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1
Collecte de
données
@RomainRouvoy
Mobile Data Deluge
700
billions
minutes
/month
2,9
millions
emails
/second
24
petabytes
processed
/day
426
purchases
/seconde
20
hours
/minute
50
millions
tweets/day
2013
•1,1 exabytes of data
2019
•5,6 billions of smartphones
•12 exabytes of data
4,5 millions / day
(source : ericsson,2013)
Interaction
Mouvement
Mobile app stores
Targeting millions of users
Point d’intérêts
Mode de transport
5
Crowd-sensing | kraʊdːsɛnsɪŋ |
Capability of lifting a (large) diffuse group of participants to
delegate the task of retrieving trustable data from the field. This
includes:
Participatory sensing involves the user in the sensing task (eg.
surveys, forms)
Opportunistic sensing uses mobile sensors carried by the user
(eg. smartphones, IoT)
Applications to data
visualisation
source: http://opensignal.com
Applications to IoT monitoring
Applications to
crowdsourcing
13
Introducing APISENSE
A crowd-sensing campaign
Collecting Stage Storing Stage Mining Stage Publishing StageTasking Stage
https://apisense.io
Crowd-sensing made easy!
Device-level Sensing Task
Location
User
Interaction
Device
State
Stings
Save
Data
Capture
Media
Survey Notify
Actions
trigger
Events
onLocationChange(…)
onBatteryStateChange(…)
onNetworkStateChange(…)
onPhoneCall(…)
publish
subscribe
Battery
Level
Increasing User Location Privacy with
Fougere
Overview of Fougere
Internet
Alice
Storage
server B
data uploaded by Fougeredata uploaded without Fougere
Fougere
LPPM GPS 1
LPPM Id 1
App A App B
Fougere
LPPM GPS 2
LPPM Time 1
App A App B
Fougere
LPPM GPS 1
LPPM Time 2
App A App C
Storage
server A
Eve Trudy
Storage
server C
Effects on data upload
delay/coverage
Evaluation of APISENSE
80%
50%
82 %
W/B-Scanner
opportunist
Citizen journalist
participative
APISENSE® 4 9
Anonysense 5 N/A
Pogo 4 N/A
MyExperience N/A 27
Medusa N/A 45
PRISM ?? 330
2%
27
Examples of crowd
studies
Applications to data
visualisation
source: http://opensignal.com
var gps = requires(‘location’);
var db = requires(‘honeycomb’);
var gsm = requires(‘gsm’);
gps.onLocationChange(function(event){
db.sync({
lat : event.latitude,
lng : event.longitude,
signal : gsm.rssi()
});
});
Event listenerFaçades
Data upload
Device-level sensing task
Enabling transport studies
Applications to
crowdsourcing
Supporting human sciences studies
Leveraging network analysis
Applications to IoT monitoring
Apolline
40
Beyond crowd-sensing
How to reproduce a crash?
41
How to reproduce a crash?
42
How to reproduce a crash?
43
How to reproduce a crash?
44
4.1
How to reproduce the
crash?
45
4.1
User Interactions
How to reproduce a crash?
46
4.1
User Interactions
Execution Context
(static/dynamic)
MoTiF: Crash
Reproduction
MoTiF: Crash
Reproduction
1. Collecting
Crash Traces
MoTiF: Crash
Reproduction
1. Collecting
Crash Traces
2. Identifying
Crash Patterns
MoTiF: Crash
Reproduction
1. Collecting
Crash Traces
2. Identifying
Crash Patterns
3. Synthesizing
Crash Test Suites
MoTiF: Crash
Reproduction
1. Collecting
Crash Traces
2. Identifying
Crash Patterns
3. Synthesizing
Crash Test Suites
4. Assessing Test
Suite Effectivity
MoTiF: Crash
Reproduction
1. Collecting
Crash Traces
2. Identifying
Crash Patterns
3. Synthesizing
Crash Test Suites
4. Assessing Test
Suite Effectivity
developers
2. Identifying Crash
Patterns
Crowd Crash Graph
e1 e2
e4 e1 e2e1
e4 e1 e2e5
c1
c1
c1
1
e1 e2
e4
e5
Crowd Crash
Graph
c1
e1 e2
e4 e1 e2e1
e4 e1 e2e5
c1
c1
c1
S
0.66
0.33
0.75
0.25
1
1
2. Identifying Crash
Patterns
Crowd Crash Graph
1
e1 e2
e4
e5
Crowd Crash
Graph
c1
e1 e2
e4 e1 e2e1
e4 e1 e2e5
c1
c1
c1
S
0.66
0.33
0.75
0.25
1
1
2. Identifying Crash
Patterns
Crowd Crash Graph
Markov probability
1
e1 e2
e4
e5
Crowd Crash
Graph
c1
e1 e2
e4 e1 e2e1
e4 e1 e2e5
c1
c1
c1
S
0.66
0.33
0.75
0.25
1
1
2. Identifying Crash
Patterns
Crowd Crash Graph
Markov probability
Context
2. Identifying Crash
Patterns
Crowd Crash Graph
1
e1 e2
e4
e5
c1
S
0.66
0.33
0.75
0.25
1
1
Synthesize steps to reproduce crashes
2. Identifying Crash
Patterns
Crowd Crash Graph
Shortest path with
highest probability
Synthesize steps to reproduce crashes
1
e1 e2
e4
e5
c1
S
0.66
0.33
0.75
0.25
1
1
CROWD
CONSOLIDATED
TRACE!!
2. Identifying Crash
PatternsLearning crash-prone contexts
c1e1 e2
2. Identifying Crash
PatternsLearning crash-prone contexts
c1e1 e2
Dynamic
context
2. Identifying Crash
PatternsLearning crash-prone contexts
c1e1 e2
Dynamic
context
2. Identifying Crash
PatternsLearning crash-prone contexts
c1e1 e2
Dynamic
context
Frequent Closed Sequential
Pattern Mining (100% support)
2. Identifying Crash
PatternsLearning crash-prone contexts
c1e1 e2
Dynamic
context
Frequent Closed Sequential
Pattern Mining (100% support)
2. Identifying Crash
PatternsLearning crash-prone contexts
c1e1 e2
Dynamic
context
Frequent Closed Sequential
Pattern Mining (100% support)
Static
context
2. Identifying Crash
PatternsLearning crash-prone contexts
c1e1 e2
Dynamic
context
Frequent Closed Sequential
Pattern Mining (100% support)
Static
context
Union
2. Identifying Crash
PatternsLearning crash-prone contexts
c1e1 e2
Dynamic
context
Frequent Closed Sequential
Pattern Mining (100% support)
Static
context
Union
3. Generating Crash Test
SuitesConsolidated Trace
e1 e2 c1
Dynamic Context
Static Context
3. Generating Crash Test
SuitesConsolidated Trace
e1 e2 c1
Dynamic Context
Static Context
Synthesize
Crash Test
Button onClick clickOnButton
Activiy onOrientationChange setOrientationActivity
…
Mapping
Rules
3. Generating Crash Test
SuitesConsolidated Trace
e1 e2 c1
Dynamic Context
Static Context
Synthesize
Crash Test
Mapping
Rules
public void testRun(){
solo.sleep(2000);
solo.clickOnView(solo.getView(215));
solo.sleep(2000);
solo.setWifiData(false);
solo.setMobileData(false);
solo.clickOnMenuItem(“Save Page”); }
Robotium Black-box
test
Button onClick clickOnButton
Activiy onOrientationChange setOrientationActivity
…
CrowdSeer: Crash
Prevention
1. Collecting
Crash Traces
2. Identifying
Crash Patterns
3. Synthesizing
Crash Test Suites
4. Assessing Test
Suite Effectivity
developers
CrowdSeer: Crash
Prevention
1. Collecting
Crash Traces
2. Identifying
Crash Patterns
3. Synthesizing
Crash Test Suites
4. Assessing Test
Suite Effectivity
developers
5. Synthesizing
Preventive patch
CrowdSeer: Crash
Prevention
1. Collecting
Crash Traces
2. Identifying
Crash Patterns
3. Synthesizing
Crash Test Suites
4. Assessing Test
Suite Effectivity
developers
5. Synthesizing
Preventive patch
6. Hot-patching
Crash-triggering
feature to disable
CrowdSeer: Crash
Prevention
CrowdSeer: Crash
Prevention
CrowdSeer: Crash
Prevention
CrowdSeer: Crash
Prevention
CrowdSeer: Crash
Prevention
CrowdSeer: Crash
Prevention
CrowdSeer: Crash
Prevention
CrowdSeer: Crash
Prevention
Take away
• Inria Spirals (http://team.inria.fr/spirals)
• Software engineering ⊕ Distributed Systems
• Expertise on privacy, security, energy, performance
• Applications from IoT to mobile to cloud
• APISENSE® platform (http://apisense.io)
• Leveraging mobile crowd-sensing
• Enabling crowd computing
• Fougere library
• Boosting user location privacy
• Enabling device-to-device communications
Contact: Romain ROUVOY (romain.rouvoy@inria.fr)

