A dynamic burst detection model over data streams

Burst detection techniques over data streams has been attracting board and home scholars´ more attention due to it´s broad applications in financial, medical service, telecommunication and other critical important areas. In order to detect bursts of positive data streams, negative data streams, first, we propose a dynamic burst detection model over data stream. Based on the model, we embed a two-dimensional array into SAT(shifted aggregation tree), and construct a elastic data structure ASAT given the input. At last, we propose a elastic burst detection algorithm over data streams. The algorithm not only can detect bursts of monotonous accumulation function and non-monotonous accumulation function, but also can search burst on large scale of negative, constant data streams. Experiments show that this algorithm is both efficient and effective.