Efficient Pattern Matching over Out-of-Order Event Streams Using Sliding Buffer

Abstract

Pattern matching over event streams has been widely used in many modern applications. A key technology tailored for pattern matching in streaming settings is Complex Event Processing (CEP). The most common CEP-based systems assume that input events arrive in total order based on their occurrence timestamps. However, this assumption fails in real-world applications for several reasons, such as network latency, machine failure, causing out-of-order events. Consequently, the current CEP systems face challenges to ensure accuracy and timeliness in pattern matching. To solve this problem, a number of out-of-order event stream handling approaches have been proposed. This paper introduces a new approach to out-of-order event stream handling for nondeterministic finite automaton (NFA)-based pattern matching. The key idea is to employ a sliding buffer structure that utilizes the sliding window concept and temporal indexing to incrementally evaluate the predefined pattern over streams. Further, we present a new matching algorithm that supports an invalidation mechanism for incorrect prior pattern matches affected by out-of-order events. We conduct extensive experimentation with synthetic and real-world datasets, and the results confirm that our proposed approach can deal with out-of-order event streams in pattern matching better than the state-of-the-art approach, especially in computational latency.