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» Scaling Clustering Algorithms to Large Databases
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CIKM
2005
Springer
15 years 7 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 3 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 2 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 6 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 1 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