Listening to the pulse of our cities fusing Social Media Streams and Call Data Records
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Transcript of Listening to the pulse of our cities fusing Social Media Streams and Call Data Records
Listening to the pulse of our cities fusing Social Media Streams and Call Data RecordsEmanuele Della [email protected]://emanueledellavalle.org
18th International Conference on Business Information Systems
24-26 June 2015, Poznań, Poland
http://emanueledellavalle.org - Emanuele Della Valle3
http://emanueledellavalle.org
Me
Assistant Professor at DEIBPolitecnico di Milano
Expert in semantic technologies and stream computing
Inventor of stream reasoning: an approach to master the velocity and variety dimension of Big Data
15 years experience in research and innovation projects
startupper: fluxedo.com
Emanuele Della Valle
http://emanueledellavalle.org
http://streamreasoning.org
http://fluxedo.com
http://emanueledellavalle.org - Emanuele Della Valle
Acknowledgements
Politecnico di Milano• DEIB
– What- Scientific direction- Semantic technologies- Stream Processing- Data science
– Who- Emanuele Della Valle- Marco Balduini
• Density Design Lab– What
- Visual analytics
– Who- Paolo Ciuccarelli- Matteo Azzi
Telecom Italia• SKIL Lab
– What- Big Data technology- Data Science
– Who - Fabrizio Antonelli- Roberto Larker
Funding agency
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Agenda
Context
Problem
Experimental setting
Solution
Evaluation
Conclusions
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The digital reflection of our cities is sharpening
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[photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg]
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The digital reflection of our cities is sharpening
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[photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg]
because the urban environment is captured in open datasets
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The digital reflection of our cities is sharpening
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[photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg]
and streams of information flows through our cities thanks to
http://emanueledellavalle.org - Emanuele Della Valle
The digital reflection of our cities is sharpening
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[photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg]
and streams of information flows through our cities thanks tothe pervasive deploymentof sensors
http://emanueledellavalle.org - Emanuele Della Valle
The digital reflection of our cities is sharpening
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[photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg]
and streams of information flows through our cities thanks tothe wide adoption of smart phones
http://emanueledellavalle.org - Emanuele Della Valle
The digital reflection of our cities is sharpening
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[photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg]
and streams of information flows through our cities thanks tothe usage of (location-based) social networks
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and it is tracking changes with a decreasing delay
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and it is tracking changes with a decreasing delay
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Data source By when Frequency Delay
Census data 100s year years months
Newspaper 100s year days 1 day
Weather sensors 10s year hours/minutes hours/minutes
TV news 10s years hours minutes
Traffic sensors years 15 minutes minutes
Call Data Recors years 15 minutes hours
Social media years seconds seconds
IoT recently milliseconds milliseconds
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Data pile up without making decision any easier
I have to decide:A or B?
Why not C?What if D?
mayor
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But smarter Big Data can …
…advance our ability to feel the pulse of our cities
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fusing all those data sources
making sense of the fused information
mayor
Definitely E!
to improve decision making and deliver innovative services
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Can we collect, analyse and repurpose
• social media and
• Call Data Records
to allow
• perceiving emerging patterns and
• observing their dynamics?
Let's focus on a concrete research question
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[photo: https://www.flickr.com/photos/debord/4932655275]
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Can we collect, analyse and repurpose
• social media captured at place and events and
• privacy-preserving aggregates of Call Data Records
to allow visually
• perceiving emerging patterns and
• observing their dynamics?
More precisely, the research question is
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[photo: https://www.flickr.com/photos/debord/4932655275]
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How to set up an experiment?
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[photo: https://www.flickr.com/photos/myfuturedotcom/6053042920]
Question Answer
Which city? Milan
Comparing what? Milan Design Week vs. Milan in general
Experimental subjects? Event Managers & casual audience
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What's Milan Design Week?
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[map: http://www.fuorisalone.it]
The Milan Design Week (MDW) is a city-scale event • held yearly in Milan, • featuring around 1,200 events • in 500+ places spread across the city and • attracting about half a million people from all over the
world.
