Active complex event processing over event streams

Abstract

State-of-the-art Complex Event Processing technology (CEP), while effective for pattern query execution, is limited in its capability of reacting to opportunities and risks detected by pattern queries. Especially reactions that affect the query results in turn have not been addressed in the literature. We propose to tackle these unsolved problems by embedding active rule support within the CEP engine, henceforth called Active CEP (ACEP). Active rules in ACEP allow us to specify a pattern query's dynamic condition and real-time actions. The technical challenge is to handle interactions between queries and reactions to queries in the high-volume stream execution. We hence introduce a novel stream-oriented transactional model along with a family of stream transaction scheduling algorithms that ensure the correctness of concurrent stream execution. We demonstrate the power of ACEP technology by applying it to the development of a healthcare system being deployed in UMass Medical School hospital. Through extensive performance experiments using real data streams, we show that our unique Low-Water-Mark stream transaction scheduler, customized for streaming environments, successfully achieves near-real-time system responsiveness and gives orders-of-magnitude better throughput than our alternative schedulers.