  This study presents an efficient incremental/decremental approach for big
streams based on Kernel Ridge Regression (KRR), a frequently used data analysis
in cloud centers. To avoid reanalyzing the whole dataset whenever sensors
receive new training data, typical incremental KRR used a single-instance
mechanism for updating an existing system. However, this inevitably increased
redundant computational time, not to mention applicability to big streams. To
this end, the proposed mechanism supports incremental/decremental processing
for both single and multiple samples (i.e., batch processing). A large scale of
data can be divided into batches, processed by a machine, without sacrificing
the accuracy. Moreover, incremental/decremental analyses in empirical and
intrinsic space are also proposed in this study to handle different types of
data either with a large number of samples or high feature dimensions, whereas
typical methods focused only on one type. At the end of this study, we further
the proposed mechanism to statistical Kernelized Bayesian Regression, so that
uncertainty modeling with incremental/decremental computation becomes
applicable. Experimental results showed that computational time was
significantly reduced, better than the original nonincremental design and the
typical single incremental method. Furthermore, the accuracy of the proposed
method remained the same as the baselines. This implied that the system
enhanced efficiency without sacrificing the accuracy. These findings proved
that the proposed method was appropriate for variable streaming data analysis,
thereby demonstrating the effectiveness of the proposed method.
