Fast approximate correlation for massive time-series data

We consider the problem of computing all-pair correlations in a warehouse containing a large number (e.g., tens of thousands) of time-series (or, signals). The problem arises in automatic discovery of patterns and anomalies in data intensive applications such as data center management, environmental monitoring, and scientific experiments. However, with existing techniques, solving the problem for a large stream warehouse is extremely expensive, due to the problem's inherent quadratic I/O and CPU complexities.