CML model based max-subset shedding for sensor streams multi-joins under limited resources

Join queries over wireless sensor data streams need to be processed immediately to keep up with the input streams. Many existing algorithms do not solve the problem in context of both limited CPU and memory resources. In this paper, we propose two CML statistic model based approximate sliding window multi-joins algorithms for the system that both CPU and memory is limited, and a maximum subset of the exact multi-join result is obtained. To shed the load effectively and produce as many join results as possible with the limited amount of resources, both the statistical information of join attributes value of each stream and the relationship between CPU and memory resource is completely considered. The CML statistic model is designed for obtaining and maintaining the statistical information dynamically. With the model, semantic load shedding algorithms are proposed over streaming sliding window multi-join under both limited CPU and memory resource. And a maximizing multi-join output can be generated without delay. Experimental results show that our approach is more efficient than other approach when both the CPU and memory resource are insufficient to keep pace with input streams.