Pipelined Implementation of a Parallel Streaming Method for Time Series Correlation Discovery on Sliding Windows

摘要

This paper addresses the problem of continuously finding highly correlated pairs of time series over the most recent time window. The solution builds upon the ParCorr parallel method for online correlation discovery and is designed to run continuously on top of the UPM-CEP data streaming engine through efficient streaming operators. The implementation takes advantage of the flexible API of the streaming engine that provides low level primitives for developing custom operators. Thus, each operator is implemented to process incoming tuples on-the-fly and hence emit resulting tuples as early as possible. This guarantees a real pipelined flow of data that allows for outputting early results, as the experimental evaluation shows.