Multiresolution Motif Discovery in Time Series

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II – Motivation • Finding motifs is an important task: • Describe the time series at hand • Help summarize/represent the database • Provide useful insight to the domain expert • Examples of motifs: • Patterns that typically precede a seizure in EEG • DNA subsequence preserved through evolution • Bursts in telecommunication traffic Nuno Castro and Paulo Azevedo 04/30/2010
III – Related work limitations • Computational complexity • Quadratic algorithms are clearly not the solution • Disk innefficient (use expensive random disk accesses) • Memory innefficient (assume data can fit into main memory) • Assume all data are available Nuno Castro and Paulo Azevedo 04/30/2010
Page 1 and 2: Tenth SIAM International Conference
Page 3: I - Motif Definition • Motifs, al
Page 7 and 8: IV - Our algorithm • We propose a
Page 9 and 10: IV - Our algorithm - Problem defini
Page 11 and 12: IV - Our algorithm Problem definiti
Page 13 and 14: IV - Our algorithm Properties • M
Page 15 and 16: IV - Our algorithm - Properties Mul
Page 17 and 18: IV - Our algorithm - Properties Spa
Page 19 and 20: V - Experimental Analysis • Scala
Page 21 and 22: V - Experimental Analysis - Scalabi
Page 23 and 24: V - Experimental Analysis - Scalabi
Page 25 and 26: V - Experimental Analysis Experimen
Page 27 and 28: VI - Conclusions • We have introd
Page 29 and 30: Thank you for your attention! • C
Page 31: On similarity Nuno Castro and Paulo

Multiresolution Motif Discovery in Time Series

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