1 / 13

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.

kenyon-green
Download Presentation

Visually Mining and Monitoring Massive Time Series

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related