EFFICIENT LOAD SHEDDING FOR STREAMING SLIDING WINDOW JOINS

摘要：

We present a novel load shedding technique, called Range Loading Shedding (denoted as RANGE), for sliding window joins when CPU capacity is insufficient in the system and the details of the distribution of streams are unknown.To obtain the statistics of data, we dynamically maintain Clustering Range Histogram (CR-Histogram) and Average Density Counter table (ADC-table) for each sliding window.The CR-Histogram is constructed and maintained by clustering technique with a fixed amount of memory.When CPU capacity is insufficient, the RANGE technique is used to select tuples to be processed by utilizing the CR-Histogram and ADC-table, and then produces maximum subset join outputs.Experimental results on synthetic and real life data show that Range load shedding approach obtains Max-subset results effectively, and outperforms the existing load shedding strategies.

关键词： Data streams Load shedding Sliding window join Histogram Clustering technique

作者: JIA-DONG REN WAN-CHANG JIANG CONG HUO

作者单位: College of Information Science and Engineering, YanShan University, Qinhuangdao 066004, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 1536-1541

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）