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

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

  • IBM Research and Development - Ireland Managing the Information of a City Spyros Kotoulas IBM Research and Development - Ireland © 2010 IBM Corporation © 2011 IBM Corporation
  • IBM Research and Development - Ireland IBM Research Worldwide Smarter Cities Risk Analytics Hybrid Computing Exascale Dublin China Zurich Almaden Watson Haifa Tokyo India Austin Brazil Melbourne © 2012 IBM Corporation
  • IBM Research and Development - Ireland © 2012 IBM Corporation
  • IBM Research and Development - Ireland © 2012 IBM Corporation
  • IBM Research and Development - Ireland 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 © 2012 IBM Corporation Water Dublin Test Bed Interconnected Solutions that Sustain Economic Development Optimization Smart City Solutions Intelligent Urban and Environmental Analytics and Systems
  • IBM Research and Development - Ireland 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 © 2012 IBM Corporation
  • IBM Research and Development - Ireland 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 © 2012 IBM Corporation
  • IBM Research and Development - Ireland 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 © 2012 IBM Corporation
  • IBM Research and Development - Ireland Data assimilation • What kind of data • What does it look like • Data to Information • Organizing data © 2012 IBM Corporation
  • 4 V’s of Big Data IBM Research and Development - Ireland Volume Velocity Variety Veracity © 2012 IBM Corporation
  • IBM Research and Development - Ireland The multiple faces of Scalability © 2012 IBM Corporation
  • IBM Research and Development - Ireland 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 © 2012 IBM Corporation
  • IBM Research and Development - Ireland What about Data in Smarter Cities Context? • What is all about? Data – Real life, – and Continuous Streams © 2012 IBM Corporation
  • IBM Research and Development - Ireland What about Data in Smarter Cities Context? • What is all about? Data – Real life, – and Continuous Streams  But also – Heterogeneous, – Imprecision, – Incompleteness, – Implicitness, – Inconsistency, – and more … Uncertainty – e.g., Private © 2012 IBM Corporation
  • IBM Research and Development - Ireland What about Data in Smarter Cities Context? • What is all about? Data – Real life, – and Continuous Streams  But also – Heterogeneous, – Imprecision, – Incompleteness, – Implicitness, – Inconsistency, – and more … Uncertainty – e.g., Private  So what about: – Information? – Knowledge? – Querying? © 2012 IBM Corporation – Reasoning? Insight
  • IBM Research and Development - Ireland 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
  • IBM Research and Development - Ireland How do you organize the information of a city? © 2012 IBM Corporation
  • IBM Research and Development - Ireland 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 © 2012 IBM Corporation Time 2011+, Gov 3.0 City as an Enterprise
  • IBM Research and Development - Ireland Data processing lifecycle © 2012 IBM Corporation
  • IBM Research and Development - Ireland 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. © 2012 IBM Corporation
  • IBM Research and Development - Ireland Urban Data Management Stack © 2012 IBM Corporation
  • IBM Research and Development - Ireland It is not all about the Data, It is about the Information!!! © 2012 IBM Corporation
  • IBM Research and Development - Ireland Our Ecosystem: The World “The world is our now our lab!” © 2012 IBM Corporation
  • IBM Research and Development - Ireland 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 © 2012 IBM Corporation
  • IBM Research and Development - Ireland Planning Levels Decision aggregation Design & long-term planning Tactical planning Operations planning Operations scheduling Real-time control Real-time Hours Days Weeks Time horizon © 2012 IBM Corporation Months Years
  • IBM Research and Development - Ireland Decision aggregation 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 © 2012 IBM Corporation Months Years
  • IBM Research and Development - Ireland Decision aggregation 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 © 2012 IBM Corporation Months Years
  • IBM Research and Development - Ireland 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 © 2012 IBM Corporation