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» Distance Functions to Detect Changes in Data Streams
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ISBI
2006
IEEE
14 years 8 months ago
Landmark matching on the sphere using distance functions
Nonlinear registration of 3D surfaces is important in many medical imaging applications, including the mapping of longitudinal changes in anatomy, or of multi-subject functional M...
Natasha Lepore, Alex D. Leow, Paul M. Thompson
SIGKDD
2008
113views more  SIGKDD 2008»
13 years 7 months ago
On exploiting the power of time in data mining
We introduce the new paradigm of Change Mining as data mining over a volatile, evolving world with the objective of understanding change. While there is much work on incremental m...
Mirko Böttcher, Frank Höppner, Myra Spil...
DASFAA
2005
IEEE
157views Database» more  DASFAA 2005»
14 years 1 months ago
Adaptively Detecting Aggregation Bursts in Data Streams
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 p...
Aoying Zhou, Shouke Qin, Weining Qian
EDBT
2009
ACM
166views Database» more  EDBT 2009»
14 years 23 hour ago
Neighbor-based pattern detection for windows over streaming data
The discovery of complex patterns such as clusters, outliers, and associations from huge volumes of streaming data has been recognized as critical for many domains. However, patte...
Di Yang, Elke A. Rundensteiner, Matthew O. Ward
VLDB
2005
ACM
87views Database» more  VLDB 2005»
14 years 26 days ago
NILE-PDT: A Phenomenon Detection and Tracking Framework for Data Stream Management Systems
In this demo, we present Nile-PDT, a Phenomenon Detection and Tracking framework using the Nile data stream management system. A phenomenon is characterized by a group of streams ...
Mohamed H. Ali, Walid G. Aref, Raja Bose, Ahmed K....