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Ingredients of the proposed solution
Big Data technologies- Address "velocity" of data streams in memory- Address "volume" of data that do not fit in memory
semantic technologies - Address "variety" using Ontology Based Data Access- Named Entity Recognition and Linking
data science- Statistical modelling- detecting anomalies
Visual analytics- Allow no-expert access to data- Tell stories out of data
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CitySensing - a solution for event managers (2013)
F. Antonelli, M.Azzi, M.Balduini, P.Ciuccarelli, E.Della Valle, R. Larcher: City sensing: visualising mobile and social data about a city scale event. AVI 2014: 337-338
http://jol.telecomitalia.com/jolskil/citysensing/
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CitySensing - a solution for casual audience (2014)
M.Balduini, E.Della Valle, M.Azzi, R.Larcher, F.Antonelli, and P.Ciuccarelli: CitySensing: Fusing City Data for Visual Storytelling. IEEE MultiMedia. TO APPEAR
http://jol.telecomitalia.com/jolskil/citysensing/http://citysensing.fuorisalone.it/
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How CitySensing works – step 0
Set up a conceptual model (FraPPE) to master the variety in the data sources
M.Balduini, E. Della Valle: FraPPE: a vocabulary to represent heterogeneous spatio-temporal data to support visual analytics. ISWC 2015 TO APPEAR
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How CitySensing works – step 0
FraPPE• Goal: a vocabulary to represent heterogeneous spatio-
temporal data to support visual analytics
FraPPE offers an homogenous view to the visual analytics interface built on heterogeneous data
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How CitySensing works – step 1
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For every pixel compute the volume of Call Data Records(using privacy-preserving aggregation)
Real data recorded on 13 April 2013 between 13:00 and 00:00
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How CitySensing works – step 2
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Find the anomalous pixels comparing the current volumes with a model of the volumes in this time period
Real data recorded on 13 April 2013 between 13:00 and 00:00
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How CitySensing works – step 3
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Map anomalies to the districts of Milano Design Week
Brera
Tortona
What'sthis?
Real data recorded on 13 April 2013 between 13:00 and 00:00
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How CitySensing works – step 4
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For every anomalous pixel capture the hashtags and semantic entities named in the social media streams
Brera
Tortona
What'sthis?
Real data recorded on 13 April 2013 between 13:00 and 00:00
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How CitySensing works – step 5
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Take away the hashtags and semantic entities that are systematically used
Brera
Tortona
Real data recorded on 13 April 2013 between 13:00 and 00:00
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Logical architecture of CitySensing – setup time
Analyse Data Stream
Build Models
Capture Data Stream Capture Static Data
MDW
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Logical architecture of CitySensing – run time
Analyse Data Stream
Build Models
Detect Anomalies
Capture Data Stream
Visualize Analysis
Store Analysis
Capture Static Data
MDW
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Capturing static data via FraPPE
The frame duration was fixed to15 minutes
Milano area was covered with • 1 grid (100x100)• 10,000 cells• 250x250 meters in each cell
(the size of the mobile network cells in the centre of Milan)
During the Milano Design Week a total of 5.76 Mln pixel werecaptured
+1000 events in +600 placeswhere collected using the crowd-sourced databases of fuorisalone.it, breradesigndistrict.it and tortonaroundesign.com thanks to a partnership with studiolabo
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Cells in which there are placeshosting Milan Design Week 2013events
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Processing Telecom Italia Call Data Records
1.92 Mln Gaussian models were built• one for each pixel (i.e., for each frame and cell)• grouping the frames by working and week-end days • using two months of Call Data Records, and• verifying volume of CDR has a Gaussian distribution with an
Anderson-Darling test with a significance of 0.05
Built on Pig, R e Cascalog
The processing on 7 m1.large EC2 machines took 24 hours
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Bad case Good case
His
togr
am
His
togr
am
Q-Q
Plo
t
Q-Q
plo
t
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Processing Telecom Italia Call Data Records
Volume of CDR captured in Milan during the Design Week
Calls, SMS and Internet access were aggregated(with privacy-preservingmethods) and an anomaly index was computed for each of the 5.76 Mln pixel
The processing of 1 day on 7 m1.large EC2 took 20 mins
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What 2013 2014
Calls 16,743,875 19,719,629
SMSs 19,454,497 20,240,485
Internet data accesses 137,381,761 197,767,245
[image: https://cerijayne.files.wordpress.com/2011/10/outliersss.png]
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Do CDR-anomalous pixels relate to events?
