Scheduling for fast response multi-pattern matching over streaming events

Abstract

Real-time pattern matching over event streams has gained much more attention recently due to the analytical capability demanded in many operation-critical applications such as credit card fraud detection, algorithmic stock trading and RFID tracking. One of the common but important requirements in the above-mentioned applications is fast response. Usually, there are a large number of pattern queries subscribed in the system, running continuously and concurrently. However, not much research has been done on the scheduling algorithms and management to improve the overall response time of these queries. To address this challenge, we focus on the study of how to improve the average response time of multiple pattern queries. We first propose two static scheduling algorithms: Event-based (EBS) and Run-based (RBS) Scheduling and discuss what would be a better choice under different system configurations. We then come up with a hybrid method called Fast Response Time Scheduling (FRTS) to dynamically manage the scheduling in order to further reduce the average response time. The experimental results of these scheduling algorithms have shown that the FRTS method can improve 5 times average response time comparing with the basic methods in some cases.