ESWC SS 2012 - Wednesday Keynote Spyros Kotoulas : Managing the Information of a City

© 2010 IBM Corporation

IBM Research and Development - Ireland


Spyros Kotoulas


Managing the Information of a City



China

Watson Almaden

Austin

Tokyo Haifa

Zurich

India

Dublin

Melbourne

Brazil

IBM Research Worldwide

Smarter Cities

Risk Analytics

Hybrid Computing

Exascale







The Smarter Cities Technology Centre is merging Collaborative Research & Smarter Cities opportunities

Instru

me

nte

d

Inte

rco

nn

ecte

d

Inte

llige

nt

Dublin

Test B

ed

Energy Movement Water

Seed Projects Real World Insight | Data Sets | Devices

Optimization

Predictive Modelling

Forecasting

Simulation

Solu

tions th

at S

usta

in E

conom

ic D

evelo

pm

en

t

Driving New Economic Models

Significant Collaborative R&D

Skills Development & Growth

Competitive Advantage

Collaboration and Access to Local, Regional & Worldwide Network SME’s | MNC’s | Universities | Public Sector | VC Community

Intelligent Urban and Environmental Analytics and Systems

Sm

art C

ity S

olu

tions

Integrated Cross Domain Solutions

City Fabric

Smarter Cities Technology Centre



A “mission control” for infrastructure A showcase for urban planning concepts

A totally “wired” city A self-sufficient, sustainable eco-city

Many Visions of what a Smarter City might be



Intelligent Transportation Systems

- Integrated Fare Management

- Road Usage Charging

- Traffic Information Management

Energy Management

- Network Monitoring & Stability

- Smart Grid – Demand Management

- Intelligent Building Management

- Automated Meter Management

Environmental Management

- City-wide Measurements

- KPI’s

- CO2 Management

- Scorecards

- Reporting

Water Management

- Water purity monitoring

- Water use optimization

- Waste water treatment

optimization

Public Safety

- Surveillance System

- Emergency Management Integration

- Micro-Weather Forecasting

Telecommunications

- Fixed and mobile operators

- Media Broadcasters

But we know they’ll intensively leverage ICT technologies



How can we help cities achieve their aspirations?

1. Data assimilation

– Data diversity, heterogeneity

– Data accuracy, sparsity

– Data volume

1. Modelling human demand

– Understand how people use the city infrastructure

– Infer demand patterns

1. Operations & Planning

– Factor in uncertainty



Data assimilation • What kind of data

• What does it look like

• Data to Information

• Organizing data



4 V

’s o

f B

ig D

ata

Volume Velocity

Variety Veracity



The multiple faces of Scalability



Transportation Water Management Energy Management City Management

Region Supply Chain Food System HealthCare

• Large, open and continuous data environment from heterogeneous domains:

and even more…

City of Data and Information: Many Areas



• What is all about? Data

– Real life,

– and Continuous

Streams

What about Data in Smarter Cities Context?




– Real life,

– and Continuous

Streams

Uncertainty

But also

– Heterogeneous,

– Imprecision,

– Incompleteness,

– Implicitness,

– Inconsistency,

– and more …

– e.g., Private





– Real life,

– and Continuous

Streams

Uncertainty Insight

So what about:

– Information?

– Knowledge?

– Querying?

– Reasoning?

But also

– Heterogeneous,

– Imprecision,

– Incompleteness,

– Implicitness,

– Inconsistency,

– and more …

– e.g., Private




Some Traffic-related Data Sets from Dublin

Big data

Heterogeneous data

Static, Continuous data

Not all open yet,

Not linked yet

Noisy data (inconsistent, imprecise)



How do you organize the information of a city?



City Data Trends

2009,

Data.gov.uk

Data.gov (US)

1993, SEC

Online

2004, USG

announces e-

Gov 2.0

Content

Factual &

Static

>350 ‘Open

City Data

Catalogs’

(data.gov)

2011+, Gov 3.0

City as an Enterprise ....

