The document discusses the challenges and opportunities faced by cities as they evolve into smarter urban environments, particularly in the context of rapid urbanization and the need for improved infrastructure. It highlights the importance of leveraging big data, semantics, and open data to enhance city management, drive citizen engagement, and optimize services such as traffic management. Specific case studies from cities like Dublin, Miami, and Bologna illustrate how innovative technology and data analytics can address urban issues, improve efficiency, and promote sustainable urban development.

1. LD4SC-­‐15
Seman&cs
for
Smarter
Ci&es.
Hype
or
Reality?
A
Study
in
Dublin,
Bologna,
Miami,
and
Rio
Freddy
Lecue

2. LD4SC-­‐15
Context

3. LD4SC-­‐15
Arrival of the “urban millennium”
By 2050 over 6 billion people, two thirds of humanity, will be living in towns and cities
Source: Smart Cities How will we manage our cities in the 21C?, Colin Harrison IBM Corporate
Strategy Member, IBM Academy of Technology
Indoor air pollution
resulting from the use of
solid fuels [by poorer
segments of society] is a
major killer!
Claims the lives of 1.5 million people
each year, more than half of them below
the age of five (4000 deaths per day)
Water problems affect
half of humanity!!!!
1.1 billion people in developing
countries have inadequate access to
water, and 2.6 billion lack basic
sanitation
1.6 billion people —
a quarter of
humanity — live
without electricity!
South Asia, Sub-Saharan Africa
and East Asia have the greatest
number of people living without
electricity (as high as 706 million
in South Asia)Source: 2008 UN Habitat; Smart Cities How will we manage our
cities in the 21C?, Colin Harrison IBM Corporate Strategy
Introduc&on
Mo&va&on
–
Today’s
Ci&es
are
Confronted
with
Serious
Dilemmas

4. LD4SC-­‐15
ASIA N.A. & EUROPE AFRICA LATIN AMERICA
Rapid expansion Negative growth
Rural exodus increasing
poverty
Decentralization
§ Over the next decade, Asia’s
urban areas will grow by more
than 100,000 people a day
§ Growth rates are more rapid
than the investment in
infrastructure
§ Benefits of new infrastructure
investments have not been
distributed equally
§ 46 countries (including
Germany, Italy, most former
Soviet states) are expected to
be smaller in 2050
§ The number of shrinking
cities has increased faster in
the last 50 years than the
number of expanding cities
§ In 2008, more than 12M
Africans left their rural homes
to live in urban areas
§ The projected increase in
urban migration will exacerbate
the problems of providing
infrastructure, sanitation.
health services, and food
§ Large cities have
incorporated nearby villages
and towns – as a result, large
urban areas developed sub-
centers whose functions
duplicated those of the central
city
§ Many large cities are
competing with their outlying
suburbs for people, revenue,
and employment
Source:
Various;
IBM
MI
Analysis;
Introduc&on
Mo&va&on
–
Regions
have
both
Common
and
Unique
Challenges

5. LD4SC-­‐15
Source:
Various;
IBM
MI
Analysis;
Introduc&on
The Sustainable Eco-City
The Well Planned City
The Healthy and Safe City
The Cultural-
Convention Hub
The City of Digital Innovation
Mo&va&on
–
Beyond
the
prac&cal
objec&ves,
ci&es
have
‘aspira&ons’
The City of Commerce

6. LD4SC-­‐15
Miami,
USA
Dublin,
Ireland
Rio,
Brazil
• 5.5
billion
hours
of
travel
delay
• 2.9
billion
gallons
of
wasted
fuel
in
the
USA
• $121
billion
/
year
• 0.7%
of
USA
GDP
*5
over
the
past
30
years
Bologna,
Italy
How
to
reduce
traffic
congesHon
?
Introduc&on
Mo&va&on
–
Socio-­‐Economic
Context

7. LD4SC-­‐15
Introduc&on
Mo&va&on
–
Limita&on
of
Exis&ng
Systems
(Example:
Traffic)
(1)
Most
traffic
systems
already
support
Basic
Analy&cs
and
Visualiza&on!!!
Basic
AnalyHcs
from
Bus
/
Taxi
data

8. LD4SC-­‐15
• All
exis&ng
traffic
management
systems
are
based
on
ONE
signal
/
stream
• No
possible
interpretaHon
of
traffic
Anomalies
• No
IntegraHon
of
Exogenous
Data
No
SemanHcs
/
Context
!
No
ExplanaHon
No
Diagnosis
Introduc&on
Mo&va&on
–
Limita&on
of
Exis&ng
Systems
(Example:
Traffic)
(2)

9. LD4SC-­‐15
• Sensor
data
assimilaHon
– Data
diversity,
heterogeneity
– Data
accuracy,
sparsity
– Data
volume
•
Modelling
human
demand
– Understand
how
people
use
the
city
infrastructure
– Infer
demand
pa_erns
•
Factor
in
Uncertainty
– Opera&ons
and
planning
– Organise
and
open
data
and
knowledge,
to
engage
ci&zens,
empower
universi&es
and
enable
business
Introduc&on
Seman&cs
for
ci&es:
How
can
Seman&cs
help
ci&es
transform
?

