Chaos-based predictive algorithm for continuous aggregate queries over data streams

CSPA (chaotic stream predictive algorithm) is proposed to predict efficiently the prospective aggregate values of the aggregate queries which are continuous and over data streams, based on the theory of chaos. Regarding the data stream as a time series where all the arrival times of data are arranged in order, the prediction of the prospective aggregate values of continuous aggregate queries is discussed in view of the conventional analysis of time series. However, a data stream series differs greatly from conventional time series in both time interval and data set processing, the moving window technique is therefore used for stream processing. In addition, the influence of the complex inherent nonlinear dynamic characteristics in streaming data on the prediction had not been considered in relevant earlier works. So, CSPA makes use of the idea about local prediction included in the theory of chaos to make up for the deficiency. Experimental results showed the high exactness of using the CSPA algorithm.