Detection of complex temporal patterns over data streams

Abstract

A growing number of applications require support for processing data that is in the form of continuous stream rather than finite stored data. For instance, network and traffic management, medical monitoring are some of the new applications that continuously examine a sensor stream in order to detect any undesirable behavior of the monitored system that requires further inspection. In this paper we present a new algorithm to detect undesirable system behaviors that are represented by some complex temporal patterns over data streams. Our algorithm efficiently scans the data stream with a sliding window, and checks the data inside the window from right-to-left to see if they satisfy the pattern predicates. By first preprocessing the complex temporal patterns at compile time, it can exploit the interdependencies between the pattern predicates, and skip unnecessary checks with efficient window slides at run time. It resembles the sliding window process of the Boyer–Moore algorithm, although allowing complex predicates that are beyond the scope of this traditional string search algorithm. Implementation and evaluation of the proposed algorithm shows its efficiency when compared to previously proposed approaches.