| Abstract |
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Real-time network and telecommunication systems
often generate tremendous volume of streaming data.
Effective modeling of such streaming data and
detecting the bursts with single-scan algorithms pose
great challenges. The aim of detecting bursts in data
streams is to find anomalous aggregation in stream
subsequences. We introduce Lasting Factor and
Abrupt Factor in the general definition of burst, in
order to characterize how a burst grows in real
applications. A novel two-layered wavelet tree
structure is designed to detect lasting bursts and
abrupt bursts in linear time. Our algorithm reports
appearance time range and average aggregate value
for lasting bursts, break point position and peak value
for abrupt bursts. Theoretical analysis and comparison
experiments on the Internet Traffic Archive dataset
verify the superiority of our approach over other burst
detection algorithms in burst characterization and
computation efficiency.
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 Additional Information
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Citation:
Tingting Chen, Yi Wang, Binxing Fang, Jun Zheng,
"Detecting Lasting and Abrupt Bursts in Data Streams Using Two-Layered Wavelet Tree,"
aict-iciw,
p. 30,
Advanced International Conference on Telecommunications and International Conference on Internet and Web Applications and Services (AICT-ICIW'06),
2006
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