PAIRFINDER: IDENTIFYING AND MEASURING TEMPORAL
ASSOCIATIONS FROM TEMPORAL EVENT SEQUENCES
Hsueh-Chien Cheng, Catherine Plaisant, Ben Shneiderman
Department of Computer Science
Human-Computer Interaction Lab
University of Maryland
cheng@cs.umd.edu
5/22/2012
TEMPORAL EVENTS
Admission
19:28
Oct. 1
ICU
19:35
Oct. 1
Floor
03:19
Oct. 4
Discharge
23:06
Oct. 7
TEMPORAL ASSOCIATIONS

Consider both order and relative time difference
Admission ICU Floor
Discharge
“ICU” occurred 52 hours before “Floor”
“Discharge” occurred 92 hours after “Floor”
What if we have a large number of records?
ALIGNMENT FRAMEWORK

Align the records by one event and move the others
accordingly in the relative time frame
ALIGN
Focal event, Floor Related event, ICU
Records
Aligned
records
1 Align
2 Aggregate
3 Summarize
AGGREGATE
Focal event, Floor Related event, ICU
Aligned
records
Aggregation
1 Align
2 Aggregate
3 Summarize
SUMMARIZE
Focal event, Floor Related event, ICU
1 Align
Aligned
records
Aggregation
2 Aggregate
Histogram
3 Summarize
-3
-2
-1
1
1 day
2
3
ORGANIZING HISTOGRAMS

Even with a small number of event types, there are
many event pairs.
10 event types
10 * 9 = 90 event pairs
We need a better way to organize the histograms
PAIRFINDER
1.
2.
Shows the histograms summarizing the associations
between all pairs of events
Applies interestingness measures to locate interesting
histograms easily
GRADUATE STUDENT DATASET

A synthetic dataset with 1000 records and 7 event types
Event Type
Class Signup
Paper Submission
Software Release
Masters Degree
Proposal
Defense
Job Interview
HISTOGRAMS

Histogram helps understand the associations between a
pair of events
o Students had defenses 13 months after their proposals
o No defense occurred before proposal
INTERESTINGNESS MEASURES

Interestingness measure helps arrange a large number
of histograms in a meaningful order
More interesting
Less interesting
INTERESTINGNESS MEASURES
o Which related event occurred mostly after the focal event?
Interesting
Not
o Which related event occurred periodically after alignment?
Interesting
Not
CASE STUDY

5 case studies were done to demonstrate the potential
of PairFinder.
CONCLUSION

PairFinder
uses histograms to summarize the association between all
pairs of events.
 applies Interestingness measures to order the histograms by
their interestingness.


Work with us -- cheng@cs.umd.edu
TAKE AWAY MESSAGES

Visualizations help summarize complicated relations and
enable further interpretation of the data.

Organizing a large number of visualizations facilitates
knowledge discovery.
We thank the National Institutes of Health (Grant RC1CA147489-02)
for partial support of this research.
Project page at www.cs.umd.edu/hcil/pairfinder