2017 IEEE 33rd International Conference on Data Engineering (2017)
San Diego, California, USA
April 19, 2017 to April 22, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2017.242
Abstract-Modern world data come from an increasing numberof sources, including data from physical sensors like weathersatellites and seismographs as well as social networks and weblogs. While progress has been made in the filtering of individualsocial networks, there are significant advantages in the integrationof big data from multiple sources. For physical events, theintegration of physical sensors and social network data canimprove filtering efficiency and quality of results beyond whatis feasible in each individual data stream.Disasters are representative physical events with real worldimpact. As illustration and demonstration, we have built theLITMUS landslide information service that combines data fromboth physical sensors and social networks in real-time. LITMUSfilters and combines reliable but indirect physical data with directreport social media data on landslides to achieve high quality andwide coverage of landslide information.
Terrain factors, Sensors, Feeds, Twitter, Information services, Real-time systems
A. Musaev and C. Pu, "Landslide Information Service Based on Composition of Physical and Social Sensors," 2017 IEEE 33rd International Conference on Data Engineering(ICDE), San Diego, California, USA, 2017, pp. 1415-1416.