1. CityPulse: Real-Time IoT Stream
Processing and Large-scale Data
Analytics for Smart City Applications
Pramod Anantharam and Amit Sheth
(in collaboration with Payam Barnaghi, University of Surrey)
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio, USA
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2. Relevance to CityPulse
Activity 3.3: Data Aggregation and Abstraction (Data Fusion)
(Month 7 – Month 24)
Activity 3.4: Event Detection for Urban Data Streams
(Month 19 – Month 30)
Activity 5.1: Real-Time Adaptive Urban Reasoning
(Month 4– Month 24)
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3. Activity 3.3: Data Aggregation and Abstraction (Data Fusion)
(Month 7 – Month 24)
Activity 3.4: Event Detection for Urban Data Streams
(Month 19 – Month 30)
Activity 5.1: Real-Time Adaptive Urban Reasoning
(Month 4– Month 24)
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4. A Semantic Approach to
Machine Perception
Making sense of sensor data with
Slides 9 to 23 borrowed from: Cory Henson, Researcher, Kno.e.sis
http://www.slideshare.net/andrewhenson/a-semanticsbased-approach-to-machine-perception
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5. Primary challenge is to bridge the gap between
data and knowledge
KNOWLEDGE
situation awareness
useful
for decision making
DATA
sensor
observations
7

6. mo
re
Levels of Abstraction
Interpreted data
Intellego
(abductive)
[in OWL]
e.g., diagnosis
2
Interpreted data
(deductive)
[in OWL] SSN
Ontology
e.g., threshold
…
3
…
Elevated
Blood
Pressure
…
1
Annotated Data
[in RDF]
e.g., label
Systolic blood pressure of 150 mmHg
les
s
us
efu
l…
us
efu
l
Hyperthyroidism
0
“150”
Raw Data
[in TEXT]
e.g., number
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7. Ontology of Perception
Low-level observed properties suggest
explanatory hypotheses through abduction
Explanation
Explanation
Observed
Propertie
s
Perceived
Features
Background knowledge
on the Web
Focus
Focus
9
An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology,

8. Ontology of Percetion
Explanation
Explanation
Observed
Propertie
s
Perceived
Features
Background knowledge
on the Web
Focus
Focus
Hypotheses imply the informational value
of future observations through deduction
10
An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology,

9. Semantics of Explanation
Abduction – or, inference to the best EXPLANATION
Task
•Given background knowledge of the environment (SIGMA), and
•given a set of sensor observation data (RHO),
•find a consistent explanation of the situation (DELTA)
Σ
Background
knowledge
∪∆
Features
(objects/events
)
in the world
ρ
Sensor
observation
data
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10. Semantics of Explanation
Σ
Background knowledge is represented as a causal network between
features (objects or events) in the world and the sensor observations
they give rise to.
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11. Semantic Perception on Resource
Constrained Devices
Off-the-shelf OWL-DL reasoners
are too resource intensive in terms
of both memory and time
•Runs out of resources with
background knowledge >> 20
nodes
•Asymptotic complexity: O(n3)
O(n3)3)<<xx<<O(n4)4)
O(n
O(n
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12. Relevance to CityPulse
Activity 3.3: Data Aggregation and Abstraction (Data Fusion)
(Month 7 – Month 24)
Activity 3.4: Event Detection for Urban Data Streams
(Month 19 – Month 30)
Activity 5.1: Real-Time Adaptive Urban Reasoning
(Month 4– Month 24)
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13. A Historical Perspective on Cities and its Inhabitants
A Historical Perspective on Cities and its Inhabitants
“kings, emperors and other rulers benefited from being on the
“kings, emperors and other rulers benefited from being on the
front lines with their people when it came to making
front lines with their people when it came to making
decisions.”11
decisions.”
Disguised as a commoner,
Disguised as a commoner,
Qianlong visited cities to
Qianlong visited cities to
understand a common man’s life
understand a common man’s life
This is popularly known as
This is popularly known as
“Management by Walking Around”
“Management by Walking Around”
since the 1980’s
since the 1980’s
Qianlong Emperor (8 October 1735 – 9 February 1796)
Qing Dynasty (1644–1912)
http://gicoaches.com/what-we-can-learn-from-kings-of-the-past-who-disguised-themselves-as-ordinary-men/
http://en.wikipedia.org/wiki/Qianlong_Emperor
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14. A Modern Perspective on Cities and its Inhabitants
A Modern Perspective on Cities and its Inhabitants
City authorities, government and other humanitarian agencies
City authorities, government and other humanitarian agencies
are benefited from being on the front lines with their people
are benefited from being on the front lines with their people
when it comes to making decisions.
when it comes to making decisions.
We want to be connected to
We want to be connected to
citizens to understand and
citizens to understand and
prioritize decisions
prioritize decisions

15. Pulse of a City (CityPulse)
Pulse of a City (CityPulse)
Public Safety
Urban planning Gov. & agency
admin.
Energy &
water
Environmental
Transportation
Image credit: http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/index.html
Social Programs
Healthcare
Education

16. What are People Talking About City Infrastructure on
What are People Talking About City Infrastructure on
Twitter?
Twitter?

