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» Scaling Clustering Algorithms to Large Databases
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CIKM
2005
Springer
15 years 11 months ago
Query workload-aware overlay construction using histograms
Peer-to-peer (p2p) systems offer an efficient means of data sharing among a dynamically changing set of a large number of autonomous nodes. Each node in a p2p system is connected...
Georgia Koloniari, Yannis Petrakis, Evaggelia Pito...
ICDE
2000
IEEE
112views Database» more  ICDE 2000»
16 years 7 months ago
DEMON: Mining and Monitoring Evolving Data
Data mining algorithms have been the focus of much research recently. In practice, the input data to a data mining process resides in a large data warehouse whose data is kept up-...
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishn...
WWW
2006
ACM
16 years 6 months ago
A pruning-based approach for supporting Top-K join queries
An important issue arising from large scale data integration is how to efficiently select the top-K ranking answers from multiple sources while minimizing the transmission cost. T...
Jie Liu, Liang Feng, Yunpeng Xing
ICCV
2009
IEEE
1176views Computer Vision» more  ICCV 2009»
16 years 11 months ago
Building Rome in a Day
We present a system that can match and reconstruct 3D scenes from extremely large collections of photographs such as those found by searching for a given city (e.g., Rome) on In...
Sameer Agarwal, Noah Snavely, Ian Simon, Steven M....
EDBT
2008
ACM
154views Database» more  EDBT 2008»
16 years 6 months ago
Data utility and privacy protection trade-off in k-anonymisation
K-anonymisation is an approach to protecting privacy contained within a dataset. A good k-anonymisation algorithm should anonymise a dataset in such a way that private information...
Grigorios Loukides, Jianhua Shao