document: Efficient elastic burst detection in data streams Burst detection is the activity of finding abnormal aggregates in data streams . Such aggregates are based on sliding windows over data streams . In some applications , we want to monitor many sliding window sizes simultaneously and to report those windows with aggregates significantly different from other periods . We will present a general data structure for detecting interesting aggregates over such elastic windows in near linear time . We present applications of the algorithm for detecting Gamma Ray Bursts in large-scale astrophysical data . Detection of periods with high volumes of trading activities and high stock price volatility is also demonstrated using real time Trade and Quote ( TAQ ) data from the New York Stock Exchange ( NYSE ) . Our algorithm beats the direct computation approach by several orders of magnitude .
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