Keynote at KMO-Kennisbeurs - Leuven - Oct. 2012

1. Big Data beyond Web Data
Tom Tourwé & Elena Tsiporkova
Software & ICT - Sirris
tom.tourwe@sirris.be elena.tsiporkova@sirris.be

2. Non-profit & industry-owned
collective centre of the technology industry

3. Web
Provide near-real-time recommendations for hyper-local
& hyper-personal content

4. Web
Provide near-real-time recommendations for hyper-local
& hyper-personal content
➙ implicit ratings for time-bound events require
customized colloborative filtering algorithms

5. Web
Provide near-real-time recommendations for hyper-local
& hyper-personal content
➙ implicit ratings for time-bound events require
customized colloborative filtering algorithms

6. Web
Provide near-real-time recommendations for hyper-local
& hyper-personal content
➙ implicit ratings for time-bound events require similar
customized colloborative filtering algorithms

7. Web
Provide near-real-time recommendations for hyper-local
& hyper-personal content
➙ implicit ratings for time-bound events require similar
customized colloborative filtering algorithms

8. Web
Provide near-real-time recommendations for hyper-local
& hyper-personal content
➙ implicit ratings for time-bound events require similar
customized colloborative filtering algorithms

9. Web
Provide near-real-time recommendations for hyper-local
& hyper-personal content
➙ implicit ratings for time-bound events require similar
customized colloborative filtering algorithms
➙ user profiles and multi-criteria filtering allow for ranking
recommendations

10. Web
Provide near-real-time recommendations for hyper-local
& hyper-personal content
➙ implicit ratings for time-bound events require similar
customized colloborative filtering algorithms
➙ user profiles and multi-criteria filtering allow for ranking
recommendations

11. Web
Provide near-real-time recommendations for hyper-local
& hyper-personal content
➙ implicit ratings for time-bound events require similar
customized colloborative filtering algorithms
➙ user profiles and multi-criteria filtering allow for ranking
recommendations

12. Emergency
Turn an overwhelming amount of data into user- and
context-sensitive information to increase situational
awareness and improve decision making

13. Emergency
Turn an overwhelming amount of data into user- and
context-sensitive information to increase situational
awareness and improve decision making
➙ capture, model and fuse raw data about users &
their environment from heterogeneous data sources

14. Emergency
Turn an overwhelming amount of data into user- and
context-sensitive information to increase situational
awareness and improve decision making
➙ capture, model and fuse raw data about users &
their environment from heterogeneous data sources
➙ analyse, filter & pro-actively deliver relevant
information using appropriate modalities

15. Security
Border guards need to strike the right balance between security
and mobility

16. Security
Border guards need to strike the right balance between security
and mobility
➙ identify different traveller profiles with associated risk index

17. Security
Border guards need to strike the right balance between security
and mobility
➙ identify different traveller profiles with associated risk index
➙ forecast travel flow by observing trends and seasonality in
historical traffic information

18. Security
Border guards need to strike the right balance between security
and mobility
➙ identify different traveller profiles with associated risk index
➙ forecast travel flow by observing trends and seasonality in
historical traffic information
➙ detect border crossing anomalies in real-time

19. Energy
Reduce the maintenance costs of wind turbines & solar plants by
means of predictive maintenance

20. Energy
Reduce the maintenance costs of wind turbines & solar plants by
means of predictive maintenance
➙ real-time failure prediction & operation anomaly detection by
identifying patterns in historical data

21. Energy
Reduce the maintenance costs of wind turbines & solar plants by
means of predictive maintenance
➙ real-time failure prediction & operation anomaly detection by
identifying patterns in historical data
Energy demand forecasting for dynamic renewable energy
production

22. Energy
Reduce the maintenance costs of wind turbines & solar plants by
means of predictive maintenance
➙ real-time failure prediction & operation anomaly detection by
identifying patterns in historical data
Energy demand forecasting for dynamic renewable energy
production
➙ evolution & forecasting of energy production & consumption by
building predictive models from historical weather & usage data

23. Our services
Tom Tourwé & Elena Tsiporkova
Software & ICT - Sirris
tom.tourwe@sirris.be elena.tsiporkova@sirris.be
http://blog.sirris.be