Satellite Health Monitoring Using CBR Framework

  • Kiran Kumar Penta
  • Deepak Khemani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3155)

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

  1. 1.
    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)Google Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD Conference (1993)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  8. 8.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–59Google Scholar
  9. 9.
    David, B.L.: Cbr in context: The present and future. In: Leake, D. (ed.) Case-Based Reasoning: Experiences, Lessons and Future Directions (1996)Google Scholar
  10. 10.
    Daw, C., Finney, C., Tracy, E.: A review of symbolic analysis of experimental data (2001)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time series databases. In: Proceedings of ACM SIGMOD Conference, Minneapolis (1994)Google Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    Berndt, D., Clifford, J.: Finding patterns in time series: A dynamic programming approach (1996)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    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)Google Scholar
  17. 17.
    Kadous, M.W.: Learning comprehensible descriptions of multivariate time series. In: Proceedings of the ICML, Morgan Kaufmann, San Francisco (1999)Google Scholar
  18. 18.
    Keogh, E.: Exact indexing of dynamic time warping. In: Proceedings of the 28th VLDB Conference, HKN, China (2002)Google Scholar
  19. 19.
    Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. On Knowledge and Data Engineering 8, 970–974 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kiran Kumar Penta
    • 1
  • Deepak Khemani
    • 1
  1. 1.Dept of Computer Science & EngineeringIIT MadrasIndia

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