Title: Better Burst Detection


Authors:  Dennis Shasha and Xin Zhang 


A burst is a large number of events occurring within a certain time window. As an unusual activity, it's a noteworthy phenomenon in many natural and social processes. Many data stream applications require the detection of bursts across a variety of window sizes. For example, stock traders may be interested in bursts having to do with institutional purchases or sales that are spread out over minutes or hours. Detecting a burst over any of $k$ window sizes, a problem we call {\em elastic burst detection}, in a stream of length $N$ naively requires $O(kN)$ time. Previous work \cite{DiscoveryBook03} showed that a simple Shifted Binary Tree structure can reduce this time substantially (in very favorable cases near to $O(N)$) by filtering away obvious non-bursts. Unfortunately, for certain data distributions, the filter marks many windows of events as possible bursts, even though a detailed check shows them to be non-bursts.
In this paper, we present a new algorithmic framework for elastic burst detection: a family of data structures that generalizes the Shifted Binary Tree. We then present a heuristic search algorithm to find an efficient structure among the many offered by the framework, given the input. We study how different inputs affect the desired structures. Experiments on both synthetic and real world data show a factor of up to 35 times improvement compared with the Shifted Binary Tree over a wide variety of inputs, depending on the data distribution. We show an example application that identifies interesting correlations between bursts of activity in different stocks.