ESWC SS 2012 - Wednesday Keynote Spyros Kotoulas : Managing the Information of a City
-
Upload
eswcsummerschool -
Category
Technology
-
view
119 -
download
1
description
Transcript of ESWC SS 2012 - Wednesday Keynote Spyros Kotoulas : Managing the Information of a City
© 2010 IBM Corporation
IBM Research and Development - Ireland
© 2011 IBM Corporation
Spyros Kotoulas
IBM Research and Development - Ireland
Managing the Information of a City
IBM Research and Development - Ireland
© 2012 IBM Corporation
China
Watson Almaden
Austin
Tokyo Haifa
Zurich
India
Dublin
Melbourne
Brazil
IBM Research Worldwide
Smarter Cities
Risk Analytics
Hybrid Computing
Exascale
IBM Research and Development - Ireland
© 2012 IBM Corporation
IBM Research and Development - Ireland
© 2012 IBM Corporation
IBM Research and Development - Ireland
© 2012 IBM Corporation
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
Data assimilation • What kind of data
• What does it look like
• Data to Information
• Organizing data
IBM Research and Development - Ireland
© 2012 IBM Corporation
4 V
’s o
f B
ig D
ata
Volume Velocity
Variety Veracity
IBM Research and Development - Ireland
© 2012 IBM Corporation
The multiple faces of Scalability
IBM Research and Development - Ireland
© 2012 IBM Corporation
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
• What is all about? Data
– Real life,
– and Continuous
Streams
What about Data in Smarter Cities Context?
IBM Research and Development - Ireland
© 2012 IBM Corporation
• What is all about? Data
– Real life,
– and Continuous
Streams
Uncertainty
But also
– Heterogeneous,
– Imprecision,
– Incompleteness,
– Implicitness,
– Inconsistency,
– and more …
– e.g., Private
What about Data in Smarter Cities Context?
IBM Research and Development - Ireland
© 2012 IBM Corporation
• What is all about? Data
– 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
What about Data in Smarter Cities Context?
IBM Research and Development - Ireland
© 2012 IBM Corporation
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)
IBM Research and Development - Ireland
© 2012 IBM Corporation
How do you organize the information of a city?
IBM Research and Development - Ireland
© 2012 IBM Corporation
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
Data processing lifecycle
IBM Research and Development - Ireland
© 2012 IBM Corporation
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.
IBM Research and Development - Ireland
© 2012 IBM Corporation
Urban Data Management Stack
IBM Research and Development - Ireland
© 2012 IBM Corporation
It is not all about the Data, It is about the Information!!!
IBM Research and Development - Ireland
© 2012 IBM Corporation
Our Ecosystem: The World
“The world is our now our lab!”
IBM Research and Development - Ireland
© 2012 IBM Corporation
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
Time horizon
Real-time Hours Days Weeks Months Years
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
Time horizon
Real-time Hours Days Weeks Months Years
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
IBM Research and Development - Ireland
© 2012 IBM Corporation
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