Listening to the pulse of our cities with Stream Reasoning (and few more technologies)

Listening to the pulse of our cities
with Stream Reasoning
(and few more technologies)
Emanuele Della Valle
@manudellavalle - emanuele.dellavalle@polimi.it
http://emanueledellavalle.org

Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org
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cities fusing Social Media Streams and Call Data Records”
by Emanuele Della Valle
 To view a copy of this license, visit
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2

Me
 Assistant Professor at DEIB
Politecnico di Milano
 Expert in semantic technologies
and stream computing
 Brander 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
 R&D advisor: socialometers.com
3

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
the pervasive deployment
of sensors

10
the wide adoption of smart
phones

11
the usage of (location-based)
social networks

and it tracks changes with a decreasing delay
12

and it tracks 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

Emanuele Della Valle - @manudellavalle - http://emanueledellavalle.org 14
Data piles up without easing decision making
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.

CitySensing 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/jols
kil/citysensing/

CitySensing 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.
http://jol.telecomitalia.com/jolskil/citysensing/
http://citysensing.fuorisalone.it/

Ingredients of the proposed solution
 Big Data technologies
- Address "volume" of data that do not fit in
memory
- Address "velocity" of data streams 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
22
StreamReasoning

What's Stream Reasoning?
Tame Variety and Velocity simultaneously
Traditional StreamReasoning
E.Della Valle, S. Ceri, F. van Harmelen, D. Fensel: It's a Streaming World! Reasoning upon
Rapidly Changing Information. IEEE Intelligent Systems 24(6): 83-89 (2009)
E. Della Valle, D. Dell'Aglio, A. Margara: Taming velocity and variety simultaneously in big
data with stream reasoning: tutorial. DEBS 2016: 394-401

How CitySensing works – step 0
Reality
Capture
Frame
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

20/07/2016
Grid
Cell
Frame

20/07/2016
Pixel Frame 1

20/07/2016
Place A
Event A

20/07/2016
Event A
Frame 1

20/07/2016
Event B
Place B
Frame 2

 FraPPE offers an homogenous view to the
visual analytics interface built on heterogeneous
data
31
Geo-spatial fragmentProvenance fragment
Time Varying fragmentFraPPE specifics

32
For every pixel compute continuously the volume of Call Data
Records (using privacy-preserving aggregation)
Real data recorded on 13 April 2013 between 13:00 and 00:00

33
Find continuously the anomalous pixels comparing the current
volumes with a model of the volumes in this time period

34
Map continuously anomalies to the districts of Milano Design Week
Brera
Tortona
What's
this?

35
For every anomalous pixel continuously capture the hashtags and
semantic entities named in the social media streams
Brera
Tortona
What's
this?

36
Continuously discard 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

Logical architecture of CitySensing – run time
Analyse Data Stream
Build Models
Detect Anomalies
Capture Data Stream
Visualize Analysis
Store Analysis
Capture Static Data
MDW
StreamReasoning
InductiveDeductive

Few more details on Stream Reasoning
Uses logical window
Connects to a
variety of
data streams
Real-time
query answering
complex event processing
analysis
Stream
Reasoner
for data
"in-motion"
(In-memory)
Store
data
"at-rest"
(distributed)
optimizes
joins
MDW

Capturing static data via FraPPE
 The frame duration was fixed to
15 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 were
captured
 +1000 events in +600 places
where collected using the
crowd-sourced databases of fuorisalone.it, breradesigndistrict.it and
tortonaroundesign.com thanks to a partnership with studiolabo
41
Cells in which there are places
hosting Milan Design Week 2013
events

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
42
Bad case Good case
Histogram
Histogram
Q-QPlot
Q-Qplot

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-preserving
methods) and an
anomaly index was
computed for each of
the 1.92 Mln pixel/day
 The processing of 1 day on 7 m1.large EC2 took 20 mins
43
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
44

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Tuesday Wednesday Thursday Friday Saturday Sunday
precision

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MilanBreraTorotna
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recall

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MilanBreraTorotna
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precision
recall

Processing Social Streams
 The machinery: the Streaming Linked Data framework
48
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
AnalyserDecorator
Adapter Publisher VisualizerStream
HTTP
HTTP
Data Source Streaming Linked Data Server HTML5 Browser

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)
Happily inside a bottle of
Heineken beer @ the Heineken Magazzini
#heinekendesignweek
Event
Milan Design Week
Event
Heineken Design Week
Location
The Magazzini
hosts
has location

KnowledgeGraph
W
Company
Heineken
W
Drink
beer
produces
organized by
Wide as Wikipedia As deep as you like

 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
50
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
51
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)
52
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
53
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
54

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MilanBreraTorotna
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0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
0
0.2
0.4
0.6
0.8
1
0904:00
0907:00
0910:00
0913:00
0916:00
0919:00
0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
56
MilanBreraTorotna
recall

0
0.2
0.4
0.6
0.8
1
0904:00
0907:00
0910:00
0913:00
0916:00
0919:00
0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
0
0.2
0.4
0.6
0.8
1
0904:00
0907:00
0910:00
0913:00
0916:00
0919:00
0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
0
0.2
0.4
0.6
0.8
1
0904:00
0907:00
0910:00
0913:00
0916:00
0919:00
0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
57
MilanBreraTorotna
precision
recall

0
0.2
0.4
0.6
0.8
1
0904:00
0907:00
0910:00
0913:00
0916:00
0919:00
0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
0
0.2
0.4
0.6
0.8
1
0904:00
0907:00
0910:00
0913:00
0916:00
0919:00
0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
0
0.2
0.4
0.6
0.8
1
0904:00
0907:00
0910:00
0913:00
0916:00
0919:00
0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
58
MilanBreraTorotna
precision
recall

Anomalous Socially active Intersection Similar?




