visually mining and monitoring massive time series

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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I. M.

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

N.Y.U.S.T.

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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Intelligent Database Systems Lab

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I. M.

Experience

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Intelligent Database Systems Lab

N.Y.U.S.T.

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