Abstract
It is one of the major tasks to execute query timely with less performance and precise loss in a data stream system when the system resource is limited.This paper solved this problem from two aspects including optimizing operator schedule and performing load shedding.Taking different operators' features into consideration,a scheduling strategy based on operator priority was presented,which comprehensively considered the factors related to the operators and the system running state.In order to dynamically modify the operator priority,the artificial neural network learning algorithm was also introduced,which can modify operator priority according to the system performance.Aiming to solve the potential overload problems caused by the uncertainty of the arrived data in a data stream management system,the load shedding issue of the data stream system was researched.Concerning the query of the two streams' joint operators,a semantic-based load shedding technique was applied.A data stream load shedding model was designed and implemented,which solved four problems including load shedding and anti-shedding time,amount,location and predicate.The experiment result was analyzed,which showed that the load shedding model presented can effectively avoid the low processing efficiency when system is in the state of overload,and guarantee the coordination of arrived data and system processing capability.
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