17. Research Questions
Research Questions
− What are people talking about city infrastructure
on twitter?
− How do we extract city infrastructure related
events from twitter?
− How can we leverage event and location
knowledge bases for event extraction?
− How well can we extract city events?

18. Some Challenges in Extracting Events from Tweets
Some Challenges in Extracting Events from Tweets
− No well accepted definition of ‘events related to a
city’
− Tweets are short (140 characters) and its
informal nature make it hard to analyze
− Entity, location, time, and type of an event
− Multiple reports of the same event and sparse
report of some events (biased sample)
− Numbers don’t necessarily indicate intensity
− Validation of the solution is hard due to the open
domain nature of the problem

19. Mu
lt i
An Face
al y t ed
s is
ntic
a
Sem ation
cial plic
So Ap
Web
http://twitris.knoesis.org/
Real time
Insights of Im
portant
Events includ
i ng
disaster respo
nse
coordination
http://usatoday30.usatoday.com/news/politics/twitter-election-meter
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20. Twitris: Analysis by Location
Twitris: Analysis by Location
How People from
How People from
Different parts of the
Different parts of the
world talked about US
world talked about US
Election
Election
Images and
Images and
Videos Related to
Videos Related to
US Election
US Election
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21. Twitris: Impact of Background Knowledge
Twitris: Impact of Background Knowledge
The Dead People
The Dead People
mentioned in the
mentioned in the
event OWC
event OWC
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22. What is Smart Data in the context of
What is Smart Data in the context of
Disaster Management
Disaster Management
ACTIONABLE: Timely
ACTIONABLE: Timely
delivery of right
delivery of right
resources and
resources and
information to the right
information to the right
people at right location!
people at right location!
Because everyone wants to Help, but DON’T KNOW HOW!
Because everyone wants to Help, but DON’T KNOW HOW!
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23. Disaster Response Coordination Framework
Disaster Response Coordination Framework
Source: Purohit et. al 2013, Information Filtering and Management Model for Disaster
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24. Activity 3.3: Data Aggregation and Abstraction (Data Fusion)
(Month 7 – Month 24)
(UNIS, ERIC, SIE, UASO, WSU)
Activity 3.4: Event Detection for Urban Data Streams
(Month 19 – Month 30)
(SIE, UNIS, ERIC, WSU)
Activity 5.1: Real-Time Adaptive Urban Reasoning
(Month 4– Month 24)
(NUIG, UNIS, ERIC, SIE, WSU)
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25. Continuous Semantics
Continuous Semantics
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26. Dynamic Model Creation
Heliopolis is a
Heliopolis is a
suburb of
suburb of
Cairo.
Cairo.
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27. Dynamic Model Creation:
Events
“Both Ahmadinejad & Mousavi
“Both Ahmadinejad & Mousavi
declare victory in Iranian
declare victory in Iranian
Elections.”
Elections.”
June 12 2009
“situation in tehran University is
“situation in tehran University is
so worrisome. police have
so worrisome. police have
attacked to girls dormitory
attacked to girls dormitory
#tehran #iranelection”
#tehran #iranelection”
June 13 2009
“Reports from Azadi Square - -44
“Reports from Azadi Square
people killed by police, people
people killed by police, people
killed police who shot. More
killed police who shot. More
shots being fired
shots being fired
#iranelections”
June 15 2009
#iranelections”
Key phrases
Ahmadinejad &
Ahmadinejad &
Tehran
Tehran
Azadi Square is
Azadi Square is
Mousavi area
Mousavi is
Universityarea
aUniversity isin
city square in
apoliticians in
city square
politicians in
University
University
Tehran
Tehran
Iran
Iran
Models
Example of how background knowledge help
understand situation described in the tweets,
while also updating knowledge model also
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28. Summarizing Continuous Semantics
Summarizing Continuous Semantics
Keeping the Background
Keeping the Background
Knowledge abreast with the
Knowledge abreast with the
changes of the event
changes of the event
Smartly learning and adapting data
Smartly learning and adapting data
acquisition (Temporally apt Big
acquisition (Temporally apt Big
Data, i.e. Fast Data)
Data, i.e. Fast Data)
In-turn providing temporally
In-turn providing temporally
relevant Smart Data through
relevant Smart Data through
analysis
analysis
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29. Thanks!
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