visually mining and monitoring massive time series
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
1Intelligent Database Systems Lab
國立雲林科技大學National Yunlin University of Science and Technology
Visually Mining and Monitoring Massive Time Series
Author: Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, and Donna M. NystromReporter: Wen-Cheng Tsai
2007/05/09
SIGKDD,2004
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Outline
Motivation Objective Method
─ V-Tree
Experience Conclusion Personal Comments
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.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.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Objective
We propose VizTree, a time series pattern discovery and visualization system based on augmenting suffix trees.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Method---Viz-TreeStep 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.
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Intelligent Database Systems Lab
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Method---Viz-TreeSubsequence 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.
Motif DiscoveryMotif Discovery
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Intelligent Database Systems Lab
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Method---Viz-TreeAnomaly Detection
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Intelligent Database Systems Lab
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Viz-Tree
Anomaly Detection by Diff-Tree
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Intelligent Database Systems Lab
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I. M.How do we obtain How do we obtain SAX?
0 20 40 60 80 100 120
C
C
0
-
-
0 20 40 60 80 100 120
bbb
a
cc
c
a
baabccbc
First convert the time series to PAA representation, then convert the PAA to symbols
It take linear time
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Intelligent Database Systems Lab
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I. M.SAX characterization
Lower bounding of Euclidean distance
n
iii sq
1
2 M
i iiii svqvsrsr1
21 ))((
DLB(Q’,S’)
DLB(Q’,S’)
S
Q
D(Q,S)
D(Q,S)
Q’
S’
Dimensionality Reduction
baabccbc
SAX((SSymbolic ymbolic AAggregate Approximation)ggregate Approximation)
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Experience
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Intelligent Database Systems Lab
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I. M.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
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Intelligent Database Systems Lab
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I. M.Personal Comments
Advantages ─ Dimensionality Reduction─ Lower bounding distance measures
Disadvantage─ …
Application─ Time series