SERENDIPITI

1. SERENDIPITI SEnsoR ENricheD Information Prediction and InTegratIon

2. 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)

3. Today’s Agenda Vision Objectives Technology Partners

4. 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?

5. 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.

6. What’s New in SERENDIPITI? • Real-time • Prediction (events) • Multimodal • Noisy, errorsome data • Novel applications

7. 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

8. How do we do this? Physical SensorBase Online

9. How do we do this? Physical - Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity SensorBase - Blogs, wikis, web feeds- Tweets, RSS- Event guides Online

10. How do we do this? Track evolution of events over space and time Physical - Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity SensorBase - Blogs, wikis, web feeds- Tweets, RSS- Event guides Online Machine learning and data mining

11. SERENDIPITI Platform How do we do this? Track evolution of events over space and time Physical - Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity SensorBase - Blogs, wikis, web feeds- Tweets, RSS- Event guides Online Machine learning and data mining

12. Use Case Scenarios Planning (City) Journalist Police SERENDIPITI Platform How do we do this? Track evolution of events over space and time Physical - Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity SensorBase - Blogs, wikis, web feeds- Tweets, RSS- Event guides Online Machine learning and data mining

13. 1 2 3 4 5 6 Partners & Roles 0 Patricia Ho-Hune ERCIM DCU-CLARITY DERI GLA QMUL UvA UEP 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

14. 1 2 3 4 5 6 Partners & Roles ERCIM DCU-CLARITY DERI GLA QMUL UvA UEP 0 EU 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

15. PARTNERS UvA DCU QMUL DERI GLA UEP Use Case Scenarios Planning (City) 1 2 Journalist 3 4 Police SERENDIPITI Platform 5 6 Track evolution of events over space and time Physical - Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity SensorBase - Blogs, wikis, web feeds- Tweets, RSS- Event guides Online Machine learning and data mining

16. 1 1 1 2 2 2 3 3 3 3 4 4 4 5 5 5 5 6 6 6 6 PARTNERS UvA DCU QMUL DERI GLA UEP Track evolution of events over space and time Use Case Scenarios Physical 1 Planning (City) - Crowd movements, CCTV- Traffic - pedestrian / vehicular- Bluetooth sensing & proximity 2 3 Journalist SensorBase 4 - Blogs, wikis, web feeds- Tweets, RSS- Event guides 5 Police 6 Online SERENDIPITI Platform Machine learning and data mining

17. 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

18. Wikimedia Foundation Boards.ie TW S+V EPA/MI/Met Ken Wood Milek Bover E. Aarts Carto Ratti One other ! Implementation User Group Data Provision Board Planning (City) Journalist Police Industrial Advisory Board

19. 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

20. Impact • SERENDIPITI : “SEnsoR ENricheD Information Prediction and InTegratIon” • People - Research - Outreach - Platform • Large-scale, semantic urban computing