Authors: Zhang, Xin (2006)Events occur in every aspect of our lives. An unexpectedly large number of events occurring within some certain measurement (e.g. within some time du-ration or a spatial region) is called a burst, suggesting unusual behaviors or activities. Bursts come up in many natural and social processes. It is a challenging task to monitor the occurrence of bursts whose lasting duration is unknown in a fast data stream environment.
This work describes efficient data structures and algorithms for high performance burst detection under different settings. Our view is that bursts, as unusual phenomena, constitute a useful preliminary primitive in a knowledge discovery hierarchy. Our intent is to build a high performance primitive detection algorithm to support high-level data mining tasks.