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» Online Data Mining for Co-Evolving Time Sequences
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KDD
2009
ACM
217views Data Mining» more  KDD 2009»
15 years 10 months ago
Efficient anomaly monitoring over moving object trajectory streams
Lately there exist increasing demands for online abnormality monitoring over trajectory streams, which are obtained from moving object tracking devices. This problem is challengin...
Yingyi Bu, Lei Chen 0002, Ada Wai-Chee Fu, Dawei L...
JCST
2008
121views more  JCST 2008»
14 years 10 months ago
Clustering Text Data Streams
Abstract Clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organiza...
Yubao Liu, Jiarong Cai, Jian Yin, Ada Wai-Chee Fu
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
15 years 10 months ago
Analyzing patterns of user content generation in online social networks
Various online social networks (OSNs) have been developed rapidly on the Internet. Researchers have analyzed different properties of such OSNs, mainly focusing on the formation an...
Lei Guo, Enhua Tan, Songqing Chen, Xiaodong Zhang,...
ICPP
1999
IEEE
15 years 2 months ago
Improving Performance of Load-Store Sequences for Transaction Processing Workloads on Multiprocessors
On-line transaction processing exhibits poor memory behavior in high-end multiprocessor servers because of complex sharing patterns and substantial interaction between the databas...
Jim Nilsson, Fredrik Dahlgren
KDD
2007
ACM
182views Data Mining» more  KDD 2007»
15 years 10 months ago
A fast algorithm for finding frequent episodes in event streams
Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the...
Srivatsan Laxman, P. S. Sastry, K. P. Unnikrishnan