Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
Ying Yan , Technology Lab, China, The Office of Chief Scientist, SAP
Jin Zhang , Technology Lab, China, The Office of Chief Scientist, SAP
Ming-Chien Shan , Technology Lab, China, The Office of Chief Scientist, SAP
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.
Ying Yan, Jin Zhang, Ming-Chien Shan, "Scheduling for fast response multi-pattern matching over streaming events", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 89-100, doi:10.1109/ICDE.2010.5447900