Database Reference
In-Depth Information
1. Introduction
The proliferation of numerous online social networks such as
Facebook
,
LinkedIn
and
Google+
has lead to an increased awareness of the power of
incorporating social elements into a variety of data-centric applications.
Such networks are typically
data rich
, and contain heterogeneous data
along with linkage stricture, which can be mined for a variety of purposes
[39, 98, 108]. In particular, it has been observed that the use of a
combination of social structure and different kinds of data can be a very
powerful tool for mining purposes [136, 175, 182]. A natural way to
enhance the power of such social applications is to embed sensors within
such platforms in order to continuously collect large amounts of data for
prediction and monitoring applications. This has lead to the creation
of numerous social sensing systems such as
Biketastic
[142],
BikeNet
[55],
CarTel
[88] and
Pier
[148], which use social sensors for a variety of
transportation and personal applications. The fusion of mobile, social,
and sensor data is now increasingly being seen as a tool to fully enable
context-aware computing [20].
A number of recent hardware platforms have extended the data-centric
capabilities of social networks, by providing the ability to embed sensor
data collection directly into the social network. Therefore, it is natu-
ral to explore whether sensor data processing can be tightly integrated
with social network construction and analysis. For example, methods
such a crowd-sourcing are a natural approach for improving the ac-
curacy of many socially-aware search applications [168]. Some of the
afore-mentioned data types on a conventional social network are static
and change slowly over time. On the other hand, sensors collect vast
amounts of data which need to be stored and processed in real time.
There are a couple of important drivers for integrating sensor and social
networks:
One driver for integrating sensors and social networks is to allow
the actors in the social network to both publish their data and
subscribe to each other's data either directly, or indirectly after
discovery of useful information from such data. The idea is that
such collaborative sharing on a social network can increase real-
time awareness of different users about each other, and provide un-
precedented information and understanding about global behavior
of different actors in the social network. The vision of integrating
sensor processing with the real world was first proposed in [177].
A second driver for integrating sensors and social networks is to
provide a better understanding and measurement of the aggre-
