Complex Temporal Patterns Detection over Continuous 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. In this paper we present a new approach for detecting temporal patterns with com- plex predicates over continuous data stream. Our algorithm efficiently scans the 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 their predicates in- terdependency, 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 tradi- tional string search algorithm. Some preliminary evaluation of our proposed al- gorithm shows its efficiency when compared to the naive approach.