Online Burst Detection Over High Speed Short Text Streams
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Mining Top-n Local Outliers in Constrained Spatial Networks
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Adaptive burst detection in a stream engine
Proceedings of the 2009 ACM symposium on Applied Computing
Topic dynamics: an alternative model of bursts in streams of topics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Identification, Modelling and Prediction of Non-periodic Bursts in Workloads
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
A web personalizing technique using adaptive data structures: The case of bursts in web visits
Journal of Systems and Software
Finding critical thresholds for defining bursts
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
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A burst is a large number of events occurring within a certain time window. 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. In this paper, we present a new algorithmic framework for elastic burst detection [1]: a family of data structures that generalizes the Shifted Binary Tree, and a heuristic search algorithm to find an efficient structure given the input. We study how different inputs affect the desired structures and the probability to trigger a detailed search. 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 inputs.