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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 ComputingWright State University, Dayton, Ohio, USA

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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)

Relevance to CityPulse

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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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Making sense of sensor data with

Slides 9 to 23 borrowed from: Cory Henson, Researcher, Kno.e.sishttp://www.slideshare.net/andrewhenson/a-semanticsbased-approach-to-machine-perception

A Semantic Approach to Machine Perception

DATAsensor

observations

KNOWLEDGEsituation awareness

usefulfor decision making

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Primary challenge is to bridge the gap between data and knowledge

SSNOntolog

y

2 Interpreted data(deductive)[in OWL]

e.g., threshold

1 Annotated Data[in RDF]e.g., label

0 Raw Data[in TEXT]

e.g., number

3 Interpreted data (abductive)[in OWL]

e.g., diagnosis

Intellego

“150”

Systolic blood pressure of 150 mmHg

ElevatedBlood

Pressure

Hyperthyroidism

less

use

ful …

…

more

use

ful

……

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Levels of Abstraction

Observed

Properties

Perceived

Features

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Background knowledgeon the Web

Low-level observed properties suggest explanatory hypotheses through abduction

ExplanationExplanation

FocusFocus

An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology, 2011)

Ontology of Perception

Observed

Properties

Perceived

Features

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Background knowledgeon the Web

Hypotheses imply the informational value of future observations through deduction

ExplanationExplanation

FocusFocus

An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology, 2011)

Ontology of Percetion

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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)

Backgroun

dknowledge

Features (objects/events)

in the world

Sensor observation

data

Semantics of Explanation

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Background knowledge is represented as a causal network between features (objects or events) in the world and the sensor

observations they give rise to.

Semantics of Explanation

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)

13O(n3) < x < O(n4)O(n3) < x < O(n4)

Semantic Perception on Resource Constrained Devices

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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)

A Historical Perspective on Cities and its InhabitantsA Historical Perspective on Cities and its Inhabitants

“kings, emperors and other rulers benefited from being on the front lines with their people when it came to making

decisions.”1

“kings, emperors and other rulers benefited from being on the front lines with their people when it came to making

decisions.”1

1http://gicoaches.com/what-we-can-learn-from-kings-of-the-past-who-disguised-themselves-as-ordinary-men/ http://en.wikipedia.org/wiki/Qianlong_Emperor

Qianlong Emperor (8 October 1735 – 9 February 1796)Qing Dynasty (1644–1912)

Disguised as a commoner, Qianlong visited cities to

understand a common man’s life

Disguised as a commoner, Qianlong visited cities to

understand a common man’s life

This is popularly known as “Management by Walking Around”

since the 1980’s

This is popularly known as “Management by Walking Around”

since the 1980’s

A Modern Perspective on Cities and its InhabitantsA Modern Perspective on Cities and its Inhabitants

City authorities, government and other humanitarian agencies are benefited from being on the front lines with

their people when it comes to making decisions.

City authorities, government and other humanitarian agencies are benefited from being on the front lines with

their people when it comes to making decisions.

We want to be connected to citizens to understand and

prioritize decisions

We want to be connected to citizens to understand and

prioritize decisions

Image credit: http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/index.html

Public Safety Urban planning Gov. & agency admin.

Energy &water

Environmental Transportation Social Programs Healthcare Education

Pulse of a City (CityPulse)Pulse of a City (CityPulse)

What are People Talking About City Infrastructure on Twitter?

What are People Talking About City Infrastructure on Twitter?

− 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?

Research QuestionsResearch Questions

Some Challenges in Extracting Events from TweetsSome 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

Social Semantic

Web ApplicationSocial Semantic

Web Application

Real timeReal time

Multi Faceted Analysis

Multi Faceted Analysis

Insights of Important Events including disaster response

coordination

Insights of Important Events including disaster response

coordination

21http://usatoday30.usatoday.com/news/politics/twitter-election-meter

http://twitris.knoesis.org/

How People from Different parts of the world talked about US

Election

How People from Different parts of the world talked about US

Election

Images and Videos Related to

US Election

Images and Videos Related to

US Election

Twitris: Analysis by Location Twitris: Analysis by Location

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The Dead People mentioned in the

event OWC

The Dead People mentioned in the

event OWC

Twitris: Impact of Background Knowledge Twitris: Impact of Background Knowledge

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What is Smart Data in the context of Disaster Management

What is Smart Data in the context of Disaster Management

ACTIONABLE: Timely delivery of right resources and

information to the right people at right location!

ACTIONABLE: Timely delivery of right resources and

information to the right people at right location!

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Because everyone wants to Help, but DON’T KNOW HOW!

Because everyone wants to Help, but DON’T KNOW HOW!

Source: Purohit et. al 2013, Information Filtering and Management Model for Disaster Response Coordination

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Disaster Response Coordination FrameworkDisaster Response Coordination Framework

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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)

Continuous Semantics Continuous Semantics

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Heliopolis is a suburb of

Cairo.

Heliopolis is a suburb of

Cairo.

Dynamic Model Creation

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Even

ts

“Both Ahmadinejad & Mousavi declare victory in

Iranian Elections.”

“Both Ahmadinejad & Mousavi declare victory in

Iranian Elections.”

“situation in tehran University is so worrisome.

police have attacked to girls dormitory #tehran

#iranelection”

“situation in tehran University is so worrisome.

police have attacked to girls dormitory #tehran

#iranelection”

“Reports from Azadi Square - 4 people killed by police, people killed police

who shot. More shots being fired #iranelections”

“Reports from Azadi Square - 4 people killed by police, people killed police

who shot. More shots being fired #iranelections”June 12 2009 June 13 2009 June 15 2009

Key

ph

rases

Mod

el

s

Ahmadinejad & Mousavi

are politicians in

Iran

Ahmadinejad & Mousavi

are politicians in

Iran

Tehran University is a University

in Iran

Tehran University is a University

in Iran

Azadi Square is a city

square in Tehran

Azadi Square is a city

square in Tehran

Dynamic Model Creation:

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Example of how background knowledge help understand situation described in

the tweets, while also updating knowledge model also

Summarizing Continuous SemanticsSummarizing Continuous Semantics

Keeping the Background Keeping the Background Knowledge abreast with the Knowledge abreast with the

changes of the eventchanges of the event

Keeping the Background Keeping the Background Knowledge abreast with the Knowledge abreast with the

changes of the eventchanges 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)

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

In-turn providing temporally In-turn providing temporally relevant Smart Data through relevant Smart Data through

analysis analysis

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Thanks!