This research proposes an early big data reduction framework at the customer end.
The proposed framework enables enterprises to reduce the
cost
of cloud service utilization to perform big data analytics.
In
addition, this framework enables local knowledge availability,
privacy
preservation, and secure data sharing functions to build
trust
between customers and enterprises. In addition, the business
model
blueprint for early data reduction is presented and
the
key components of a few application areas are mapped on the
business
canvas model. Finally, a few challenges relevant to technology
adoption
are
discussed in this study. In the future, we will
develop
a software component-based architecture for the proposed
framework
and will test it for real-world applications to assess the
performance
of the proposed framework and quantify the achieved
levels
of V2C and V2F.