Research Of Auto-adapted Load Shedding Algorithm On Data Stream Inquires Continuously

The method of auto-adapted load shedding on data stream inquires continuously is becoming one of the core contents in the data stream research area in recent years. In this aspect,some scholars of the domestic and foreign have already done the massive research and experimental work,as well as obtained some research results,but also had some new problems.For example,at present,data stream inquiry processing technology main consideration sliding window data,but these research work all based on sliding window which size fixed invariable,however,it will lead to the memory resources wasted, and the inquiry result can't match the accuracyin many practical applications.these methods of load shedding which based on the window operation were mainly researched on the limitation of main memory. Need to study the issues of the computing power in CPU.Firstly,in this essay ,based on the analysis and research of inquires continuously and load shedding to the data stream combined with related technology,a method of data stream inquires continuously which based on the variable size windows was proposed.That is putting a window controller in the cache,to achieve the variable window technology,When the memory usage is not full.According to the user's questions and the speed of data stream flow, The assignment operators will change the data streams'size in term of time-lists. at one time,the Matching operator carry through similar pattern matching,to make it correspond with the query characteristics The method solves the issues of memory resources wasted and the accuracy of query result.Secondly,for the sake of solved the CPU overload situation.In this essay,an original method by combine some basic windows with more larger output rate which are included in the sliding windows on the adjacent over data stream can easily solved the problem.Finally,application has been conducted.Pre-processing technology play a very important role in data stream mining,it can improve the quality of data stream and effectively prevent data stream overloading,it also help to improve the precision and performance in the process of mining.In this essay, based on the characteristics of the data streams,use the experience of the preprocessing methods of static data sets.A method was proposed to reprocessing the data stream which have gotten by sampling method and sliding window model method.This method will reduce the spatial complexity and temporal complexity.