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» Very Large Databases: How Large, How Different
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WWW
2009
ACM
14 years 9 months ago
Towards context-aware search by learning a very large variable length hidden markov model from search logs
Capturing the context of a user's query from the previous queries and clicks in the same session may help understand the user's information need. A context-aware approac...
Huanhuan Cao, Daxin Jiang, Jian Pei, Enhong Chen, ...
AMR
2007
Springer
197views Multimedia» more  AMR 2007»
14 years 2 months ago
How to Use SIFT Vectors to Analyze an Image with Database Templates
During last years, local image descriptors have received much attention because of their efficiency for several computer vision tasks such as image retrieval, image comparison, fea...
Adrien Auclair, Laurent D. Cohen, Nicole Vincent
DSRT
2000
IEEE
14 years 29 days ago
Scheduling for Very Large Virtual Environments and Networked Games Using Visibility and Priorities
The problem of network bundwidth limitutions is encountered in ulmost uny distributed virtuul environment or networkedgume. In U typicul client-serversetup, where the virtuul worl...
Chris Faisstnauer, Dieter Schmalstieg, Werner Purg...
KDD
2006
ACM
160views Data Mining» more  KDD 2006»
14 years 9 months ago
Coherent closed quasi-clique discovery from large dense graph databases
Frequent coherent subgraphscan provide valuable knowledgeabout the underlying internal structure of a graph database, and mining frequently occurring coherent subgraphs from large...
Zhiping Zeng, Jianyong Wang, Lizhu Zhou, George Ka...
SIGMOD
2001
ACM
184views Database» more  SIGMOD 2001»
14 years 8 months ago
Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases
Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because of the typically high dimensionality of the data....
Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehro...