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Visually Mining and Monitoring Massive Time Series

Visually Mining and Monitoring Massive Time Series. Author: Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, and Donna M. Nystrom Reporter: Wen-Cheng Tsai 2007/05/09. SIGKDD,2004. Outline. Motivation Objective Method V-Tree Experience Conclusion Personal Comments.

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Visually Mining and Monitoring Massive Time Series

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  1. Visually Mining and Monitoring Massive Time Series Author: Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, and Donna M. Nystrom Reporter: Wen-Cheng Tsai 2007/05/09 SIGKDD,2004

  2. Outline • Motivation • Objective • Method • V-Tree • Experience • Conclusion • Personal Comments

  3. Motivation • Moments before the launch of every space vehicle, engineering discipline specialists must make a critical go/no-go decision. • To reduce the possibility of wrong go/no-go decisions • To mine the archival launch data from previous missions. • To visualize the streaming telemetry data in the hours before launch. • Electronic strip charts do not provide any useful higher-lever information that might be valuable to the analyst.

  4. Objective • We propose VizTree, a time series pattern discovery and visualization system based on augmenting suffix trees.

  5. Method---Viz-Tree Step 1: Discretization (via SAX) The following time series is converted to string "acdbbdca" Step 2: Insertion The following tree is of depth 3, with alphabet size of 4. The frequencies of the strings are encoded as the thickness of branches.

  6. Motif Discovery Method---Viz-Tree Subsequence Matching and Motif Discovery via VizTree This example demonstrates subsequence matching and motif discovery. We want to find a U-shaped pattern, so we'd try something that starts high, descends, and then ascends again. Clicking on "abdb" shows such patterns.

  7. Method---Viz-Tree Anomaly Detection

  8. Viz-Tree Anomaly Detection by Diff-Tree

  9. c c c b b b a a - - 0 0 40 60 80 100 120 20 How do we obtain SAX? C C 0 20 40 60 80 100 120 First convert the time series to PAA representation, then convert the PAA to symbols It take linear time baabccbc

  10. Q’ Q S’ S D(Q,S) DLB(Q’,S’) D(Q,S) DLB(Q’,S’) SAXcharacterization • Lower bounding of Euclidean distance • Dimensionality Reduction SAX (Symbolic Aggregate Approximation) baabccbc

  11. Experience

  12. Conclusion • We proposed VizTree, novel visualization framework for time series that summarizes the global and local structures of the data. • We demonstrated how pattern discovery can be achieved very efficiently with Viz Tree • Lower bounding of Euclidean distance • Dimensionality Reduction

  13. Personal Comments • Advantages • Dimensionality Reduction • Lower bounding distance measures • Disadvantage • … • Application • Time series

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