1. Jessica Lin Eamonn Keogh Stefano Lonardi
Visualizing and discovering non-trivial patterns in large time series databases
Jessica Lin
Eamonn Keogh
Stefano Lonardi
Presented by Thomas Lotze

2. Purpose: Motifs and Anomalies
Timeseries visualization
Motifs (frequently occurring patterns)
Anomalies

3. Semantic Representation: SAX
Equprobable
a c d c b d b a

4. Sequence Trees Set 1: Set 2: 000 100 010 101 001 101 010 101

5. Subsequence Trees

6. Subsequence Trees on Timeseries
Sliding window
Discretized using SAX
Store the subsequence pattern
Display as tree
Thickness: frequency of pattern
Color: grey if none exist

7. Numerosity Reduction Avoiding Trivial matches (neighbors)
Same as previous
MINDIST
No overlap
None
Non-monotonic
Can make a big difference!!!

8. VizTree Demo Remember to note:
- pixels required only depends on number of characters, depth…

9. Comparing Series Support Confidence Surprisingness
Difference in frequency of pattern
Confidence
Average of how significant this pattern is
Surprisingness
Support x Confidence
Mention Bayesian method if the support is 0 (i.e., if the pattern does not occur in the time series)
Maybe just talk about this during the demo (or do contrast portion of demo after this slide)

10. Likes Tree structure visualization Mapping using color and thickness
Automatic Pattern Identification
Speed of computation
Generality of patterns
Simultaneous view of subsequences matching a pattern/motif
Can zoom to different tree levels/sections
Remember to mention the generality is especially nice because it

11. Wishlist Dynamic parameter response
Sliders for window size/character parameters
Automatic suggestions for parameters
Select individual subsequences in simultaneous time-zoom panel
HCIL-style selectors for tree focus
Maybe a human-assisting time series clusterer?
Allowing for different lengths of patterns
Allow user to “kick a subsequence out” from a cluster
Allow user to recluster the unclustered
More possible patterns
Functional Data Analysis?
Allow “no pattern overlap” or fuzzy overlap…fuzzy length?...somehow?
Okay, now I’m just dreaming…
…when lots of neighbors (relative to window size) have the same pattern, perhaps that indicates that the window should be longer?

12. Calendar Clusters van Wijk, van Selow
Office hours are followed strictly. Most people arrive
between 8:30 and 9:00 am, and leave between 4:00 and
5:00 pm. Furthermore, in the morning the number of
employees present is slightly higher than in the afternoon.
On Fridays and in the summer fewer people are present
(cluster 722);
On Fridays in the summer even fewer people are
present (cluster 718);
In the weekend and at holidays only very few people
are working (cluster 710): security and fire brigade;
Holidays in the Netherlands in 1997 were January 1st,
March 28th, March 31st, April 30th, May 5th,May 8th,
May 19th, December 25th and 26th.
School vacations are visible in Spring (May 3rd toMay
11th), in Autumn (October 11th to October 19th), and
in Winter (December 21th to December 31st);
Many people take a day off after a holiday (cluster
721);
On December 5th many people left at 4:00 PM. Dutch
people will immediately knowthe explanation: On this
day we celebrate Santa Claus and are allowed to leave
earlier!

13. Spirals
Marc Weber Marc Alexa Wolfgang Müller

14. Periodicity/Pattern Length
Now: known in advance
Find good candidates?
spectral analysis?
non-constant period?
Visual insight
scroll/slide through different window lengths
see when patterns start to coalesce
Note that even VizTree requires knowing the length of the subsequence motif…but it does allow for varying space between the subsequence occurrences