On-Demand Pattern Aggregation in Event Networks

Steven Purtzel, Samira Akili, Matthias Weidlich

Abstract: Complex Event Processing (CEP) detects patterns in event streams based on predefined queries. However, CEP applications often do not require the enumeration of all detected patterns, but the computation of an aggregate over them, which enables more efficient processing. Yet, existing optimizations for the computation of aggregates over the matches of pattern queries are limited to centralized settings that assume that events are processed at a single location.In this paper, we present Pattern Aggregation (PA) plans as a model for on-demand computation of aggregates over patterns that are detected in networks of event sources. The model combines the distributed evaluation of pattern queries with a distributed computation of an aggregate over them. We introduce PA plans along with a cost model for the induced communication overhead and an algorithm for the construction of efficient plans. We demonstrate the feasibility and efficiency of our approach in a series of experiments.