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the effect by which one accidentally discovers something fortunate, especially while looking for something entirely unrelated. (wikipedia.org)
Serendipity n.
Scoperta (Italian)Serendipiteit (Dutch)Vrozené štěstí (Czech)
/ˌsɛ.rɛn.ˈdɪp.ə.ti/
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Vision What happens when you aggregate partial
observations?
• Maps and ship captains– No single human has been to
every point on a map– Cartographers resolved partial
observations from ship captains– Many needed, potentially
conflicting– Slowly, there emerged a map of
the world
• Can we do something similar to learn something new about our cities?
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Objectives• Aim? Stimulate and integrate research groups
from currently fragmented research areas, creating synergies at the cross-over points.
• Approach? By fostering long-term relationships between research groups, based around people, and laying the foundations for a Virtual Centre of Excellence (VCE).
• In what areas? Real-time, large-scale data analysis and inference for fusing semantic information and predicting events.
• How? By harvesting, mining, correlating and clustering extremely large, highly dynamic, very noisy, contradictory and incomplete information from multiple sources including: tweets, logs, RSS, web-sources, mobile texts, web-cams, CCTV and other publicly-available multimedia archives.
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What’s New in SERENDIPITI?
• Real-time
• Prediction (events)
• Multimodal
• Noisy, errorsome data
• Novel applications
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How do we do this?
• Sensing– Aggregate diverse sources of information from
the real and online worlds
• Analysis– Extract stable spatio-temporal patterns of
human activity – Track these over time
• Use Case Scenarios– City Planning– Journalist (e.g. see Appendix)– Police
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How do we do this?
SensorBase
- Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity
- Blogs, wikis, web feeds- Tweets, RSS- Event guides
Physical
Online
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Track evolution of events over space
and time
SensorBase
- Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity
- Blogs, wikis, web feeds- Tweets, RSS- Event guides
Physical
OnlineMachine
learning and data mining
How do we do this?
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Track evolution of events over space
and time
SensorBase
- Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity
- Blogs, wikis, web feeds- Tweets, RSS- Event guides
Physical
OnlineMachine
learning and data mining
How do we do this?
SERENDIPITIPlatform
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Track evolution of events over space
and time
SensorBase
- Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity
- Blogs, wikis, web feeds- Tweets, RSS- Event guides
Physical
OnlineMachine
learning and data mining
How do we do this?
SERENDIPITIPlatform
Planning (City)
Journalist
Police
Use Case Scenarios
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Partners & Roles
Patricia Ho-Hune
Alan Smeaton, Noel O’Connor, Barry Smyth
Giovanni Tummarello, John Breslin, Paul Buitelaar
Keith van Rijsbergen, Joemon Jose, Mark Girolami
Ebroul Izquierdo
Maarten de Rijke, Arnold Smeulders
Vojtech Svatek
ERCIM
DCU-CLARITY
DERI
GLA
QMUL
UvA
UEP
0
1
2
3
4
5
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Partners & RolesEU Project Management
Analysis of multimodal sensor data, real-time integration of physical & online sources, organisation & management of online sources
Semantic text analysis/mining, large-scale semantic search/indexing, linked data, social semantics, online communities
Multimedia information retrieval, formal models, mining information from large data sets, event detection, information fusion, machine learning
Correlating/mining media and textual data, sensor base, automatic (CCTV-based) event analysis
Focused crawling, wrapper induction, mining social media, information extraction, data integration, cross-media mining and information fusion, machine learning
Mining rich associations from large databases, information extraction from the Web, semantic and social Web technology, effectiveness of ICT
ERCIM
DCU-CLARITY
DERI
GLA
QMUL
UvA
UEP
0
1
2
3
4
5
6
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1 2 3 4 5 6DCU QMULDERI GLA UvA UEP
PARTNERS
Track evolution of events over space
and time
SensorBase
- Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity
- Blogs, wikis, web feeds- Tweets, RSS- Event guides
Physical
OnlineMachine
learning and data mining
SERENDIPITIPlatform
Planning (City)
Journalist
Police
Use Case Scenarios
16
1 2 3 4 5 6DCU QMULDERI GLA UvA UEP
PARTNERS
Track evolution of events over space
and time
SensorBase
- Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity
- Blogs, wikis, web feeds- Tweets, RSS- Event guides
Physical
OnlineMachine
learning and data mining
SERENDIPITIPlatform
Planning (City)
Journalist
Police
Use Case Scenarios1
34
5
4
63 5
236
12
56
123456
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Work Packages
• WP1 – Management• WP2 – Integration of Organisation & People
• WP6 – Applications & Infrastructure Sharing
• WP7 – Outreach (Spreading Excellence)
Joint Program of Activities• WP3 – Real-time Large-scale Data
Analysis• WP4 – Semantic Information Fusion• WP5 – Inference & Event Prediction
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Implementation
Planning (City)
Journalist
Police
User Group Data Provision Board
Industrial Advisory Board
Wikimedia Foundation
Boards.ie
TW
S+V
EPA/MI/Met
Ken Wood
Milek Bover
E. Aarts
Carto Ratti
One other !
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Tangible Outputs
• Provision of mini projects to tackle unforeseen research topics
• Industrial placements of research staff• Academic exchanges• Contribution to standardisation efforts• Public software repositories and
tools/data access through the SERENDIPITI platform
• Outreach to other targeted projects