[PDF] Visualizing and discovering non trivial patterns in large time-series databases

1. Visualizing and Discovering Non-Trivial
Patterns in Large Time Series Databases
Quan Le
HCI Lab
23th Mar, 2015
Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, Daonna M. Nystrom
Computer Science & Engineering Department University of California, Riverside, CA 92521
Proceedings of the 30th VLDB Conference, Toronto, Canada, 2004

2. Contents
 Introduction
 Background
 Time series data mining tasks
 Visualizing Time Series
 VizTree
 Evaluation
 Conclusion
2
Fig 1. Time Series Visualization

3. Introduction
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion- Data visualization techniques are very important for data
analysis.
- Visualizing massive time series datasets.
- VizTree – a time series pattern discovery and visualization
system based on augment suffix trees.
 Occurring pattern (Motif Discovery)
 Surprising Pattern (Anomaly Discovery)
 Query by content
 Measures the dissimilarity between any two time series
3

4. Introduction
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion- DoD – The U.S Department of Defense and The Aerospace
Corp (TAC)
- There are two major directions of research area:
 Producing better technique to mine the archival launch
data from previous missions (mining stage).
 Producing better techniques to visualize the streaming
telemetry data in the hours before launch (monitor
stage)
- Dr. Ben Shneiderman of Uni. of Maryland – “Overview, zoom
& filter, details-on-demand”.
4

5. Background
 Time Series data mining tasks
 Visualization Time Series
5
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion

6. Time Series data mining tasks
 Subsequence matching
 Motifs Discovery
 Anomaly detection
6
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion

7. Subsequence Matching
- Sequence matching has long been divided into two categories:
whole matching and subsequence matching.
- Subsequence matching: a short query subsequence time series
is matched against longer time series by sliding it along the
longer sequence, looking for the best matching location.
- Chunking – the process where a time series is broken into
individual time series by either specific period.
7
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Fig 2. A weekly map of drought conditions in Texas

8. Time Series Motif Discovery
- A substantial body of literature has been devoted to
techniques to discover frequently recurring, overrepresented
patterns in time series.
8
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Fig 3. Time series Subsequence Motifs Discovery

9. Anomaly Detection
9
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion- The problem of detecting anomalous/surprising patterns has
attracted much attention.
- Keogh’s definition – “whose frequently of occurrences differs
substantially from that expected or given previously seen
data”.
Fig 4. Illustration of Anomalous Series Detection ( Red represents anomalous time series)

10. Visualizing Time Series
10
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion TimeSearcher
 Cluster and Calendar-based visualization
 Spirals

11. TimeSearcher
11
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion- A time series exploratory and visualization tool that allows
user to retrieve time series by creating queries.
Fig 5. The TimeSearcher visual query interface.
User can filter away sequence that are not
interesting by insisting that all sequences have
at least on data point within the query box.
http://www.cs.umd.edu/hcil/timesearcher/vide
os/TimeSearcherDemo.mp4
 Flexibility
 Specify different regions
to compare.

12. Cluster and Calendar-based visualization
12
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion- The time series data are chunked into sequences of day
patterns.
- This visualization system displays patterns represented
calendar with each day color-coded by the cluster that it
belongs to.
Fig 6. The cluster and calendar-based
visualization on employee working
hours data. It shows 6 clusters,
representing different working day
patterns.
 Good overview
 Limited to calendar-
based data

13. Spirals
13
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion- Weber developed this tool to visualize time series on spirals.
- Identify periodic structures in the data.
- Do not exhibit periodic behaviors of time series data.
- Requires pixel space in length of time series.
Fig 7. The Spiral visualization approach of Weber applied to the power usage
dataset

14. VizTree - Motivation
14
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Here are two sets of bit strings.
Which set is generated by human and
which one is generated by a computer?
0101100101111001101001000010001010
0110110101110000101010111011111000
1101101101111110100110010010001101
0001111001101101000101111000101101
0011011001101000000100110001001110
000011101001100101100001010010
1000100010100100010101010000101010
0010101110111101011010010111010010
1010011101010101001010010101011101
0101001010101011010101001011001011
1011110100011100001010000100111010
100011100001010101100101110101

