Online event recognition over noisy data streams

Abstract

Composite event recognition (CER) systems process streams of sensor data and infer composite events of interest by means of pattern matching. Data uncertainty is frequent in CER applications and results in erroneous detection. To support streaming applications, we present oPIECbd, an extension of oPIEC with a bounded memory, leveraging interval duration statistics to resolve memory conflicts. oPIECbd may achieve comparable predictive accuracy to batch reasoning, avoiding the prohibitive cost of such reasoning. Furthermore, the use of interval duration statistics allows oPIECbd to outperform significantly earlier versions of bounded oPIEC. The empirical evaluation demonstrates the efficacy of oPIECbd on a benchmark activity recognition dataset, as well as real data streams from the field of maritime situational awareness.