  Nonparametric correlations such as Spearman's rank correlation and Kendall's
tau correlation are widely applied in scientific and engineering fields. This
paper investigates the problem of computing nonparametric correlations on the
fly for streaming data. Standard batch algorithms are generally too slow to
handle real-world big data applications. They also require too much memory
because all the data need to be stored in the memory before processing. This
paper proposes a novel online algorithm for computing nonparametric
correlations. The algorithm has O(1) time complexity and O(1) memory cost and
is quite suitable for edge devices, where only limited memory and processing
power are available. You can seek a balance between speed and accuracy by
changing the number of cutpoints specified in the algorithm. The online
algorithm can compute the nonparametric correlations 10 to 1,000 times faster
than the corresponding batch algorithm, and it can compute them based either on
all past observations or on fixed-size sliding windows.
