Shun Hattori
Granularity Analysis for SpatioTemporal Web Sensors
256 - 264
2013
7
2
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/831
https://publications.waset.org/vol/74
World Academy of Science, Engineering and Technology
In recent years, many researches to mine the exploding Web world, especially User Generated Content (UGC) such as
weblogs, for knowledge about various phenomena and events in the physical world have been done actively, and also Web services
with the Webmined knowledge have begun to be developed for
the public. However, there are few detailed investigations on how accurately Webmined data reflect physicalworld data. It must be
problematic to idolatrously utilize the Webmined data in public Web services without ensuring their accuracy sufficiently. Therefore,
this paper introduces the simplest Web Sensor and spatiotemporallynormalized
Web Sensor to extract spatiotemporal data about a target
phenomenon from weblogs searched by keyword(s) representing the
target phenomenon, and tries to validate the potential and reliability of the Websensed spatiotemporal data by four kinds of granularity
analyses of coefficient correlation with temperature, rainfall, snowfall,
and earthquake statistics per day by region of Japan Meteorological
Agency as physicalworld data spatial granularity (regions population
density), temporal granularity (time period, e.g., per day vs. per week), representation granularity (e.g., “rain" vs. “heavy rain"), and
media granularity (weblogs vs. microblogs such as Tweets).
Open Science Index 74, 2013