Abstract
This paper presents a method to summarize massive spacecraft telemetry data by extracting significant event and change patterns in the low-level time-series data. This method first transforms the numerical time-series into a symbol sequence by a clustering technique using DTW distance measure, then detects event patterns and change points in the sequence. We demonstrate that our method can successfully summarize the large telemetry data of an actual artificial satellite, and help human operators to understand the overall system behavior.
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Yairi, T., Ogasawara, S., Hori, K., Nakasuka, S., Ishihama, N. (2004). Summarization of Spacecraft Telemetry Data by Extracting Significant Temporal Patterns. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_31
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DOI: https://doi.org/10.1007/978-3-540-24775-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22064-0
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