Beyond artificial reality: finding and monitoring live events from social sensors
| Autor: | Calton Pu, João Eduardo Ferreira, Danesh Irani, Rodrigo Alves Lima, De Wang, Steve Webb, Abhijit Suprem, Aibek Musaev |
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| Rok vydání: | 2020 |
| Předmět: |
Concept drift
Artificial reality Computer Networks and Communications Computer science business.industry Big data 02 engineering and technology Knowledge acquisition Litmus Data science SISTEMAS DE INFORMAÇÃO 020204 information systems 0202 electrical engineering electronic engineering information engineering Information system 020201 artificial intelligence & image processing Social media Noise (video) business |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | With billions of active social media accounts and millions of live video cameras, live new big data offer many opportunities for smart applications. However, the main consumers of the new big data have been humans. We envision the research on live knowledge , to automatically acquire real-time, validated, and actionable information. Live knowledge presents two significant and diverging technical challenges: big noise and concept drift. We describe the EBKA (evidence-based knowledge acquisition) approach, illustrated by the LITMUS landslide information system. LITMUS achieves both high accuracy and wide coverage, demonstrating the feasibility and promise of EBKA approach to achieve live knowledge. |
| Databáze: | OpenAIRE |
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