10. LD4SC-­‐15
Introduc&on
Seman&cs
for
ci&es:
why
now?
Open
Data!

11. LD4SC-­‐15
Introduc&on
Seman&cs
for
ci&es:
why
now?
Ci&es
want
to
be
Smarter
Ci&es!
Nowadays:
Issues
of
Urban
Quality
such
as
housing,
economy,
culture,
social
and
environmental
condi&ons.

12. LD4SC-­‐15
Introduc&on
Seman&cs
for
ci&es:
why
Seman&cs?
Big
Data

13. LD4SC-­‐15
Working harder is not sustainable
Cities require innovative approaches
Introduc&on
Seman&cs
for
ci&es:
why
Seman&c
Web,
Linked
Open
Data?
Smart
Systems

14. LD4SC-­‐15
Introduc&on
Seman&cs
for
ci&es
Walkonomics
FixMyStreet
Schooloscope
FloodAlerts
BeatTheBurglar
Where
can
I
live?

15. LD4SC-­‐15
Introduc&on
Seman&cs
for
ci&es
–
Our
Experience

16. LD4SC-­‐15
Data
format
and
data
access,
collec&on,
storage,
transforma&on
Big
Data
–
The
World
of
Data
Source:
Various;
IBM
MI
Analysis;

17. LD4SC-­‐15
Data
format
and
data
access,
collec&on,
storage,
transforma&on
Big
Data
–
4Vs
Source:
Various;
IBM
MI
Analysis;

18. LD4SC-­‐15
Data
format
and
data
access,
collec&on,
storage,
transforma&on
Data
Format
and
Heterogeneity
-­‐
City
Data
!=
Open
Data
(1)
A Global
Movement Has
Begun to Provide
Transparency and
Democratization
of Data
18November 30, 2011
Don’t see your site?
Update via @usdatagov
(*)
“Driving
Innova&on
with
Open
Data”,
Jeanne
Holm,
Data.gov,
February
9th,
2012
(Presenta&on
to
Ontology
2012)
Think Big, Start Small, Innovate
Data.gov Quick Facts May 2009 October 2011
Total datasets available 47 >400,000
Hits to Data.gov 0 >200 million
Apps and mash ups by citizens and government 0 372 + 1113
RDF triples for semantic applications 0 6.7 billion
Dataset downloads 0 >2.0 million
Nations establishing open data sites 0 28
States offering open data sites 0 31
Cities in North America with open data sites 0 13
Open data contacts in Federal agencies 24 396
Agencies and subagencies participating 7 185
Communities 0 7
Community challenges 0 23
November 30, 2011 11
A
lot
of
relevant
open
data
for
city
data
analy&cs

19. LD4SC-­‐15
Applica&ons
in
Ci&es:
Bologna,
Dublin,
Miami,
Rio…
and
Airlines

20. LD4SC-­‐15
Miami,
USA
STAR-­‐CITY
(Seman&c
Traffic
Analy&cs
and
Reasoning
for
CITY)
Input
• Type
of
anomaly
(bus
speed,
ambulance
delay…)
• Type
of
explana&on
(city
events,
unplanned
events,
road
works,
…)
Output
• Impact
of
events
and
their
characteris&cs
on
anomalies
SemanHc
InterpretaHon
of
Diagnosis
SemanHc
Reasoning:
Diagnosis
SemanHc
Context
SemanHc
Search:
Diagnosis
ExploraHon
Live
IBM
demo:
h_p://208.43.99.116:9080/simplicity/demo.jsp
Live
WWW
demo:
h_p://dublinked.ie/sandbox/star-­‐city/
Video:
h_p://goo.gl/TuwNyL

21. LD4SC-­‐15
STAR-­‐CITY
(Seman&c
Traffic
Analy&cs
and
Reasoning
for
CITY)
Context
• Dublin:
(Diagnosis
of)
Traffic
conges&on
• Bologna:
(Diagnosis
of)
Bus
conges&on
• Miami:
(Diagnosis
of)
Bus
bunching
• Rio:
(Diagnosis
of)
Low
on-­‐&me
performance
of
buses
Source
of
Anomaly
Source
of
Diagnosis