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Inria Tech Talk - Collecte de données - 30 Mai 2018

Editor's Notes

  1. ’30 Les données sont souvent considérées comme le pétrole de demain En effet depuis les années 2000 déja, de nombreux acteurs industrielle ont fondé leur modèle économique sur la collecte et l’exploitation de données utilisateur Mais plus récemment, notament grâce à la forte intégration des équipes mobiles dans notre vie quotidienne, la collecte de données connais une croissance sans égale estimé à plus de 1000% entre 2013 et 2014 selon une étude publié par ericson l’an dernier Si c’est prédiction s’avere exact, rien que pou l’année 2019, plus de 5 millard de smartphone serait vendu, et la quantité de données échangé par ces terminaux serai de 12 exacttets, correspondant à 4,5 millions de film échangé par jour
  2. En effet, de nombreux critéres font des smartphones une plate-forme idéal de collecte de données Disposant non seulement d’une grande capacité de calcul Ces terminaux sont programmable et équipé de nombreux capteurs Qui peuvent utilisé pour collecter de nombreuses informations Un autre aspect intéressant sont magasins d’application en ligne, qui rende potentiellement accessible des millons d’utilisateurs pour le déploiement d’application de collecte
  3. Despite the high amount of apps currently available, the quality of them greatly varies We, as mobile user, frequently experience errors and failures with some of the apps installed on our devices. Users report negative reviews and ratings which impacts on both the developer and the store reputation This is very critical, given that the mobile ecosystem is very competitive High quality and provide a good user experience in order to keep their users and attract new users
  4. Agrregate crash traces in a compact representation