Event detection in urban context by using aggregated mobile activity as for example CDR data and social network data in this case geo-referenced Twitter data. The experiments show that the two datasets - CDR and social data - used, complement each other by providing better event detection results and event detscription.

1. INTRODUCTION TO DATA FUSION

2. INTRODUCTION TO DATA FUSION METHODS
• Stage based methods.
• Feature level-based.
• Semantic meaning-based data fusion methods

3. LOCATION DATA FUSION : SIDE EFFECT
• Data fusion enables a huge number of applications
• Privacy risks for individual data

4. DATA FUSION FOR EVENT DETECTION /
DESCRIPTION BY USING AGGREGATED CDR DATA
AND GEO-TAGGED SOCIAL NETWORK DATA
Detecting and describing events happening in urban
areas by analysing spatio – temporal data
Detecting and describing events happening in urban areas
by analysing spatio – temporal data
Riferimento all’articolo

6. The dataset

7. The dataset: spatio-temporal aggregation
Spatial Aggregation
Temporal aggregation

8. STATISTICAL MODELLING

9. OUTLIER
DETECTION
METHOD
Median method :
[LB,UB] = [Q50 – k*Q50, Q50 + k*Q50]
IQR method :
[LB,UB] = [Q25 – k*IQR, Q75 + k*IQR]
Q75 method :
[LB,UB] = [Q25 – k*Q25, Q25 + k*Q75]

10. GROUNDTRUTH
DATASET
 Football matches
 Fairs
 Protests
 Other events
Events happeing in the period of
time the data covers

11. MEASURING PRECISION
AND RECALL OF THE
SYSTEM
True positives (tp)
False positives (fp)
False negatives (fn)
Precision = tp / (tp + fp)
Recall = tp / (tp + fn)

12. PRECISION – RECALL OF EVENT DETECTION
SYSTEM

13. Precision – Recall Milano vs Trentino SMS-Call

14. Precision – Recall Milano vs Trentino SMS-Call

15. Precision – Recall Milano vs Trentino SMS-Call

16. IMPROVING EVENT DETECTION RESULTS BY DATA
FUSION
By combining the results from
the two datasets
• Improvement of precision – recall
performance of the method
• The improvement is limited in the
long run by the main dataset.
• The same improvement can be
observed also by joining the results
of the other datasets.

17. DATA FUSION FOR EVENT DESCRIPTION
By using the CDR the events
can be detected but not
described:
• By joining the results the data
can complement and enrich
each other.
• In this case the social dataset
can be used to describe
semantically the events

18. CONFRONTING THE RESULTS WITH OTHER WORKS ON
EVENT DETECTION
• Two other similar works
• Using much more sophisticated algorithms
• Comparable results

19. CHALLENGES
• One of the main challenges is the lack of common engineering standards for data fusion
systems. It has been one of the main impediments to integration and data fusion.
• As different methods of data fusion behave differently in different applications, it is not trivial
to choose the best method for a specific task.
• Challenges during the data fusion design phase. At which level of abstraction, reduction and
simplification the data should be fused ?
• The lack of a unified framework that could orient the process of data fusion towards a
“structured data fusion” vision.

20. CONCLUSIONS AND FUTURE WORK
• Information fusion as a an enabling process for novel applications
- Future work oriented towards the “structured data fusion” idea
• Privacy
- Assesment of variations of existing privacy preserving techniques (D.P.)

21. PUBLICATIONS
• Nicola Bicocchi, Alket Cecaj, Damiano Fontana, Marco Mamei, Andrea Sassi, Franco Zambonelli: “ Collective Awareness for
Human ICT Collaboration in Smart Cities”. IEEE WETICE International conference on state-of-the art research in enabling
technologies for collaboration 17-20 2013.
• Alket Cecaj, Marco Mamei, Nicola Bicocchi : “ Re-identification of Anonymized CDR datasets Using Social Network Data ”. IEEE
Percom International conference on Pervasive Computing and Communications. Budapest, Hungary 24-28, 2014.
• Cecaj Alket, Marco Mamei (2016) : “Data Fusion for City Life Event Detection” In: Journal of Ambient Intelligence and
Humanized Computing, pp 1– 15.
• Nicola Bicocchi, Alket Cecaj, Damiano Fontana, Marco Mamei, Andrea Sassi, Franco Zambonelli.(2014) “ Social Collective
Awareness in Socio-Technical Urban Superorganisms ”. Social Collective Intelligence Combining the Powers Of Humans and
Machines to Build a Smarter Society,Part III, Applications and Case studies, page 227.
• Cecaj, Alket, Marco Mamei, and Franco Zambonelli (2015). “Re-identification and Information Fusion Between Anonymized
CDR and Social Network Data”. In: Journal of Ambient Intelligence and Humanized Computing, pp. 1–14.