China (2002)
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. On Knowledge and Data Engineering 8, 970–974 (1996)
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Penta, K.K., Khemani, D. (2004). Satellite Health Monitoring Using CBR Framework. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_53
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DOI: https://doi.org/10.1007/978-3-540-28631-8_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22882-0
Online ISBN: 978-3-540-28631-8
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