Information Object Model (IOM) is - first developed at ISP-NICT, Japan - used for managing spatio-temporal-semantic events detected from heterogeneous sensors - published in [1] Minh-Son Dao, Koji Zettsu, Siripen Pongpaichet, Laleh Jalali, Ramesh Jain: Exploring spatio-temporal-theme correlation between physical and social streaming data for event detection and pattern interpretation from heterogeneous sensors. Big Data 2015: 2690-2699 Demo of using IOM in detect events affected by heavy rain in Japan, here https://www.youtube.com/watch?v=vkvzZuP8W60

1. Complex IOM
Creation and Evaluation
Minh-Son Dao, PhD
dao.minhson@gmail.com

2. IOM
Information Object Model is
• first developed at ISP-NICT, Japan
• used for managing spatio-temporal-semantic events
detected from heterogeneous sensors
• published in
[1] Minh-Son Dao, Koji Zettsu, Siripen Pongpaichet, Laleh
Jalali, Ramesh Jain: Exploring spatio-temporal-theme
correlation between physical and social streaming data for
event detection and pattern interpretation from
heterogeneous sensors. Big Data 2015: 2690-2699
Demo of using IOM in detect events affected by heavy rain in
Japan, here
https://www.youtube.com/watch?v=vkvzZuP8W60

3. CreatePatterns
AAAAAABBBAAAAAAAAAAAAAAAAAAAAAAABBBBBBAAAABBCCCCCCCCCCCCCCCCCC
CCCCBBCBCCABAAAAAAABCCCAAAAAAAABCCCCCCCBCCBBCCCCCCCAABCCBAAAAA
Trend SAX
Burst
t1
s t1
e
t2
s t2
e t3
s t3
e
…BCCCCCCCCCCCC…
…BBCCCCCCCAABC…
…BCCCCCCCCCCCC…
…BBCCCCCCCAABC…
…BCCCCCCCCCCCC…
…BBCCCCCCCAABC…
…BCCCCCCCCCCCC…
…BBCCCCCCCAABC…
…BCCCCCCCCCCCC…
…BBCCCCCCCAABC…
…BCCCCCCCCCCCC…
…BBCCCCCCCAABC…
. . .
CAA, CCC ->
positive label
CCC, CCC ->
positive label
CCC, CCC ->
positive label
CCC, CCC ->
positive label
CCC, CCC ->
positive label
AAB, CCC ->
positive label
CCC, CCC ->
positive label
Transactions
CCC, CCC -> positive label
CCA, CCC -> positive label
CAA, CCC -> positive label
AAB, CCC -> positive label
Physical
sensors
Social, IOM
sensors

4. CreatePatternsforIOM
AAAAAABBBAAAAAAAAAAAAAAAAAAAAAAABBBBBBAAAABBCCCCCCCCCCCCCCCCCC
CCCCBBCBCCABAAAAAAABCCCAAAAAAAABCCCCCCCBCCBBCCCCCCCAABCCBAAAAA
Trend SAX
Burst
t1
s t1
e
t2
s t2
e t3
s t3
e
BCC, BBA -> positive label
CCA, BAA -> positive label
CAB, AAA -> positive label
ABA, AAA -> positive label
BCC, AAA -> positive label
CCC, AAA -> positive label
CCA, AAA -> positive label
CCC, CCC -> positive label (heavy rain)
(ignore duplicate patterns)
CCA, CCC -> positive label
CAA, CCC -> positive label
AAB, CCC -> positive label
generate
generate
generate
Patterns
(feature
vectors/rules)

5. CreateIOM(offline)
latitude
Time
Physical
sensors
Tweet
sensor
tTSW
e
tTSW
s
STT
cube
Sensor
cell
AAAAAABBBAAAAAAAAAAAAAAAAAAAAAAABBBBBBAAAABBCCCCCCCCCCCCCCCCCC
CCCCBBCBCCABAAAAAAABCCCAAAAAAAABCCCCCCCBCCBBCCCCCCCAABCCBAAAAA
Trend SAX
Burst
t1
s t1
e
t2
s t2
e t3
s t3
e
2014-Jun-01
IOM
t1
s
t1
e
t2
s
t2
e
t3
s
t3
e
2014-Jun-30

