The nature of storytelling has been evolving. Now it is becoming more data-based in many applications. This objective storytelling is closely tied to rise of big data.

1. 1

2. • To
live
is
to
communicate!
– All
Human
dealings
are
based
on
communica7on.
Modern
model
of
Communica0on

3. • Verbal
• Non
Verbal
• Formal
• Informal

4. • Informa7on
Sharing
• Knowledge
Sharing
• Emo7on
Sharing
• Comple7ng
transac7ons

5. Even
scien0fic
papers.

6. • A
story
is
a
chain
of
events
that
begins
at
one
place
and
ends
at
another.
• A
story
is
a
selec0ve
batch
of
informa0on.
It
selects
details,
arranges
them,
perhaps
embellishes
them.
hEp://janefriedman.com/2011/09/27/what-­‐is-­‐a-­‐story/

7. • Storytelling
is
conveying
of
event
experiences
in
– Words,
– Images,
– Audio
– Mul7media
• For
– Entertainment
– Educa7on
– Cultural
preserva7on
– Moral
values

8. • Imaginary
– Mythology
– Science
Fic7on
– Fic7on
• Historical
• Hybrid
• Data
based
8

9. Mythology
shapes
culture:
Mythological
stories
become
stronger
than
historical
facts.

12. 12
Hans
Rosling
from
Gapminder.org

13. • Capture
• Storage
• Selec7on/Filtering
• Transmission
• Rendering

15. Human
memory
Big
Data

16. 16

17. 17

18. 18

19. Media
• Marshal
McLuhan
in
1964.
– A
medium
itself,
not
the
content
it
carries,
should
be
the
focus
of
study.
– A
medium
affects
the
society
in
which
it
plays
a
role
not
only
by
the
content
delivered
over
the
medium,
but
also
by
the
characteris@cs
of
the
medium
itself.

20. Media
+
+
Digital
technology
allowed
combining
and
using
media
leading
to
a
major
disrup0on.

21. 21
From
Mary
Meeker’s
presenta@on

23. • Con7nuously
growing
warehouse
of
events.
• Events
related
to
Everything.

25. • In
Real
World.
• Images,
video,
audio,
all
other
sensors
are
just
a
means
to
detect
and
capture
some
aspect
of
the
event.

27. • Prepare
a
database
of
all
events.
– Collect
all
experien0al
and
informa0onal
data
about
each
event.
• Considering
audience
and
the
message,
select
important
events.
• Render
Events
– For
each
event
decide
what
informa0on
and
experien0al
data
should
be
used.
– Render
all
events
and
related
data
in
the
best
format
for
highest
quality
of
experience.

28. Inven0ng
Events
Selec0ng
Events
Rendering
Events
Imagine

29. Collec0ng
Events
Selec0ng
Events
Rendering
Events
Analyze
Data

30. 30

31. 31

32. 32

33. • Why
is
video
edi0ng
so
difficult?
– Even
aOer
so
many
products?
• How
can
we
make
it
usable
by
consumers?
• Is
Video
edi0ng
a
real
problem?
• Is
it
possible
to
reinvent
video
edi0ng
given
all
the
‘big
data’?

34. Face ID Lyn Russell
Hillary Clinton
Lyn Russell
Clinton running
for senate
Speaking
at rally
Improving
Medicare
“Clinton spoke
with reporters” “When She arrived” “Older citizens
who have”
00:08:26:12 00:08:34:29 00:08:40:00
00:08:26:12 00:08:34:29 00:08:40:00
Speaker ID
User Annotation
Speech to Text
Time Code
Keyframes
Video
Time
Hillary Clinton
©
Ramesh
Jain
34

35. OTHER
Ac7vity
Loca7on
Manual
Audio
Visual
Walk
Walk
Walk
Walk
Walk
Walk
Home
Drive
Mee7ng
Work
Exercise
Home
Drive
Home
DriveM
ee7ng
WoMrke
e7ng
Work
Exercise
Exercise
Event
Track

36. 36

38. • Traveled
a
lot
–
and
took
about
15000
photos.
• Want
to
share
Story
of
Year
2014
in
Photos.
• Use
about
15-­‐20
photos.
• With
– My
professional
friends
– My
family
– My
friends
– My
…

39. Your Personalized Visual Web

41. • Top
8
Apps
in
2013
were
Visual.
• Selfies
are
becoming
a
communica7on
tool.
• Significantly
more
7me
spent
on
visual
media.
Source:
hEp://digiday.com/plagorms/5-­‐charts-­‐growth-­‐visual-­‐web/

44. Events
Loca7o
n
People

45. • Biographies
and
Auto-­‐biographies
• Using
it
for
sharing
experiences
• Predic0ng
and
controlling
future

46. Un0l
recently,
you
were
either
your
memories
or
a
folder.

47. Now

48. • Anecdotal
• Diarizing
data
• Quan0fied
Self
Objec0ve
Self

50. Health
Profile
Profess-­‐
ional
Interest
Social
Perosna-­‐
lity
• Sports
• Books
• Movies
• Music
• …
• Family
• Friends
• Religion
• Poli7cs
• …
• Type
• Temper
• Style
• Appear-­‐
ence
• …
• Gene7cs
• Life
Style
• Medicine
• Known
Ailments
• …
• Personal
Info
• Languages
• Contact
Info
• Special
Info
• …
• Current
• Past
• Exper7se
• Leadership
• …

51. • When
did
I
get
interested
in
Digital
Storytelling?
• When
did
my
friendship
with
Tom
start
gekng
sour?
Why?
• What
causes
me
sleepless
nights?

52. • Ge[ng
objec0ve
data
• Storing
data
• Analyzing
data
• Privacy
of
data.

53. Ac0vi0es
Media
Communica0ons
AGGREGATED
PERSONAL
ANALYTICS
Sensors
Cyber-­‐
Physical-­‐
Social
Cloud

54. • Con7nuously
growing
warehouse
of
personal
events.
• Events
related
to
Everything
in
personal
life.
• Private,
Social,
Professional,
Health,
Spiritual,
…

56. • Personicle
=
Personal
Chronicle
• Personal
Data
warehouse
• BeEer
if
automa7cally
collected
56

63. • Con0nuous
sensors
crea0ng
‘health
persona’.
• Predic0ve
techniques
for
preven0ve
advice.
• Quan0ta0ve
measurements
for
be^er
control.

64. • Understanding
personal
ac0ons
in
context.
• Crea0ng
Personal
‘Good
Health
Habits’
in
own
eco-­‐system.
• Contextual
persuasive
pervasive
feedback.
• Connected:
The
Surprising
Power
of
Our
Social
Networks
and
How
They
Shape
Our
Lives.

65. • Red: “Severe asthma attack”.
• Orange: High activity level.
• Exercise triggers asthma!
Personicle
Ac7vity
Stream
t1
t2
t3
t4
t5

66. • Blue: Shortness of breath. The lighter the
color, the worse the symptom.
• Green: Low air pollution.
• Red to Purple: High and very high air
pollution
• Exposure to polluted air is a risk factor
of asthma attack within X hour?
Personicle
case
Environmental
factors

68. • Storytelling
is
older
profession
than
supposedly
oldest
profession.
• We
can
detect
events
using
many
sources
of
data;
and
we
may
use
those
in
stories.
• Stories
derived
from
Personicles
represent
Objec0ve
Self.
– Objec0ve
self
helps
individuals
in
many
ways.
– By
u0lizing
objec0ve
self
data
for
society,
we
can
build
be^er
societal
models
for
solving
challenging
problems.

69. For
ques7ons:
jain@ics.uci.edu