Beyond Artificial Reality: Finding and Monitoring Live Events from Social Sensors
ACM Transactions on Internet Technology, ISSN: 1557-6051, Vol: 20, Issue: 1, Page: 1-21
2020
- 5Citations
- 33Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
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.
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