CDR-anomalous pixels =pixels in which the anomaly index is high (>+2σ and <-2σ)
To test if the anomalous pixels were related to the events of the Milan Design Week• We used three ground truth
– the pixel of Milan– the pixels of Brera district– the pixels of Tortona district
where there was at least an event of Milan Design Week 2013• We compute
– Precision – Recall
of the anomalous pixels to find pixels in those three ground truths
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Do CDR-anomalous pixels relate to events?
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precision recall
Lesson learnt
• High precision
• Low recall at city scale
• High recall in Brera and Tortona
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Processing Social Streams
The machinery: the Streaming Linked Data framework
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M.Balduini, E.Della Valle, D.Dell'Aglio, M.Tsytsarau, T.Palpanas, and C.Confalonieri:Social Listening of City Scale Events Using the Streaming Linked Data Framework. International Semantic Web Conference (2) 2013: 1-16
Stream Bus
AnalyserDecorato
r
Adapter Publisher VisualizerStream
HTTP
HTTP
Data Source Streaming Linked Data Server HTML5 Browser
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Processing Social Streams
Decoration at work
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Happily into a bottle of Heineken bear #heinekendesignweek @ the Heineken Magazzini
City-Scale Event: Milano Design Week
Event: Heineken Design Week
Location: The Magazzini
hosts
takesPlaceIn
M.Balduini, A.Bozzon, E.Della Valle, Y.Huang, G-J Houben: Recommending Venues Using Continuous Predictive Social Media Analytics. IEEE Internet Computing 18(5): 28-35 (2014)
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Processing Social Streams
predictive models were built• For hastags and semantic entities systematically present• Using a Holt-Winter method
• grouping the frames by – working and week-end days and– Early morning, morning, afternoon, evening, and late night
• Analysing 300,000 geo-located micro-posts collected other 6 months in Milano area (november 2013, aprile 2014)
• It takes few seconds per hashtag/semantic entity on a 60€/month VM in a IaaS
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Data
Fitted
Forecast
Lower 2,5%
Upper 97,5%
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Processing Social Streams
Usage of #milan in the weeks around Milan Design Week
Subtracting the predicted usage of #milan
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200 – 700
700 – 1100
1100 – 1400
1400 – 1900
1900 – 200
200 – 700
700 – 1100
1100 – 1400
1400 – 1900
1900 – 200
WD WE WD WE WD WE WD WE WD
Milan Design Week
WD WE WD WE WD WE WD WE WD
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Processing Social Streams
The difference between the observed and the predicted usage of #milan perfectly fits the usage of #mdw (the official hashtag of Milan Design Week)
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200 – 700
700 – 1100
1100 – 1400
1400 – 1900
1900 – 200
200 – 700
700 – 1100
1100 – 1400
1400 – 1900
1900 – 200
WD WE WD WE WD WE WD WE WD
Milan Design Week
Anomalous usage of
#milan
Usage of #mdw
http://emanueledellavalle.org - Emanuele Della Valle
Processing Social Streams
Geo-references micro-posts captured, semantically annotated, cleansed using the predictive models and analyzed in Milan area
For each pixel with at least 1 micro-post we computed The volume related to Milano Design Week
The top-10 hashtags
The top-3 locations/events
Real-time processing was possible with our in-memory C-SPARQL engine and the Streaming Linked Data framework on a 20€/month VM in a IaaS
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What 2013 2014
Geo-located micropost 57,154 21,782
Linked to Milano Design Week 3,569 3,499
Linked to a specific location/event 761 547
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Do socially active pixels relate to events?
