Presently existing correlation analysis method for multiple data streams were all oriented single dimensions data streams only

which could not identify the real correlation between fields built by multiple variables.To quickly detect correlations between two multiple dimension data streams under constrained resources

a novel correlation analysis algorithm based on canonical correlation analysis (CCA)

called StreamCCA

is proposed.Focusing on the computational bottleneck of traditional CCA

StreamCCA introduces a low-rank approximation technique to reduce the dimensionality of product matrix resulted from sample correlation matrix and sample variance matrix

which improves computational performance efficiently on the premise of holding approximate precision.Theoretic analysis and experiments results on synthetic and real data sets indicate that StreamCCA can online detect correlations between multiple dimension data streams accurately.The algorithms proposed herein

are presented as generic forecasting and diagnosis tools

with a multitude of applications on data streams mining problems.