6. latitude
Time
STT
cube
AAAAAABBBAAAAAAAAAAAAAAAAAAAAAAABBBBBBAAAABBCCCCCCCCCCCCCCCCCC
CCCCBBCBCCABAAAAAAABCCCAAAAAAAABCCCCCCCBCCBBCCCCCCCAABCCBAAAAA
Trend SAX
Burst
t1
s t1
e
t2
s t2
e t3
s t3
e
2014-Jun-01
IOM
t1
s
t1
e
t2
s
t2
e
t3
s
t3
e
2014-Jun-30
t1
s
t1
e
2014-Jun-30
2014-Jun-01
Determining the area with the
highest co-occurrence of data
Heterogeneous data stream
Refining IOMs

7. Confidence&SupportRatesforIOM
2014-Jun-01
t1
s
t1
e
t2
s
t2
e
t3
s
t3
e
2014-Jun-30
Instances of other tweet-sensors
Instance of tweet sensor
Zoom-in
t1
s
t1
e
Confidence =
#
(# + # + #
[inside STC]
Support =
# [inside STC]
# [inside STC] + # [outside STC and inside ex-STC ]
STC (longitude, latitude, time) cube
latitude
) [inside STC]
Gaussian Distribution
Ex-STC a cube covers STC with each dimension extended by Gaussian function
(How important an
event represented by
IOM is w.r.t people
attention ?)
(How large IOM can cover an event w.r.t people attention? In other words, is IOM captures
the scope of event impacts?)
X
# + # + # + #
[inside STC]
ǁlongǁxǁlatǁxǁtimeǁ)

8. La1
T1
La1 x Δ1
T1+Δ3
Relevant (9)
Irrelevant (24)
IOM1 is more
confidence than
IOM2
IOM1
IOM2
ǁLa1ǁ = 2
ǁLo1ǁ = 3
ǁT1ǁ = 1.5
Δ1 = 1.5
Δ2 = 0.5
Δ3 = 2.5
9
24
9 + 24
2 ∗ 3 ∗ 1.5*Confidence IOM1 =
9
24
9 + 24
(2 + 1.5) ∗ (3 + 0.5) ∗ (1.5 + 2.5)*Confidence IOM2 =
= 1.375
= 0.252
Confidence Example

9. t1
s
t1
e
Gaussian Distribution
t1
s
t1
e
Gaussian Distribution
IOM1 IOM2
10
7
8
11
Support IOM1 = Support IOM2 =is better than
Support Example

10. 2
latitude
Time
1
2
latitude
longitude
1
2
3
Time
t1
s
t1
e
t5
s
t5
e
t3
s
t3
e
t2
s
t2
e
1
3
4 5
t4
s = t5
s < t1
s < t4
e = t5
e < t2
s < t1
e < t3
s < t2
e < t3
e
t4
s
t4
e
Project to (long, lat)
Projectto“time”
1
2
3
?
Predict
Merge
Track
Physical
sensors
Interpolate
ComplexIOM(intra-IOMs)
IOM
sensor cell
(rectangle)
Tweet
sensor
4
3
54
5
? 4

11. 2
latitude
Time
1
2
latitude
longitude
1
2
3
Time
t1
s
t1
e
t3
s
t3
e
t2
s
t2
e
1
3
Time-rule = t1
s < t2
s < t1
e < t3
s < t2
e < t3
e
Project to (long, lat)
Projectto“time”
1
2
3
?
ComplexIOM
(Inter-IOMs)
IOM
3
Spatio-rule
Theme-rule =
(IOM1: heavy rain,
IOM2: flooded
road
IOM3: metro
station gets flood)
→IOM4: traffic jam
in area A
If at t3
s, theme-rule, time-
rule, and spatio-rule are
satisfied, IOM4 is detected
Area
A

12. latitude
Time
1
2
3
latitude
longitude
1
2
3
4
Time
t1
s
t1
e
t5
s
t5
e
t3
s
t3
e
t2
s
t2
e
1
2
3
4 5
t4
s = t5
s < t1
s < t4
e = t5
e < t2
s < t1
e < t3
s < t2
e < t3
e
t4
s
t4
e
Project to (long, lat)
Projectto“time”
1
2
3
?
Predict
Merge
Track
Interpolate
Multi-resolutionIOM(case2)
IOM
Tweet cell
(circle)
sensor cell
(rectangle)
? 4
54
5
Physical
sensors
Tweet
sensor