My presentation at Search Computing on a topic that I think is going to be quite popular soon.

1.
9/25/12
1

2. An
Elephant
and
Six
Blind
Men

3. An
Elephant
is
NOT
• Wall
• Rope
• Snake
• Spear
• Tree
• Fan

4. An
Elephant
is
…
an
Elephant

5. Mobile
Social
Search
(MSS)
is
NOT
Mobile
is
NOT
Social
is
NOT
Search
Is
Mobile
Social
Search

6. Changing
Times
in
Search
Mobile
Social
Search:
Search
‘inside’
problem
solving
in
real
;me.
Problem
Solving
Search

7. MSS:
A
Problem
of
Riches
When
we
were
(data)
poor
–
we
searched.
Now
that
we
are
(data)
rich
–
we
need
mobile
social
search.

8. Search
Connecting

9. Then
Now

10. Then
Now

11. • What
is
an
Italian
Restaurant?
– List
Italian
Restaurants
in
Brussels.
• Show
me
Italian
Restaurants?
– Use
Yelp
(or
any
other
social
approach)
and
distance
from
me
to
rank
them.
• Where
should
I
go
to
eat
Italian
food
NOW?
– Consider
Tme
taken,
current
ambiance,
and
quality
of
food
into
consideraTon.

12. • What
are
symptoms
of
Swine
Flu?
• Is
there
a
major
Swine
Flu
outbreak
in
my
area?
• You
are
likely
to
be
very
sick
with
extreme
case
of
Swine
Flu
soon,
your
doctor
is
ready
with
the
set
up.

13. Social
Networks
Connecting
People

14. Social
Life
Networks
Connecting Information
People
Aggregation Situation Alerts
and Detection
CompositionAnd
Queries
Resources
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15. Concept
recogni;on
from
data
Heterogeneous
Media
Heterogeneous
Media
LocaTon
Scenes
Single
Media
Environm Trajectories
SituaTons
aware
ents
K
3.4
Single
Media
LocaTon
Visual
Real
world
Visual
360
K
11.4K
AcTviTes
unaware
Objects
Objects
Events
StaTc
Dynamic
SPACE
TIME
15

16. Proprietary
and
ConfidenTal,
Not
For
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16
DistribuTon

17. • SituaTon:
An
acTonable
abstracTon
of
observed
spaTo-­‐temporal
characterisTcs.
• Allow
users
to
define
their
own
spaTo-­‐
temporal
features
and
create
the
situaTon
detecTon
filters.
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18. Swine
flu:
Situa;on
Segmenta;on
into
‘high’
and
‘low
’ac;vity
zones.
Proprietary
and
ConfidenTal,
Not
For
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18
DistribuTon

19. (a) Pollen levels (Source: Visual) (b) Census data (Source: text file) (c) Reports on ‘Hurricanes’ (source: Twitter stream)
d) Cloud cover (Source: Satellite imagery) (e) Predicted hurricane path (source: KML) (f) Open shelters coverage(Source: KML)
Representa;on
for
different
data
sources
into
a
common
spa;o-­‐temporal
format.

20. Level
0:
Raw
data
streams
e.g.
tweets,
cameras,
traffic,
weather,
…
…
Level
1:
Unified
representaTon
ProperTes
(STT
Data)
STT
Stream
Level
2:
AggregaTon
ProperTes Emage
(Emage)
Level
3:
Symbolic
rep.
ProperTes SituaTon
(SituaTons)

21. Billions
of
data
sources.
Selec;ng
and
combining
appropriate
sources
to
detect
situa;ons.
Interac;ons
with
different
types
of
Users
Decision
Makers
Individuals
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21

22. Front
End
GUI
New New E-­‐mage Alert
Data Query Stream Request
Source
Back
End
Controller
E-­‐mage
Stream
Personalized
Registered Stream
Query
Processor
Queries Alert
Unit
E-­‐mage
Stream User
Info
Registered
Data Data
Ingestor Raw
Data
Storage
Sources
API
Calls Raw
Spatial
Data
Stream
Data
Cloud
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23. 9/25/12
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24. S.
No
Operator
Input
Output
1
Selection
σ
Temporal
Temporal
E-­‐mage
Set
E-­‐mage
Set
2
Arithmetic
&
K*Temporal
E-­‐mage
Temporal
E-­‐mage
Set
Logical⊕
Set
3
Aggregation
α
Temporal
E-­‐mage
set
Temporal
E-­‐mage
Set
4
Grouping
γ
Temporal
E-­‐mage
Set
Temporal
E-­‐mage
Set
5
Characterization
:
• Spatial
φ
• Temporal
E-­‐mage
Set
• Temporal
Pixel
Set
• Temporal
τ
• Temporal
Pixel
Set
• Temporal
Pixel
Set
6
Pattern
Matching
ψ
• Spatial
φ
• Temporal
E-­‐mage
Set
• Temporal
Pixel
Set
• Temporal
τ
• Temporal
Pixel
Set
• Temporal
Pixel
Set
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25. Personal
SituaTon
Proprietary
and
ConfidenTal,
Not
For
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25
DistribuTon

26. Macro
situa;on
Alert
Level=High
Date=12/09/10
Micro
event
Situa;onal
controller
Control
Ac;on
e.g.
“Arrgggh,
I
“Please
visit
have
a
sore
throat”
• Goal
nearest
CDC
(Loc=New
York,
• Macro
SituaTon
center
at
4th
St
Date=12/09/10)
• Rules
immediately”
Level
1
personal
threat
+
Level
3
Macro
threat
-­‐>
Immediate
ac;on
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27. Planetary
1)
Macro
scale
sensing
situaTon
Social
sensors
Device
sensors
Macro
sensors
2)
Personalized
situaTon
Personal
context
Personal
life
streams
Profile/
Preferences
e.g.
High
Flu
risk
3)
Recommend
AcTons
Available
resources
Resource
data

28. Classify (Flood level - Shelter
Flood Shelter)
Twitter
Flood Level
Shelter
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29. Taking
personalized
acTons
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30. • Was
for
Archived
data
and
mostly
for
researchers
(using
Desktop).
• Currently:
Mobile
clients,
Local
data,
and
Social
Graph
(SoMoLo).
• Immediate
Future:
SituaTons
from
Real
Time
data
and
RecommendaTons.
• Future:
PredicTve
control
of
emerging
situaTons.

31. Thanks
for
your
Tme
and
alenTon.
For
quesTons:
jain@ics.uci.edu

32. Applica;on
scenarios
¨ Business
decision
making:
Demand-­‐supply
analysis,
opening
a
new
store,
offer,…
¨ Medical
:
Epidemic
monitoring,
Asthma,
polluTon
effect
miTgaTon
¨ Disaster
relief:
(hurricane,
flood,
fire)
direcTng
people
to
appropriate
resources.
¨ Traffic:
SuggesTng
best
routes
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