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CityPulse: Real-Time IoT Stream
Processing and Large-scale Data
Analytics for Smart City Applications
2
CityPulse– a quick snapshot
− CityPulse aims to support the integration of dynamic data
sources and context-dependent on...
3
An Integrated Approach
4
CityPulse Consortium
Industrial
SIE (Austria,
Romania),
ERIC
SME AI,
Higher
Education
UNIS, NUIG,
UASO, WSU
City BR, AA
...
5
The main objective in CityPulse is:
− to develop, build and test a distributed framework for the
semantic discovery and ...
6
Processing steps and Life-cycle stages
Analytics
Toolbox
Context-aware
Decision
Support,
Visualisation
Knowledge-
based
...
7
The Key issues addressed
− Virtualisation: Semantic annotation of heterogeneous data
for automated discovery and knowled...
8
Overall Structure
WP1ProjectManagement
CityPulse Framework
WP6 Integration & Evaluation:Smart City Applications
WP5 Real...
Contact
Payam Barnaghi, University of Surrey, UK
Email: p.barnaghi@surrey.ac.uk
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EU FP7 CityPulse Project

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EU FP7 CityPulse Project (starting date: September 2013)

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EU FP7 CityPulse Project

  1. 1. 1 CityPulse: Real-Time IoT Stream Processing and Large-scale Data Analytics for Smart City Applications
  2. 2. 2 CityPulse– a quick snapshot − CityPulse aims to support the integration of dynamic data sources and context-dependent on-demand adaptations of processing chains during run-time. − CityPulse aims to bridge the gap between the application technologies on the IoT and real world data streams. − It will use Cyber-Physical and Social data and will employ big data analytics and intelligent methods to aggregate, interpret and extract meaningful knowledge and perceptions from large sets of heterogeneous data streams.
  3. 3. 3 An Integrated Approach
  4. 4. 4 CityPulse Consortium Industrial SIE (Austria, Romania), ERIC SME AI, Higher Education UNIS, NUIG, UASO, WSU City BR, AA Partners: Duration: 36 months
  5. 5. 5 The main objective in CityPulse is: − to develop, build and test a distributed framework for the semantic discovery and processing of large-scale real-time IoT and relevant social data streams for knowledge extraction in a city environment. − It will prototype and demonstrate its major concepts in a city environment and evaluate the results for exploitation towards future smart city delivery and development platform and testing products.
  6. 6. 6 Processing steps and Life-cycle stages Analytics Toolbox Context-aware Decision Support, Visualisation Knowledge- based Stream Processing Real-Time Monitoring& Testing Accuracy& Trust Modelling Semantic Integration On Demand Data Federation Open Reference Data Sets Real-Time IoT Information Extraction IoT Stream Processing Federation of Heterogenous Data Streams Design-Time Run-Time Testing Exposure APIs
  7. 7. 7 The Key issues addressed − Virtualisation: Semantic annotation of heterogeneous data for automated discovery and knowledge-based processing − Federation: On demand integration of heterogeneous Cyber-Physical-Social sources − Aggregation: Large-scale data analytics − Smart Adaptation: Real-Time interpretation and data analytics control − User centric decision support: Context aware customized IoT information extraction − Reliable Information Processing: Testing and monitoring accuracy and trust − Smart City Applications: Application programming interface for rapid prototyping
  8. 8. 8 Overall Structure WP1ProjectManagement CityPulse Framework WP6 Integration & Evaluation:Smart City Applications WP5 Real-Time IoT Intelligence WP4 Reliable Information Processing WP3 Large-Scale IoT Stream Processing WP2 Requirements and Smart City Framework Configuration Semantic IOT Stream Monitoring & Testing WP7DisseminationandExploitation
  9. 9. Contact Payam Barnaghi, University of Surrey, UK Email: p.barnaghi@surrey.ac.uk 9

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