Load Shedding for Window Aggregation Queries over Sensor Streams Management

Today information society is becoming a knowledge intensive; mining of knowledge is becoming a very significant task for numerous people. The main issue in stream mining is handling a huge amount of data from wireless sensor Network (WSN) is to deliver rapidly which makes it infeasible to store everything in active storage. To overcome this problem of handling voluminous data we exposed a novel load shedding system using window based aggregate function of the data stream in which we accept those tuples in the stream that meet a criterion. Accepted tuples are conceded to another process as a stream, while further tuples are dropped. This proposed model conceivably segregates the data input stream into windows and probabilistically decides which tuple to drop based on the window function. The result shows that the best window aggregate function used for dropping tuples is identified with the three prediction models used in data, from these model naïve Bayes shows Better result than the other two prediction models.