Are CDR-anomalous and socially active pixels similar?
 Which of the following four scenarios?
59

 More formally
• Jaccard
• E.g.,
60
J(A,B) = 8/11 J(A,B) = 3/11
A B A
B
J(A,B) =
|A ∩ B|
|A∪B|

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0904:00
0907:00
0910:00
0913:00
0916:00
0919:00
0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0904:00
0907:00
0910:00
0913:00
0916:00
0919:00
0922:00
1001:00
1004:00
1007:00
1010:00
1013:00
1016:00
1019:00
1022:00
1101:00
1104:00
1107:00
1110:00
1113:00
1116:00
1119:00
1122:00
1201:00
1204:00
1207:00
1210:00
1213:00
1216:00
1219:00
1222:00
1301:00
1304:00
1307:00
1310:00
1313:00
1316:00
1319:00
1322:00
1401:00
1404:00
1407:00
1410:00
1413:00
1416:00
1419:00
1422:00
1501:00
61
BreraTorotna
recall CDR-anomalous
recall socially active
Jaccard

Visualizing for a casual audience

See it in action!
http://youtu.be/MOBie09NHxM

Evaluation methodology for casual audience
 Guessability study
• Can you guess what I mean without any explanation?
 E.g.
64
Dinosaur extinction
"The Shining" by Stephen King

Evaluation of interface guessability
65

The patters you should have got
 The CDR-anomaly and the social activity is
66
Correlated Partially correlated Not correlated

67
Q: In Brera District
the volume of social
media signal is
partially correlated
with the value of
mobile anomaly
signal
A:
0
0.2
0.4
0.6
0.8
1

68
Q: In Brera District
media signal is
partially correlated
with the value of
mobile anomaly
signal
A:
0
0.2
0.4
0.6
0.8
1

69
Q: In Porta Romana
media signal is
strongly correlated
with the value of
mobile anomaly
signal
A:
0
0.2
0.4
0.6
0.8
1

70
Q: In Porta Romana
media signal is
strongly correlated
with the value of
mobile anomaly
signal
A:
0
0.2
0.4
0.6
0.8
1

71
Q: In Tortona District
media signal is
strongly correlated
with the value of
mobile anomaly
signal
A:
0
0.2
0.4
0.6
0.8
1

72
Q: In Tortona District
media signal is
strongly correlated
with the value of
mobile anomaly
signal
A:
0
0.2
0.4
0.6
0.8
1

Back to the research question
73
• 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 ]

… and I was so crazy to start up a company …
74
http://www.socialometers.com

Lesson Learnt for Stream Reasoning
 The technical barriers are high
 The theoretical foundations are incomplete
 The veracity problem is sort of forgotten
75

High Technical Barriers for Stream Reasoning
 We are getting close to a shared understanding on
RDF Stream Processing (RDF stream and continuous
extension of SPARQL)
• See http://www.w3.org/community/rsp/
 Missing infrastructure
• Only one proposal for RDF stream publishing
– http://streamreasoning.github.io/TripleWave/
• Only one proposal for RDF Stream Processing APIs
– http://streamreasoning.org/resources/rsp-services
 Only prototypes, some unmaintained
 Need for scalable system built on Big Data technologies
(e.g., Spark/Flink)
 Lack of systematic and comparative evaluation
• too many benchmarks all focusing RDF stream processing
with little emphasis on reasoning
76

Incomplete Stream Reasoning theory
 Two reference models exist
• RSP-QL: Built on SPARQL semantics
– D.Dell'Aglio, E. Della Valle, J-P Calbimonte, Ó. Corcho:
RSP-QL Semantics: A Unifying Query Model to Explain Heterogeneity of
RDF Stream Processing Systems. Int. J. Semantic Web Inf. Syst. 10(4):
17-44 (2014)
• LARS: Built on datalog-style rules
– H.Beck, M.Dao-Tran, T.Eiter, M.Fink: LARS: A Logic-Based Framework
for Analyzing Reasoning over Streams. AAAI 2015: 1431-1438
 However
• What's the complexity of Q/A in RSP-QL/LARS?
• How to deal with inconsistency appearing over time?
• How do stream reasoning and event calculus relates?
 OBDA on static data ≠ OBDA for continuous querying
ans = data + query Ans(t) = sys(t) + data(t) + query
 What about inductive stream reasoning?
77

The veracity problem is sort of forgotten
 Some initial works
• D.F. Barbieri, D. Braga, S. Ceri, E. Della Valle, Y. Huang, V.
Tresp, A. Rettinger, H. Wermser: Deductive and Inductive
Stream Reasoning for Semantic Social Media Analytics.
IEEE Intelligent Systems 25(6): 32-41 (2010)
• M. Nickles, A. Mileo: Web Stream Reasoning Using
Probabilistic Answer Set Programming. RR 2014: 197-
205
• A-Y Turhan, E. Zenker: Towards Temporal Fuzzy Query
Answering on Stream-based Data. HiDeSt@KI 2015: 56-
69
 Missing Theory?
78

Take home message … guess it :-)

Take home message … guess it :-)
Emanuele Della Valle
@manudellavalle
emanuele.dellavalle@polimi.it
http://emanueledellavalle.org

Listening to the pulse of our cities with Stream Reasoning (and few more technologies)

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