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

IBM Research and Development - Ireland

Managing the Information of a City
Spyros Kotoulas

© 2010 IBM Corporation


IBM Research Worldwide
Smarter Cities
Risk Analytics
Hybrid Computing
Exascale

Dublin
China
Zurich
Almaden

Watson

Haifa

Tokyo
India

Austin

Brazil

Melbourne



The Technology Centre
Smarter Cities Smarter Cities Technology Centre is merging

Collaborative Research & Smarter Cities opportunities
Driving New Economic Models

Predictive Modelling

Significant Collaborative R&D

Forecasting

Skills Development & Growth

Simulation

Intelligent

Competitive Advantage

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

Instrumented

Seed Projects
Real World Insight | Data Sets | Devices

City Fabric

Energy

Movement

Integrated Cross Domain Solutions

Water

Dublin Test Bed

Interconnected

Solutions that Sustain Economic Development

Optimization

Smart City Solutions

Intelligent Urban and Environmental Analytics and Systems


Many Visions of what a Smarter City might be

A “mission control” for infrastructure

A totally “wired” city

A showcase for urban planning concepts

A self-sufficient, sustainable eco-city



But we know they’ll intensively leverage ICT technologies
Telecommunications
- Fixed and mobile operators
- Media Broadcasters

Intelligent Transportation Systems
- Integrated Fare Management
- Road Usage Charging
- Traffic Information Management

Public Safety
- Surveillance System
- Emergency Management Integration
- Micro-Weather Forecasting

Energy Management
- Network Monitoring & Stability
- Smart Grid – Demand Management
- Intelligent Building Management
- Automated Meter Management

Water Management
- Water purity monitoring
- Water use optimization
- Waste water treatment
optimization

Environmental Management
- City-wide Measurements
- KPI’s
- CO2 Management
- Scorecards
- Reporting



How can we help cities achieve their aspirations?
1. Data assimilation
–
–
–

1.

Modelling human demand
–
–

1.

Data diversity, heterogeneity
Data accuracy, sparsity
Data volume

Understand how people use the city
infrastructure
Infer demand patterns

Operations & Planning
–

Factor in uncertainty



Data assimilation
• What kind of data
• What does it look like
• Data to Information
• Organizing data


4 V’s of Big Data


Volume

Velocity

Variety

Veracity



The multiple faces of Scalability



City of Data and Information: Many Areas
• Large, open and continuous data environment from heterogeneous domains:

Energy Management

City Management

Transportation

Water Management

and even more…
Supply Chain

Region

Food System

HealthCare



What about Data in Smarter Cities Context?
• What is all about? Data
– Real life,
– and Continuous

Streams



– Real life,
– and Continuous
Streams
 But also
– Heterogeneous,
– Imprecision,
– Incompleteness,
– Implicitness,
– Inconsistency,
– and more …
Uncertainty
– e.g., Private



– Real life,
– and Continuous
Streams
 But also
– Heterogeneous,
– Imprecision,
– Incompleteness,
– Implicitness,
– Inconsistency,
– and more …
Uncertainty
– e.g., Private
 So what about:
– Information?
– Knowledge?
– Querying?
– Reasoning?

Insight


Some Traffic-related Data Sets from Dublin

 Big data

 Not all open yet,

 Heterogeneous data

 Not linked yet

 Static, Continuous data

© 2012 IBM
 NoisyCorporation (inconsistent, imprecise)
data


How do you organize the information of a city?



City Data Trends
Activity
Aggregation
& Efforts to
create linkage
based on
Semantic Web

Content
Factual &
Static

>350 ‘Open
City Data
Catalogs’
(data.gov)

1993, SEC
Online

....

>25 Billion
Triples on
Linked Data
Cloud

2004, USG
announces eGov 2.0

Ecosystem
increasingly
focused on
long-term
sustainability

Innovation
based on
Collaboration
& Social
Innovation

Publicdata.eu –
LOD2 for
Citizen study
due 2014

35 Cities in
Open Data
Hackday,
12/2010

Content

Structure

Innovation

2009,
Data.gov.uk
Data.gov (US)

2010,
Amazon,
Google & MSoft


Time
2011+, Gov 3.0
City as an Enterprise


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



Planning Levels

Decision aggregation

Design & long-term
planning
Tactical
planning
Operations
planning

Operations
scheduling
Real-time
control

Real-time

Hours

Days

Weeks

Time horizon

Months

Years



Examples of Decisions

Plant & network design
(e.g. valve placement),
capacity expansion

Production,
maintenance plans
(e.g. leak detection)
Pump
scheduling
Equipment
set points

Reservoir
targets

Design & longterm
planning

Tactical
planning

Operations
planning

Operations
scheduling

Real-time
control

Real-time

Hours

Days

Weeks

Time horizon

Months

Years



Impact of Uncertainty

Plant & network design
(e.g. valve placement),
capacity expansion

Production,
maintenance plans
(e.g. leak detection)
Pump
scheduling
Equipment
set points

Reservoir
targets

Tactical
planning

Operations
planning

Design & longterm
planning
Population growth

Long-term demand patterns

Operations
scheduling
Energy costs, demand

Real-time
control

Rainfall, renewable energy sources

Real-time

Hours

Days

Weeks

Time horizon

Months

Years


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

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