  With the emerging technologies and all associated devices, it is predicted
that massive amount of data will be created in the next few years, in fact, as
much as 90% of current data were created in the last couple of years,a trend
that will continue for the foreseeable future. Sustainable computing studies
the process by which computer engineer/scientist designs computers and
associated subsystems efficiently and effectively with minimal impact on the
environment. However, current intelligent machine-learning systems are
performance driven, the focus is on the predictive/classification accuracy,
based on known properties learned from the training samples. For instance, most
machine-learning-based nonparametric models are known to require high
computational cost in order to find the global optima. With the learning task
in a large dataset, the number of hidden nodes within the network will
therefore increase significantly, which eventually leads to an exponential rise
in computational complexity. This paper thus reviews the theoretical and
experimental data-modeling literature, in large-scale data-intensive fields,
relating to: (1) model efficiency, including computational requirements in
learning, and data-intensive areas structure and design, and introduces (2) new
algorithmic approaches with the least memory requirements and processing to
minimize computational cost, while maintaining/improving its
predictive/classification accuracy and stability.
