Heavy-tailed probability distributions in the World Wide Web
A practical guide to heavy tails
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Fast, small-space algorithms for approximate histogram maintenance
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Wavelet synopses with error guarantees
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sketch-based change detection: methods, evaluation, and applications
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Online Burst Detection Over High Speed Short Text Streams
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Opt-in detection based on call detail records
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
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Finding bursts in data streams is attracting much attention in research community due to its broad applications. Existing burst detection methods suffer the problems that 1) the parameters of window size and absolute burst threshold, which are hard to be determined a priori, should be given in advance. 2) Only one side bursts, i.e. either increasing or decreasing bursts, can be detected. 3) Bumps, which are changes of aggregation data caused by noises, are often reported as bursts. The disturbance of bumps causes much effort in subsequent exploration of mining results. In this paper, a general burst model is introduced for overcoming above three problems. We develop an efficient algorithm for detecting adaptive aggregation bursts in a data stream given a burst ratio. With the help of a novel inverted histogram, the statistical summary is compressed to be fit in limited main memory, so that bursts on windows of any length can be detected accurately and efficiently on-line. Theoretical analysis show the space and time complexity bound of this method is relatively good, while experimental results depict the applicability and efficiency of our algorithm in different application settings.