serendipiti
DESCRIPTION
SERENDIPITI. SEnsoR ENricheD Information Prediction and InTegratIon. Serendipity n. / ˌ s ɛ .r ɛ n. ˈ d ɪ p. ə .ti/. Scoperta (Italian) Serendipiteit (Dutch) Vrozené štěstí (Czech). - PowerPoint PPT PresentationTRANSCRIPT
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SERENDIPITI SEnsoR ENricheD
Information Prediction and InTegratIon
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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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Today’s Agenda
VisionObjectivesTechnology
Partners
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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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Physical
Online
How do we do this?
SensorBase
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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
Online Machine 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
Online Machine 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
Online Machine learning and data mining
How do we do this?
SERENDIPITIPlatform
Planning (City)
Journalist
Police
Use Case Scenarios
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Partners & RolesPatricia 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
ERCIMDCU-
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Partners & RolesEU Project Management
Analysis of multimodal sensor data, real-time integration of physical & online sources, organisation & management of online sourcesSemantic text analysis/mining, large-scale semantic search/indexing, linked data, social semantics, online communitiesMultimedia information retrieval, formal models, mining information from large data sets, event detection, information fusion, machine learningCorrelating/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
ERCIMDCU-
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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
Online Machine learning and data mining
SERENDIPITIPlatform
Planning (City)
Journalist
Police
Use Case Scenarios
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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
Online Machine learning and data mining
SERENDIPITIPlatform
Planning (City)
Journalist
Police
Use Case Scenarios134
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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 FoundationBoards.ieTWS+VEPA/MI/Met
Ken WoodMilek BoverE. AartsCarto RattiOne 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
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Impact• SERENDIPITI : “SEnsoR ENricheD Information Prediction and InTegratIon”
• People - Research - Outreach - Platform
• Large-scale, semantic urban computing