Activity

Time 2010,

Amazon,

Google & MSoft

Content

Structure

Innovation

Aggregation

& Efforts to

create linkage

based on

Semantic Web

>25 Billion

Triples on

Linked Data

Cloud

Innovation

based on

Collaboration

& Social

Innovation

35 Cities in

Open Data

Hackday,

12/2010

Ecosystem

increasingly

focused on

long-term

sustainability

Publicdata.eu –

LOD2 for

Citizen study

due 2014

http://www.google.com/imgres?imgurl=http://blog.davidjeade.com/wp-content/uploads/2010/01/Gov20.png&imgrefurl=http://blog.davidjeade.com/2010/02/on-government-2-0/&usg=__CzjE8qMZVmwkYOX5PSADib8d3hI=&h=376&w=839&sz=41&hl=en&start=13&zoom=1&tbnid=R8DZFuSG1ietdM:&tbnh=65&tbnw=144&ei=O5-2Tf7THcHo4Ab769zDDw&prev=/search?q=government+2.0&um=1&hl=en&sa=N&tbm=isch&um=1&itbs=1

http://www.facebook.com/govfresh

http://en.wikipedia.org/wiki/File:Open_gov.jpg

http://www.ibiblio.org/hhalpin/homepage/presentations/ms2007/rdf.png

http://aws.amazon.com/

http://www.google.com/publicdata/home

http://www.microsoft.com/industry/government/opengovdata/Default.aspx



Data processing lifecycle



Challenges

– Fitness-for-use. The users of the system are not data integration experts and not qualified to use industry data integration tools. Furthermore, they are not able to query data using structured query languages.

– Domain modeling. The domain of the information is very broad and open. As such, generating and mapping data to a single model is infeasible or too expensive.

– Global integration. Addressing the information needs for solving problems in an urban environment requires integration with an open set of external datasets. Furthermore, it is desirable that city data becomes easily consumable by other parties.

– Scale. The data in a city changes often (streams), is potentially very large and it is interlinked with an open set of external data.

• Traditional Data Integration methods cannot scale to 100’s datasets.



Urban Data Management Stack



It is not all about the Data, It is about the Information!!!



Our Ecosystem: The World

“The world is our now our lab!”



Data in a Human Context

Understand how people use the city's

infrastructure. Infer information

about:

Mobility (transportation mode)

Consumption (energy, water, waste)

Environmental impact (noise, pollution)

Potentials

Improve city’s services

Optimize planning

Minimizing operational costs

Create feedback loops with citizens to

reduce energy consumption and

environmental impact



Design & long-term

planning

Tactical

planning

Operations

planning

Time horizon

Real-time Hours Days Weeks Months Years

Decis

ion

ag

gre

gati

on

Operations

scheduling

Real-time

control

Planning Levels



Time horizon


Decis

ion

ag

gre

gati

on

Design & longterm

planning

Tactical

planning

Operations

planning

Operations

scheduling

Real-time

control

Plant & network design

(e.g. valve placement),

capacity expansion

Reservoir

targets Production,

maintenance plans

(e.g. leak detection)

Pump

scheduling

Equipment

set points

Examples of Decisions



Time horizon


Decis

ion

ag

gre

gati

on

Design & longterm

planning

Tactical

planning

Operations

planning

Operations

scheduling

Real-time

control

Reservoir

targets

Pump

scheduling

Equipment

set points

Population growth

Long-term demand patterns

Energy costs, demand

Rainfall, renewable energy sources

Production,

maintenance plans

(e.g. leak detection)

Plant & network design

(e.g. valve placement),

capacity expansion

Impact of Uncertainty



THANKS!

Acknowledgements

Lisa Amini, Pol Mac Aonghusa, Francesco Calabrese, Giusy di Lorenzo, Martin Stephenson, Vanessa Lopez, Freddy Lecue, Suzara van der Heeven, Olivier Verscheure, Marco Luca Sbodio, Raymond Lloyd

ESWC SS 2012 - Wednesday Keynote Spyros Kotoulas : Managing the Information of a City

Technology

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ibm corporation reasoning

heterogeneous data

data diversity

imprecise data

kind of data

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development irelandhow