INTRODUCTION TO DATA FUSION

INTRODUCTION TO DATA FUSION METHODS

• Stage based methods.

• Feature level-based.

• Semantic meaning-based data fusion methods

LOCATION DATA FUSION : SIDE EFFECT

• Data fusion enables a huge number of applications

• Privacy risks for individual data

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 dataDetecting and describing events happening in

urban areas by analysing spatio – temporal dataRiferimento all’articolo

The dataset

The dataset: spatio-temporal aggregation

Spatial Aggregation

Temporal aggregation

STATISTICAL MODELLING

OUTLIER DETECTION

METHODMedian 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]

GROUNDTRUTH DATASET

Football matches

Fairs

Protests

Other events

Events happeing in the period of time the data covers

MEASURING PRECISION AND RECALL OF THE SYSTEM

True positives (tp)

False positives (fp)

False negatives (fn)

Precision = tp / (tp + fp)Recall = tp / (tp + fn)

PRECISION – RECALL OF EVENT DETECTION SYSTEM

Precision – Recall Milano vs Trentino SMS-Call

Precision – Recall Milano vs Trentino SMS-Call

Precision – Recall Milano vs Trentino SMS-Call

IMPROVING EVENT DETECTION RESULTS BY DATA FUSIONBy 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.

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

CONFRONTING THE RESULTS WITH OTHER WORKS ON EVENT DETECTION

• Two other similar works

• Using much more sophisticated algorithms

• Comparable results

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.

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.)

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.