22. LD4SC-­‐15
Challenge:
Source
of
Anomaly
Source
of
Diagnosis
• Explaining
Traffic
Condi&ons
with
Diagnosis
Reasoning
• Logical
correla&on
of
anomalies
and
diagnosis
in
dynamic
sepngs
• Knowledge
Representa&on
and
Reasoning
• Machine
Learning
/
AI
Diagnosis
• Database:
Large
scale
data
integra&on
• Signal
Processing
/
Stream
Reasoning
Core
Areas
/
Problems:
STAR-­‐CITY
(Seman&c
Traffic
Analy&cs
and
Reasoning
for
CITY)

23. LD4SC-­‐15
Architecture
of
Diagnosis
Components
STAR-­‐CITY
(Seman&c
Traffic
Analy&cs
and
Reasoning
for
CITY)

24. LD4SC-­‐15
STAR-­‐CITY
(Seman&c
Traffic
Analy&cs
and
Reasoning
for
CITY)
Data
Vocabulary

25. LD4SC-­‐15
Challenge:
Predic&ve
reasoning
(as
opposed
to
analyHcs)
in
heterogeneous
and
dynamic
sepngs
• Knowledge
Representa&on
and
Reasoning
• Machine
Learning
/
Knowledge
Discovery
• Database:
Large
scale
data
integra&on
• Signal
Processing
/
Stream
Reasoning
Core
Areas
/
Problems:
Traffic
Condi&on
Road
Incident
Weather
Condi&on
STAR-­‐CITY
(Seman&c
Traffic
Analy&cs
and
Reasoning
for
CITY)

26. LD4SC-­‐15
Miami,
USA
STAR-­‐CITY
(Seman&c
Traffic
Analy&cs
and
Reasoning
for
CITY)
Input
• Type
of
anomaly
(bus
speed,
ambulance
delay…)
• Type
of
explana&on
(city
events,
unplanned
events,
road
works,
…)
Output
• Impact
of
events
and
their
characteris&cs
on
anomalies
Input
• Type
of
anomaly
(bus
speed,
ambulance
delay…)
• Type
of
explana&on
(city
events,
unplanned
events,
road
works,
…)
Output
• Real-­‐&me/Historical
diagnosis
results
• Evolu&on
of
diagnosis
over
&me/
space
• Comparison
vs.
historical
Objec&ve:
Real-­‐Time
and
Historical
Traffic
Diagnosis
(1)
Live
demo:
h_p://208.43.99.116:9080/simplicity/demo.jsp
Video:
h_p://goo.gl/TuwNyL

27. LD4SC-­‐15
Miami,
USA
STAR-­‐CITY
(Seman&c
Traffic
Analy&cs
and
Reasoning
for
CITY)
Input
• Type
of
anomaly
(bus
speed,
ambulance
delay…)
• Type
of
explana&on
(city
events,
unplanned
events,
road
works,
…)
Output
• Impact
of
events
and
their
characteris&cs
on
anomalies
Input
• Type
of
anomaly
(bus
speed,
ambulance
delay…)
• Type
of
explana&on
(city
events,
unplanned
events,
road
works,
…)
Output
• Categoriza&on
of
diagnosis
Live
demo:
h_p://208.43.99.116:9080/simplicity/demo.jsp
Video:
h_p://goo.gl/TuwNyL
Objec&ve:
Real-­‐Time
and
Historical
Traffic
Diagnosis
(2)

28. LD4SC-­‐15
Miami,
USA
h_p://vtce.altervista.org/
Reverse
STAR-­‐CITY
system
for
city
managers

29. LD4SC-­‐15
Reverse
STAR-­‐CITY
system
for
city
managers
Objec&ve:
City
Planning
Input
• Type
of
anomaly
(bus
speed,
ambulance
delay…)
• Type
of
explana&on
(city
events,
unplanned
events,
road
works,
…)
Output
• Impact
of
events
and
their
characteris&cs
on
anomalies
h_p://www.vtce.altervista.org/

30. LD4SC-­‐15
Reverse
STAR-­‐CITY
system
for
city
managers
Data
Vocabulary

31. LD4SC-­‐15
Miami,
USA
Mobile
STAR-­‐CITY
app
for
ci&zen
in
Dublin
and
Bologna

32. LD4SC-­‐15
Miami,
USA
Mobile
STAR-­‐CITY
app
for
ci&zen
in
Dublin
and
Bologna
Use
case
scenario:
MeeHng
the
Increased
Mobility
Demand:
• Scenario
1
“Road
Traffic
Diagnosis”
• Scenario
2
“Road
Traffic
Predic&on”
• Scenario
3
“Personalized
Traffic
Restric&ons”
Outcome:
• One
mobile
app
• Two
pilot
ci&es
(Dublin
and
Bologna)
• Live
and
real-­‐&me
environment
• Real
data
(user
calendar,
open
data:
traffic
conges&on,
weather,
events,
road
works,
accident
…)
• but
with
simulated
car
sensor
data
–
Aus&n
;-­‐)
Context

