Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems

1
Intelligent Icons Integrating Lite-Weight Data
Mining and Visualization into GUI Operating
Systems
Eamonn Keogh Li Wei Xiaopeng Xi Stefano
Lonardi Jin Shieh Scott Sirowy Computer
Science Engineering Dept.University of
California Riverside
2
Outline

• Overview
• An Example DNA to Intelligent Icon
• Icon Generation Algorithm
• Experimental Evaluation
• Conclusion

Eamonn, patent this idea!
Christos Faloutsos
3
Dataset Kalpakis_ECG
Icons in a traditional browser
4
Dataset Kalpakis_ECG

• Suppose I magically..
• Color the icons to somehow reflect the contents
  of the file.
• Position the icons based on their colors/patterns

5
TGGCCGTGCTAGGCCCCACCCCTACCTTGCAGTCCCCGCAAGCTCATCTG
CGCGAACCAGAACGCCCACCACCCTTGGGTTGAAATTAAGGAGGCGGTTG
GCAGCTTCCCAGGCGCACGTACCTGCGAATAAATAACTGTCCGCACAAGG
AGCCCGACGATAGTCGACCCTCTCTAGTCACGACCTACACACAGAACCTG
TGCTAGACGCCATGAGATAAGCTAACACAAAAACATTTCCCACTACTGCT
GCCCGCGGGCTACCGGCCACCCCTGGCTCAGCCTGGCGAAGCCGCCCTTC
A
Let us start with visualizing a special data
type, DNA. The DNA of two species Are they
similar?
CCGTGCTAGGGCCACCTACCTTGGTCCGCCGCAAGCTCATCTGCGCGAAC
CAGAACGCCACCACCTTGGGTTGAAATTAAGGAGGCGGTTGGCAGCTTCC
AGGCGCACGTACCTGCGAATAAATAACTGTCCGCACAAGGAGCCGACGAT
AAAGAAGAGAGTCGACCTCTCTAGTCACGACCTACACACAGAACCTGTGC
TAGACGCCATGAGATAAGCTAACA
6
C
T
C
T
C
T
C
T
C
T
A
G
A
G
A
G
A
G
A
G
0.20
0.24
CCGTGCTAGGGCCACCTACCTTGGTCCGCCGCAAGCTCATCTGCGCGAAC
CAGAACGCCACCACCTTGGGTTGAAATTAAGGAGGCGGTTGGCAGCTTCC
AGGCGCACGTACCTGCGAATAAATAACTGTCCGCACAAGGAGCCGACGAT
AAAGAAGAGAGTCGACCTCTCTAGTCACGACCTACACACAGAACCTGTGC
TAGACGCCATGAGATAAGCTAACA
0.26
0.30
7
CC
CCC
CCT
CTC
CCC
CCT
CTC
CCC
CCT
CTC
CCC
CCT
CTC
CC
CT
TC
TT
CC
CT
TC
TT
CC
CT
TC
TT
CC
CT
TC
TT
CC
CT
TC
TT
CC
CT
TC
TT
CC
CT
TC
TT
CT
TC
TT
C
T
C
T
C
T
C
T
C
T
C
T
CCA
CCG
CTA
CCA
CCG
CTA
CCA
CCG
CTA
CCA
CCG
CTA
CAC
CAT
CAC
CAT
CAC
CAT
CAC
CAT
CA
CG
TA
TC
CA
CG
TA
TG
CA
CG
TA
TC
CA
CG
TA
TC
CA
CG
TA
TC
CA
CG
TA
TC
CA
CG
TA
TC
CA
CG
TA
TG
CAA
CAA
CAA
CAA
AC
AT
GC
GT
AC
AT
GC
GT
AC
AT
GC
GT
AC
AT
GC
GT
AC
AT
GC
GT
AC
AT
GC
GT
AC
AT
GC
GT
AC
AT
GC
GT
A
G
A
G
A
G
A
G
A
G
A
G
AA
AG
GA
GG
AA
AG
GA
GG
AA
AG
GA
GG
AA
AG
GA
GG
AA
AG
GA
GG
AA
AG
GA
GG
AA
AG
GA
GG
AA
AG
GA
GG
CCGTGCTAGGGCCACCTACCTTGGTCCGCCGCAAGCTCATCTGCGCGAAC
CAGAACGCCACCACCTTGGGTTGAAATTAAGGAGGCGGTTGGCAGCTTCC
AGGCGCACGTACCTGCGAATAAATAACTGTCCGCACAAGGAGCCGACGAT
AAAGAAGAGAGTCGACCTCTCTAGTCACGACCTACACACAGAACCTGTGC
TAGACGCCATGAGATAAGCTAACA
