Data fusion for city live event detection
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Transcript of Data fusion for city live event detection
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INTRODUCTION TO DATA FUSION
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INTRODUCTION TO DATA FUSION METHODS
• Stage based methods.
• Feature level-based.
• Semantic meaning-based data fusion methods
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LOCATION DATA FUSION : SIDE EFFECT
• Data fusion enables a huge number of applications
• Privacy risks for individual data
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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
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The dataset
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The dataset: spatio-temporal aggregation
Spatial Aggregation
Temporal aggregation
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STATISTICAL MODELLING
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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]
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GROUNDTRUTH DATASET
Football matches
Fairs
Protests
Other events
Events happeing in the period of time the data covers
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MEASURING PRECISION AND RECALL OF THE SYSTEM
True positives (tp)
False positives (fp)
False negatives (fn)
Precision = tp / (tp + fp)Recall = tp / (tp + fn)
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PRECISION – RECALL OF EVENT DETECTION SYSTEM
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Precision – Recall Milano vs Trentino SMS-Call
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Precision – Recall Milano vs Trentino SMS-Call
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Precision – Recall Milano vs Trentino SMS-Call
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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.
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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
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CONFRONTING THE RESULTS WITH OTHER WORKS ON EVENT DETECTION
• Two other similar works
• Using much more sophisticated algorithms
• Comparable results
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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.
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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.)
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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.