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Towards Social Media as a Data Source for Opportunistic Sensor Networking
Meneghello, J., Lee, K. and Thompson, N.
The quality and diversity of available data sources
has a large impact on the potential for sensor
networks to support rich applications. The high cost
and narrow focus of new sensor network
deployments has led to a search for diverse, global
data sources to support more varied sensor network
applications. Social networks are culturally and
geographically diverse, and consist of large amounts
of rich data from users. This provides a unique
opportunity for existing social networks to be
leveraged as data sources. Using social media as a
data source poses signficant challenges. These
include the large volume of available data, the
associated difficulty in isolating relevant data
sources and the lack of a universal data format for
social networks. Integrating social and other data
sources for use in sensor networking applications
requires a cohesive framework, including data
sourcing, collection, cleaning, integration,
aggregation and querying techniques. While similar
frameworks exist, they require long-term collection
of all social media data for aggregation, requiring
large infrastructure outlays. This paper presents a
novel framework which is able to source social data,
integrate it into a common format and perform
querying operations without the high level of
resource requirements of existing solutions.
Framework components are fully extensible, allowing
for the addition of new data sources as well as the
extension of query functionality to support sensor
networking applications. This framework provides a
consistent, reliable querying interface to existing
social media assets for use in sensor networking
applications and experiments - without the cost or
complexity of establishing new sensor network
deployments. |
Cite as: Meneghello, J., Lee, K. and Thompson, N. (2014). Towards Social Media as a Data Source for Opportunistic Sensor Networking. In Proc. Twelfth Australasian Data Mining Conference (AusDM14) Brisbane, Australia. CRPIT, 158. Li, X., Liu, L., Ong, K.L. and Zhao, Y. Eds., ACS. 183-194 |
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