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/

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

[email protected]

http://emanueledellavalle.org

http://streamreasoning.org

http://fluxedo.com


mailto:[email protected]


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

4



Agenda

Context

Problem

Experimental setting

Solution

Evaluation

Conclusions

5



The digital reflection of our cities is sharpening

6

[photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg]




7


because the urban environment is captured in open datasets




8


and streams of information flows through our cities thanks to




9


and streams of information flows through our cities thanks tothe pervasive deploymentof sensors




10


and streams of information flows through our cities thanks tothe wide adoption of smart phones




11


and streams of information flows through our cities thanks tothe usage of (location-based) social networks



and it is tracking changes with a decreasing delay

12



and it is tracking changes with a decreasing delay

13

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


http://emanueledellavalle.org - Emanuele Della Valle 14

Data pile up without making decision any easier

I have to decide:A or B?

Why not C?What if D?

mayor



But smarter Big Data can …

…advance our ability to feel the pulse of our cities

15

fusing all those data sources

making sense of the fused information

mayor

Definitely E!

to improve decision making and deliver innovative services



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

16

[photo: https://www.flickr.com/photos/debord/4932655275]




• social media captured at place and events and

• privacy-preserving aggregates of Call Data Records

to allow visually



More precisely, the research question is

17




How to set up an experiment?

18

[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



What's Milan Design Week?

19

[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.



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

20



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/



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/



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




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

24




25

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




26

Find the anomalous pixels comparing the current volumes with a model of the volumes in this time period





27

Map anomalies to the districts of Milano Design Week

Brera

Tortona

What'sthis?





28

For every anomalous pixel capture the hashtags and semantic entities named in the social media streams

Brera

Tortona

What'sthis?





29

Take away the hashtags and semantic entities that are systematically used

Brera

Tortona




Logical architecture of CitySensing – setup time

Analyse Data Stream

Build Models

Capture Data Stream Capture Static Data

MDW



Logical architecture of CitySensing – run time

Analyse Data Stream

Build Models

Detect Anomalies

Capture Data Stream

Visualize Analysis

Store Analysis

Capture Static Data

MDW



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

32

Cells in which there are placeshosting Milan Design Week 2013events



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

33

Bad case Good case

His

togr

am

His

togr

am

Q-Q

Plo

t

Q-Q

plo

t



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

34

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]



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

35




09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

Mila

nB

rera

Toro

tna 09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

Tuesday Wednesday Thursday Friday Saturday Sunday

precision




09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

Mila

nB

rera

Toro

tna 09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1


recall




09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

Mila

nB

rera

Toro

tna 09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1


precision recall

Lesson learnt

• High precision

• Low recall at city scale

• High recall in Brera and Tortona



Processing Social Streams

The machinery: the Streaming Linked Data framework

39

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




Decoration at work

40

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)




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

41

Data

Fitted

Forecast

Lower 2,5%

Upper 97,5%




Usage of #milan in the weeks around Milan Design Week

Subtracting the predicted usage of #milan

42

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





The difference between the observed and the predicted usage of #milan perfectly fits the usage of #mdw (the official hashtag of Milan Design Week)

43

200 – 700

700 – 1100

1100 – 1400

1400 – 1900

1900 – 200

200 – 700

700 – 1100

1100 – 1400

1400 – 1900

1900 – 200


Milan Design Week

Anomalous usage of

#milan

Usage of #mdw




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

44

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



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

45



09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

46

Do socially active pixels relate to events? M

ilan

Bre

raTo

rotn

a


precision



09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

47


ilan

Bre

raTo

rotn

a


recall



09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

48


ilan

Bre

raTo

rotn

a


precision recall



09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.10.20.30.40.50.60.70.80.9

1

49


ilan

Bre

raTo

rotn

a


precision recall

Lesson learnt

• High precision

• Acceptable recall in the districts



Anomalous Socially active Intersection Similar?

Are CDR-anomalous and socially active pixels similar?

Which of the following four scenarios?

50



Are CDR-anomalous and socially active pixels similar?

More formally• Jaccard

• E.g.,

51

J(A,B) = 8/11 J(A,B) = 3/11

A B A

B

J(A,B) = |A ∩ B|

|A∪B|



09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

09 04:00

09 10:00

09 16:00

09 22:00

10 04:00

10 10:00

10 16:00

10 22:00

11 04:00

11 10:00

11 16:00

11 22:00

12 04:00

12 10:00

12 16:00

12 22:00

13 04:00

13 10:00

13 16:00

13 22:00

14 04:00

14 10:00

14 16:00

14 22:000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

52

Are CDR-anomalous and socially active pixels similar?B

rera

Toro

tna


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



Visualizing for a casual audience



See it in action!

http://youtu.be/MOBie09NHxM


http://youtu.be/MOBie09NHxM


Evaluation methodology for the casual audience

Guessability study• Can you guess what I mean without any explanation?

E.g.

55

Dinosaur extinction

"The Shining" by Stephen King



Evaluation of interface guessability

56



The patters you should have got

The CDR-anomaly and the social activity is

57

Correlated Partially correlated Not correlated




58

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




59

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




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



Back to the research question

61



• social media captured at place and events and

• privacy-preserving aggregates of Call Data Records

to allow visually



Yes!

at least, in Milano Design Week 2013 and 2014

[photo: https://flic.kr/p/beuDaX ]



Take home message … guess it :-)



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


Listening to the pulse of our cities fusing Social Media Streams and Call Data Records

Internet

Transcript of Listening to the pulse of our cities fusing Social Media Streams and Call Data Records