socially active pixels =pixels in which we captured social media that talk about Milan Design Week
To computes • precision• recall
of the socially active pixels in find pixels in pixels in the three ground truths about Milan, Brera district and Tortona district
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Do socially active pixels relate to events? M
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0.10.20.30.40.50.60.70.80.9
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Do socially active pixels relate to events? M
ilan
Bre
raTo
rotn
a
Tuesday Wednesday Thursday Friday Saturday Sunday
precision recall
Lesson learnt
• High precision
• Acceptable recall in the districts
http://emanueledellavalle.org - Emanuele Della Valle
Anomalous Socially active Intersection Similar?
Are CDR-anomalous and socially active pixels similar?
Which of the following four scenarios?
50
http://emanueledellavalle.org - Emanuele Della Valle
Are CDR-anomalous and socially active pixels similar?
More formally• Jaccard
• E.g.,
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J(A,B) = 8/11 J(A,B) = 3/11
A B A
B
J(A,B) = |A ∩ B|
|A∪B|
http://emanueledellavalle.org - Emanuele Della Valle
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Are CDR-anomalous and socially active pixels similar?B
rera
Toro
tna
Tuesday Wednesday Thursday Friday Saturday Sunday
recall CDR-anomalous recall socially active Jaccard
Lesson learnt
At district level, in the large majority of the cases the
socially active pixels are also CDR-anomalous pixels
http://emanueledellavalle.org - Emanuele Della Valle 53
Visualizing for a casual audience
http://emanueledellavalle.org - Emanuele Della Valle 54
See it in action!
http://youtu.be/MOBie09NHxM
http://emanueledellavalle.org - Emanuele Della Valle
Evaluation methodology for the casual audience
Guessability study• Can you guess what I mean without any explanation?
E.g.
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Dinosaur extinction
"The Shining" by Stephen King
http://emanueledellavalle.org - Emanuele Della Valle
Evaluation of interface guessability
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http://emanueledellavalle.org - Emanuele Della Valle
The patters you should have got
The CDR-anomaly and the social activity is
57
Correlated Partially correlated Not correlated
http://emanueledellavalle.org - Emanuele Della Valle
Evaluation of interface guessability
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Q: In Brera District the volume of social media signal is partially correlated with the value of mobile anomaly signal A:
FALSE
UNCERTAINTRUE
0
0.2
0.4
0.6
0.8
1
http://emanueledellavalle.org - Emanuele Della Valle
Evaluation of interface guessability
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Q: In Porta Romana the volume of social media signal is strongly correlated with the value of mobile anomaly signal A:
FALSE
UNCERTAINTRUE
0
0.2
0.4
0.6
0.8
1
http://emanueledellavalle.org - Emanuele Della Valle
Evaluation of interface guessability
60
Q: In Tortona District the volume of social media signal is strongly correlated with the value of mobile anomaly signalA:
FALSE
UNCERTAINTRUE
0
0.2
0.4
0.6
0.8
1
http://emanueledellavalle.org - Emanuele Della Valle
Back to the research question
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[photo: https://www.flickr.com/photos/debord/4932655275]
Can we collect, analyse and repurpose
• social media captured at place and events and
• privacy-preserving aggregates of Call Data Records
to allow visually
• perceiving emerging patterns and
• observing their dynamics?
Yes!
at least, in Milano Design Week 2013 and 2014
[photo: https://flic.kr/p/beuDaX ]
http://emanueledellavalle.org - Emanuele Della Valle 62
Take home message … guess it :-)
http://emanueledellavalle.org - Emanuele Della Valle 63
Take home message … guess it :-)
Emanuele Della [email protected]://emanueledellavalle.org
Thank you!
Any question?
Listening to the pulse of our cities fusing Social Media Streams and Call Data RecordsEmanuele Della [email protected]://emanueledellavalle.org
18th International Conference on Business Information Systems
24-26 June 2015, Poznań, Poland