33. LD4SC-­‐15
Mobile
STAR-­‐CITY
app
for
ci&zen
in
Dublin
and
Bologna
Data
Dublin
Car-­‐related
User-­‐related
Vocabulary
Bologna

34. LD4SC-­‐15
Mobile
STAR-­‐CITY
app
for
ci&zen
in
Dublin
and
Bologna
User
Context-­‐aware
Driving
(User
data)
Open
Context-­‐aware
Driving
(Open
data)
Private
Context-­‐aware
Driving
(City
data)
Objec&ve:
Context-­‐aware
driving
experience
(1)

35. LD4SC-­‐15
Mobile
STAR-­‐CITY
app
for
ci&zen
in
Dublin
and
Bologna
Objec&ve:
Context-­‐aware
driving
experience
(2)
Personal
Events-­‐aware
Driving
Car
Sensor
Simula@on
Car
Sensor-­‐aware
Driving

36. LD4SC-­‐15
Real-­‐Time
Urban
Monitoring
in
Dublin
h_ps://www.youtube.com/watch?v=ImTI0jm3OEw
Green:
Dublin
Bike
availability
Purple
dot:
Bus
in
congesHon
Blue:
Noise
Purple
bar:
PolluHon
Red:
AmeniHes
Yellow:
Cameras

37. LD4SC-­‐15
Seman&c
Processing
of
Urban
Data
h_ps://www.youtube.com/watch?v=lrUHet5awzw&feature=youtu.be
h_p://50.97.192.242:8080/Dali/

38. LD4SC-­‐15
Cogni&ve
Explana&on
of
Flight
Delays

39. LD4SC-­‐15

40. LD4SC-­‐15
Conclusion
Ci&es
are
characterized
by:
• Big
Data
• Complex
Systems
Ci&es
want
to
be
Smarter:
• More
efficient
• More
reliable
• More
secure
• More
open
Ci&es
can
benefits
from
• REAL
World
data:
Open
Data
to
get
Smarter
• Advances
in
AI
AI
already
helped
a
lot!!
...
and
should
even
contribute
further
• Op&miza&on,
coordina&on
…
• Cheaper
• Faster
• More
integrated
• More
ci&zen-­‐centric
• More
a_rac&ve
• More
Intelligent
• Sustainable
• Be_er
city
planning
• Integrated
Problems
• Scalability
Challenges

41. LD4SC-­‐15
Future
Work
Analy&cs
and
Reasoning:
Scalability
from
One
City
to
Another
One
Picture
of
different
ci&es

42. LD4SC-­‐15
Future
Work
Applica&on:
From
Ci&es
to
mini-­‐Ci&es
Airport
Supply-­‐Chain
Building
Stadium
Warehouse

43. LD4SC-­‐15
Future
Work
More
Mul&-­‐disciplinary:
AI
(Planning,
KRR,
ML,
…),
Database,
Mathema&cs
…
More
Science
Integra&on

