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Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
Mining in the Middle of the City: The
needs of Big Data for...
Problem statement
• Smart Cities are presenting new challenges for Big Data.
• The emerging amount of data needs to be pro...
Big Data / Smart Cities ecosystem
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
SmartSantander Testbed
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
SmartSantander Testbed
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
SmartSantander Testbed
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
SmartSantander Testbed (Traffic)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
SmartSantander Testbed (Temperature)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
Data Fusion
• Temperature area totally insolated from the traffic
monitoring zones.
• Not required fine-grain analysis of ...
Traffic (without data fusion)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
Traffic vs Temperature in April (with data fusion)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
Traffic vs Temperature in July (with data fusion)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
57,4 % Li...
Traffic vs Temperature in December (with data fusion)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
Modelling of Temp / Traffic in April
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
Modelling of Temp / Traffic in July
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
Modelling of Temp / Traffic in December
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
KNIME workflow
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
KNIME workflow for visualization
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland
Conclusions
• Data Fusion is required for Smart Cities analysis.
• Correlation of non-aggregated data is non-feasible.
• D...
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Mining in the Middle of the City: The needs of Big Data for Smart Cities

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Smart Cities are presenting new challenges for Big Data. The emerging amount of data needs to be processed to make feasible its analysis (data fusion to avoid noise and apparently random behaviors, correlation in order to see hidden behaviors, focused on insight and integration into business models, needs from the market to define the questions that are expecting to answer for the Smart Cities).

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Mining in the Middle of the City: The needs of Big Data for Smart Cities

  1. 1. Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland Mining in the Middle of the City: The needs of Big Data for Smart Cities A Real Experience in the SmartSantander Testbed Antonio J. Jara, Dominique Genoud, Yann Bocchi HES-SO, Switzerland Palo Alto, USA 19th June 2014
  2. 2. Problem statement • Smart Cities are presenting new challenges for Big Data. • The emerging amount of data needs to be processed to make feasible its analysis. • First step, data fusion to avoid noise and apparently random behaviors. • Second step, correlation in order to see hidden behaviors. • Next steps more focused on insight, and integration into business models. • Needs from the market to define the questions that are expecting to answer for the Smart Cities. Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  3. 3. Big Data / Smart Cities ecosystem Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  4. 4. SmartSantander Testbed Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  5. 5. SmartSantander Testbed Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  6. 6. SmartSantander Testbed Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  7. 7. SmartSantander Testbed (Traffic) Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  8. 8. SmartSantander Testbed (Temperature) Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  9. 9. Data Fusion • Temperature area totally insolated from the traffic monitoring zones. • Not required fine-grain analysis of temperature, since not influenced by traffic. • Traffic sensors needs to be aggregated by highways and lanes. • Data fusion feasible due to the nature of the problem. • This simplify and makes feasible the correlation between Temperature and Traffic Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  10. 10. Traffic (without data fusion) Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  11. 11. Traffic vs Temperature in April (with data fusion) Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  12. 12. Traffic vs Temperature in July (with data fusion) Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland 57,4 % Line Correlated
  13. 13. Traffic vs Temperature in December (with data fusion) Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  14. 14. Modelling of Temp / Traffic in April Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  15. 15. Modelling of Temp / Traffic in July Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  16. 16. Modelling of Temp / Traffic in December Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  17. 17. KNIME workflow Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  18. 18. KNIME workflow for visualization Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland
  19. 19. Conclusions • Data Fusion is required for Smart Cities analysis. • Correlation of non-aggregated data is non-feasible. • Data Fusion has demonstrated the similarity among the temperature and traffic trends. • KNIME offers an intuitive tool to works with Data. • In addition, it offers correlation tools, characterization tools, and classification tools from Weka and R, and finally with Hadoop. • Current works focused on human dynamics analysis over the data; Burst vs Poisson. • An extended / advanced version of this work avaiable under request to jara@ieee.org Dr. Antonio J. Jara – jara@ieee.org HES-SO//Valais Switzerland

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