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 Experience Conclusion
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
Crawler：the system uses crawler to download patents form the USPTO site based on a query.
Parser：The parser parses these patents to extract information like Inventors, assignees, Title, Abstract.
Annotators：the biological terms in the parsed files are first annotated by the BioAnnotator system.
我們使用UMLS建構的medical terms詞庫，他是一個Semantic Network which has 135 biomedical semantic classes.
Formal evaluation of relation annotator is difficult because there is no standard corpus.所以使用人工的方式調查88patents透過query：”protein protein interactions”從USPTO.
有124相關的protein被定義在abstracts of patents.relation annotator using this corpus are shown in above it.
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. Method---Viz-Tree Motif Discovery
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.
Motif Discovery

7. Method---Viz-Tree
Anomaly Detection

8. Viz-Tree
Anomaly Detection by Diff-Tree

9. baabccbc How do we obtain SAX? C c b a C20 40 60 80
100
120 - 20 40 60 80
100
120 b a c
First convert the time series to PAA representation, then convert the PAA to symbols
It take linear time
baabccbc

10. SAX characterization Lower bounding of Euclidean distance Q Q’ S’ S
DLB(Q’,S’) S Q
D(Q,S) Q’ S’
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 Disadvantage Application
Dimensionality Reduction
Lower bounding distance measures
Disadvantage …
Application
Time series