8
0.04
0.02
0.04
0.09
1
0.02
0.03
0.07
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
0.03
0.11
AC
AT
AC
AT
AC
AT
AC
AT
AC
AT
AC
AT
AC
AT
AC
AT
AC
AT
AC
AT
AA
AG
AA
AG
AA
AG
AA
AG
AA
AG
AA
AG
AA
AG
AA
AG
AA
AG
AA
AG
CCGTGCTAGGCCCCACCCCTACCTTGCAGTCCCCGCAAGCTCATCTGCGC
GAACCAGAACGCCCACCACCCTTGGGTTGAAATTAAGGAGGCGGTTGGCA
GCTTCCCAGGCGCACGTACCTGCGAATAAATAACTGTCCGCACAAGGAGC
CCGACGATAGTCGACCCTCTCTAGTCACGACCTACACACAGAACCTGTGC
TAGACGCCATGAGATAAGCTAACA
0
9
OK. Given any DNA string I can make a colored
bitmap, so what?
CCGTGCTAGGCCCCACCCCTACCTTGCAGTCCCCGCAAGCTCATCTGCGC
GAACCAGAACGCCCACCACCCTTGGGTTGAAATTAAGGAGGCGGTTGGCA
GCTTCCCAGGCGCACGTACCTGCGAATAAATAACTGTCCGCACAAGGAGC
CCGACGATAGTCGACCCTCTCTAGTCACGACCTACACACAGAACCTGTGC
TAGACGCCATGAGATAAGCTAACA
10
Indian
Indian
rhinoceros.dna
rhinoceros.dna
white
white
rhinoceros.dna
rhesus
rhesus
monkey.dna
monkey.dna
pygmy
pygmy
chimpanzee.dna
chimpanzee.dna
sperm
sperm
whale.dna
whale.dna
Indian
chimpanzee.dna
hippopotamus.dna
Indian
chimpanzee.dna
hippopotamus.dna
elephant.dna
elephant.dna
Human.dna
Human.dna
African
orangutan.dna
African
orangutan.dna
elephant.dna
elephant.dna
pygmy
pygmy
sperm whale.dna
sperm whale.dna
11
Note Elephas maximus is the Indian Elephant,
Loxodonta africana is the African elephant and
Pan troglodytes is the chimpanzee.
12
Can we make Intelligent Icons for time series?
Yes, with SAX!
accbabcdbcabdbcadbacbdbdcadbaacb
c

c
c








b
b
b
a
a
Time Series Bitmap
13
While they are all example of EEGs, example_a.dat
is from a normal trace, whereas the others
contain examples of spike-wave discharges.
14
We can further enhance the time series bitmaps by
arranging the thumbnails by cluster, instead of
arranging by date, size, name etc We can achieve
this with MDS.
We can further enhance the time series bitmaps by
arranging the thumbnails by cluster, instead of
arranging by date, size, name etc We can achieve
this with MDS.
August.txt
July.txt
June.txt
April.txt
May.txt
Sept.txt
Oct.txt
Feb.txt
Dec.txt
March.txt
Nov.txt
Jan.txt
15
Text Example
Here are some papers that reference Eamonn Keoghs
work
16
Text Example
17
Intelligent Icon Search
18
Paper Summary

• We show how to map DNA, time series and natural
  language into intelligent icons.
• We give a generic framework for mapping any kind
  of data into intelligent icons.
• We show the utility of intelligent icons for
  finding patterns (clusters, outliers etc)

19
Questions?