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SERENDIPITI SEnsoR ENricheD Information Prediction and InTegratIon 1 Serendipity n. /ˌsɛ.rɛn.ˈdɪp.ə.ti / Scoperta (Italian) Serendipiteit (Dutch) Vrozené štěstí (Czech) the effect by which one accidentally discovers something fortunate, especially while looking for something entirely unrelated. (wikipedia.org) 2 Today’s Agenda Vision Objectives Technology Partners 3 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? 4 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. 5 What’s New in SERENDIPITI? • Real-time • Prediction (events) • Multimodal • Noisy, errorsome data • Novel applications 6 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 7 How do we do this? Physical SensorBase Online 8 How do we do this? Physical - Crowd movements, CCTV - Traffic - pedestrian / vehicular - Bluetooth sensing & proximity - Blogs, wikis, web feeds - Tweets, RSS - Event guides SensorBase Online 9 How do we do this? Physical Track evolution of events over space and time - Crowd movements, CCTV - Traffic - pedestrian / vehicular - Bluetooth sensing & proximity - Blogs, wikis, web feeds - Tweets, RSS - Event guides Online SensorBase Machine learning and data mining 10 How do we do this? Physical Track evolution of events over space and time - Crowd movements, CCTV - Traffic - pedestrian / vehicular - Bluetooth sensing & proximity - Blogs, wikis, web feeds - Tweets, RSS - Event guides Online SensorBase Machine learning and data mining SERENDIPITI Platform 11 How do we do this? Physical Track evolution of events over space and time Use Case Scenarios Planning (City) - Crowd movements, CCTV - Traffic - pedestrian / vehicular - Bluetooth sensing & proximity - Blogs, wikis, web feeds - Tweets, RSS - Event guides Online Journalist SensorBase Police Machine learning and data mining SERENDIPITI Platform 12 Partners & Roles 0 ERCIM Patricia Ho-Hune 1 DCU-CLARITY Alan Smeaton, Noel O’Connor, Barry Smyth 2 DERI Giovanni Tummarello, John Breslin, Paul Buitelaar 3 GLA Keith van Rijsbergen, Joemon Jose, Mark Girolami 4 QMUL Ebroul Izquierdo 5 UvA Maarten de Rijke, Arnold Smeulders 6 UEP Vojtech Svatek 13 Partners & Roles 0 ERCIM EU Project Management 1 DCU-CLARITY Analysis of multimodal sensor data, real-time integration of physical & online sources, organisation & management of online sources 2 DERI Semantic text analysis/mining, large-scale semantic search/indexing, linked data, social semantics, online communities 3 GLA Multimedia information retrieval, formal models, mining information from large data sets, event detection, information fusion, machine learning 4 QMUL Correlating/mining media and textual data, sensor base, automatic (CCTV-based) event analysis 5 UvA Focused crawling, wrapper induction, mining social media, information extraction, data integration, crossmedia mining and information fusion, machine learning 6 UEP Mining rich associations from large databases, information extraction from the Web, semantic and social 14 Web technology, effectiveness of ICT PARTNERS 1 DCU 2 DERI Physical 3 GLA 4 QMUL Track evolution of events over space and time 5 UvA 6 UEP Use Case Scenarios Planning (City) - Crowd movements, CCTV - Traffic - pedestrian / vehicular - Bluetooth sensing & proximity - Blogs, wikis, web feeds - Tweets, RSS - Event guides Online Journalist SensorBase Police Machine learning and data mining SERENDIPITI Platform 15 PARTNERS 1 Physical DCU 2 DERI 1 4 3 - Bluetooth sensing & proximity Online 5 1 2 6 GLA 4 QMUL Track evolution of events over space and time - Crowd movements, CCTV - Traffic - pedestrian / vehicular 5 - Blogs, wikis, web feeds - Tweets, RSS - Event guides 3 5 UvA UEP Use Case Scenarios 3 2 6 4 SensorBase 6 5 3 Machine learning and data mining 6 SERENDIPITI Platform 1 Planning (City) 2 3 Journalist 4 5 Police 6 16 Work Packages • WP1 – Management • WP2 – Integration of Organisation & People Joint Program of Activities • WP3 – Real-time Large-scale Data Analysis • WP4 – Semantic Information Fusion • WP5 – Inference & Event Prediction • WP6 – Applications & Infrastructure Sharing • WP7 – Outreach (Spreading Excellence) 17 Implementation User Group Planning (City) Journalist Police Data Provision Board Wikimedia Foundation Boards.ie TW S+V EPA/MI/Met Industrial Advisory Board Ken Wood Milek Bover E. Aarts Carto Ratti One other ! 18 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 19 Impact • SERENDIPITI : “SEnsoR ENricheD Information Prediction and InTegratIon” • People - Research - Outreach - Platform • Large-scale, semantic urban computing 20