History is a mirror to the future: Best-effort approximate complex event matching with insufficient resources

Complex event processing (CEP) has proven to be a highly relevant topic in practice. As it is sensitive to both errors in the stream and uncertainty in the pattern, approximate complex event processing (ACEP) is an important direction but has not been adequately studied before. ACEP is costly, and is often performed under insufficient computing resources. We propose an algorithm that learns from the past behavior of ACEP runs, and makes decisions on what to process first in an online manner, so as to maximize the number of full matches found. In addition, we devise effective optimization techniques. Finally, we propose a mechanism that uses reinforcement learning to dynamically update the history structure without incurring much overhead. Put together, these techniques drastically improve the fraction of full matches found in resource constrained environments.