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).

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. 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. Big Data / Smart Cities ecosystem
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

4. SmartSantander Testbed
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

5. SmartSantander Testbed
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

6. SmartSantander Testbed
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

7. SmartSantander Testbed (Traffic)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

8. SmartSantander Testbed (Temperature)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

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. Traffic (without data fusion)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

11. Traffic vs Temperature in April (with data fusion)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

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. Traffic vs Temperature in December (with data fusion)
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

14. Modelling of Temp / Traffic in April
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

15. Modelling of Temp / Traffic in July
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

16. Modelling of Temp / Traffic in December
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

17. KNIME workflow
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

18. KNIME workflow for visualization
Dr. Antonio J. Jara – jara@ieee.org
HES-SO//Valais Switzerland

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