15. VizTree - Motivation
15
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Fig 8. (Left) Computer-generated random bits presented as an augmented suffix tree.
(Right) Human-constructed bits presented as an augmented suffix tree.
0
1
(0,1,0)
(1,0,1)
(0,1,1)
1000100010100100010101010000101010
0010101110111101011010010111010010
1010011101010101001010010101011101
0101001010101011010101001011001011
1011110100011100001010000100111010
100011100001010101100101110101
0101100101111001101001000010001010
0110110101110000101010111011111000
1101101101111110100110010010001101
0001111001101101000101111000101101
0011011001101000000100110001001110
000011101001100101100001010010

16. VizTree - Motivation
16
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion- The strings represented in the tree are in fact “subsequences”
rather than “suffixes”.
- Using a time-series discretization method.
- Given the same parameters, the tree has the same overall
shape for any dataset.
Fig 9. VizTree Tool

17. Discretizing time series method
17
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
SAX
Symbolic Aggregate ApproXimation
baabccbc

18. 18
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Discretizing time series method
Convert
time series
to PAA
Convert PAA
to symbols
PAA = Piecewise Aggregate Approximation
Fig 10. A summarization of the notation used

19. 19
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Discretizing time series method
Fig 11. A time series dataset of electrical consumption (of length 1024) is
converted into an eight-symbol string “acdcbdba”. Note that the general
shape is the time series is preserved, in spite of the massive amount of
dimensionality reduction.

20. 20
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
VizTree – First Look
Fig 12. A screenshot of VizTree
The parameter
setting area
The actual
subsequence
when the
technician clicks
on a branch
The input time series
The subsequence
tree for the time
series
Zoom-in window

21. VizTree
21
 Motifs Discovery
 Anomaly Detection
 Diff-Tree (Surprising Patterns)
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion

22. 22
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Motif Discovery
Fig 13. Example of Motif discovery on the winding dataset. Two nearly identical
subsequences are identified, among the other motifs.

23. 23
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Anomaly Detection
Fig 14. Heart-beat data with anomaly is shown. While the subsequence tree can be
used to identify motifs, it can be used for simple anomaly detection as well.

24. 24
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Diff-Tree (Surprising Patterns)
Fig 15. The blue ECG data is the reference data and the green ECG data is the testing
data. The resulting tree show the difference in pattern distributions of two datasets. The
surprising patterns are ranked with the red one.

25. 25
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Evaluation
 Subsequence Matching & Motifs Discovery: Human
motion data of Yoga Postures
 Anomaly Detection: Power consumption data

26. 26
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Yoga Postures – Subsequence Matching
- A model postured yoga routines in front of a green screen.
- The motion capture is transformed into a time series.
- The length of the time series is approximately 26.000.
Fig 16. The sample yoga sequence that we are interested in finding

27. 27
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Yoga Postures – Subsequence Matching
- A model postured yoga routines in front of a green screen.
- The motion capture is transformed into a time series.
- The length of the time series is approximately 26.000.
Fig 17. Matches for the yoga sequence. The
bottom right corner shows how similar
these two subsequences are

28. 28
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Yoga Postures – Motif Discovery
- Identify approximately motifs by examining the subsequences
represented by thick three paths.
Fig 18. Example of Motifs discovery
on the winding dataset. Two nearly
identical subsequences are identified,
among the other motifs.

29. 29
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Power Consumption – Anomaly Detection
- Electricity consumption is recorded every 15 minutes, in 1997
Fig 19. Anomaly detection on power
consumption data. The anomaly
shown here is a short week during
Christmas.

30. 30
Introduction
Background
Time Series data mining tasks
Visualizing Time Series
VizTree
Evaluation
Conclusion
Conclusion
- Proposed VizTree as a visualization framework for massive
time series datasets.
- Mining and monitoring purposes.
- Process new data arrive.

31. Thank you!