44. LD4SC-­‐15
References
(1)
• Freddy
Lecue,
Jeff
Z.Pan.
Consistent
Knowledge
Discovery
from
Evolving
Ontologies.
AAAI
2015.
Aus&n,
Texas,
USA,
January,
2015.
• Anika
Schumann,
Freddy
Lecue.
Minimizing
User
Involvement
for
Accurate
Ontology
Matching
Problems.
AAAI
2015.
Aus&n,
Texas,
USA,
January,
2015.
• Freddy
Lecue,
Simone
Tallevi-­‐Diotallevi,
Jer
Hayes,
Robert
Tucker,
Veli
Bicer,
Marco
Sbodio,
Pierpaolo
Tommasi.
Smart
traffic
analy&cs
in
the
seman&c
web
with
STAR-­‐CITY:
Scenarios,
system
and
lessons
learned
in
Dublin
City.
In
Jour-­‐
nal
of
Web
Seman&cs.
Web
Seman&cs:
Science,
Services
and
Agents
on
the
World
Wide
Web
(JWS).
Vol
XX
(2014).
• Spyros
Kotoulas,
Vanessa
Lopez,
Raymond
Lloyd,
Marco
Luca
Sbodio,
Freddy
Lecue,
MarHn
Stephenson,
Elizabeth
M.
Daly,
Veli
Bicer,
Aris
Gkoulalas-­‐Divanis,
Giusy
Di
Lorenzo,
Anika
Schumann,
Pol
Mac
Aonghusa.
SPUD
-­‐
Seman&c
Pro-­‐
cessing
of
Urban
Data.
In
Journal
of
Web
Seman&cs.
Web
Seman&cs:
Science,
Services
and
Agents
on
the
World
Wide
Web
(JWS),
Vol
24
(2014).
• Freddy
Lecue,
Robert
Tucker,
Simone
Tallevi-­‐Diotallevi,
Rahul
Nair,
Yiannis
Gkoufas,
Giuseppe
Liguori,
Mauro
Borioni,
Alexandre
Rademaker
and
Luciano
Barbosa.
Seman&c
Traffic
Diagnosis
with
STAR-­‐CITY:
Architecture
and
Lessons
Learned
from
Deployment
in
Dublin,
Bologna,
Miami
and
Rio,
ISWC
2014,
Riva,
Italy,
October
2014
(Best
In
Use
Paper
Award)
• Joern
Ploennigs,
Anika
Schumann,
Freddy
Lecue.
AdapHng
SemanHc
Sensor
Networks
for
Smart
Building
Diagnosis.
In
Proceedings
of
the
13th
Interna&onal
Seman&c
Web
Conference
(ISWC
2014),
pages
308-­‐323,
October
19-­‐23,
2014,
Riva
del
Garda,
Italy.
Springer
2014
ISBN
978-­‐3-­‐319-­‐11914-­‐4.
(Best
In
Use
Paper
Award
Nominee)
• Jiewen
Wu,
Freddy
Lecue.
Towards
Consistency
Checking
over
Evolving
Ontologies.
Proceedings
of
the
23rd
ACM
Conference
on
Informa&on
and
Knowledge
Management,
CIKM
2014,
Shanghai,
China,
November
3-­‐7,
2014.
• Anika
Schumann,
Freddy
Lecue,
Joern
Ploennigs.
Exploi&ng
the
Seman&c
Web
for
Systems
Diagnosis.
In
Proceedings
of
the
Twenty-­‐First
European
Conference
on
Ar&
cial
Intelligence
(ECAI
2014),
pages
??-­‐??,
August
18-­‐22,
2014,
Prague,
Czech
Republic.
IOS
Press
2014.

45. LD4SC-­‐15
References
(2)
• Joern
Ploennigs,
Anika
Schumann,
Freddy
Lecue.
Extending
Seman&c
Sensor
Networks
for
Automa&cally
Tackling
Smart
Building
Problems.
In
Proceedings
of
the
Twenty-­‐First
European
Conference
on
Ar&
cial
Intelligence
(ECAI
2014),
pages
??-­‐??,
August
18-­‐22,
2014,
Prague,
Czech
Republic.
IOS
Press
2014.
• Freddy
Lécué:
Towards
Scalable
Explora&on
of
Diagnoses
in
an
Ontology
Stream.
AAAI
2014:
87-­‐93
• Freddy
Lécué,
Robert
Tucker,
Veli
Bicer,
Pierpaolo
Tommasi,
Simone
Tallevi-­‐Diotallevi,
Marco
Luca
Sbodio:
Predic&ng
Severity
of
Road
Traffic
Conges&on
Using
Seman&c
Web
Technologies.
ESWC
2014:
611-­‐627
(Best
In
Use
Paper
Award)
• Freddy
Lécué,
Simone
Tallevi-­‐Diotallevi,
Jer
Hayes,
Robert
Tucker,
Veli
Bicer,
Marco
Luca
Sbodio,
Pierpaolo
Tommasi:
STAR-­‐CITY:
seman&c
traffic
analy&cs
and
reasoning
for
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46. LD4SC-­‐15
Ques&ons
Thank you!
Credits:
Pol
Mac
Aonghusa,
Luciano
Barbosa,
Veli
Bicer,
Antonio
Corradi,
Elizabeth
Daly,
Luca
Foschini,
Yiannis
Gkoufas,
Jer
Hayes,
Pascal
Hitzler,
Vanessa
Lopez,
,
Raghava
Mutharaju,
Rahul
Nair,
Jeff
Z.
Pan,
Joern
Ploennigs,
Alexandre
Rademaker,
Marco
Luca
Sbodio,
Anika
Schumann,
Mar&n
Stephenson,
Simone
Tallevi-­‐Diotallevi,
Pierpaolo
Tommasi,
Robert
Tucker,
Yuan
Ren,
Jiewen
Wu