Gloria: Graph-based Sharing Optimizer for Event Trend Aggregation

Large workloads of event trend aggregation queries are widely deployed to derive high-level insights about current event trends in near real time. To speed-up the execution, we identify and leverage sharing opportunities from complex patterns with flat Kleene operators or even nested Kleene expressions. We propose GLORIA, a graph-based sharing optimizer for event trend aggregation. First, we map the sharing optimization problem to a graph path search problem in the GLORIA graph with execution costs encoded as weights. Second, we shrink the search space by applying cost-driven pruning principles that guarantee optimality of the reduced GLORIA graph in most cases. Lastly, we propose a path search algorithm that identifies the sharing plan with minimum execution costs. Our experimental study on three real-world data sets demonstrates that our GLORIA optimizer effectively reduces the search space, leading to 5-fold speed-up in optimization time. The optimized plan consistently reduces the query latency by 68% 93% compared to the plan generated by state-of-the-art approaches.