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AUSDM
2007
Springer
145views Data Mining» more  AUSDM 2007»
14 years 1 months ago
Discovering Frequent Sets from Data Streams with CPU Constraint
Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredictable rates, and fast changing data characteristics. It has been hence recogniz...
Xuan Hong Dang, Wee Keong Ng, Kok-Leong Ong, Vince...
ICALP
1994
Springer
13 years 11 months ago
Simple Fast Parallel Hashing
A hash table is a representation of a set in a linear size data structure that supports constanttime membership queries. We show how to construct a hash table for any given set of...
Joseph Gil, Yossi Matias
BTW
2011
Springer
218views Database» more  BTW 2011»
12 years 11 months ago
Tracking Hot-k Items over Web 2.0 Streams
Abstract: The rise of the Web 2.0 has made content publishing easier than ever. Yesterday’s passive consumers are now active users who generate and contribute new data to the web...
Parisa Haghani, Sebastian Michel, Karl Aberer
DATAMINE
2006
230views more  DATAMINE 2006»
13 years 7 months ago
Mining top-K frequent itemsets from data streams
Frequent pattern mining on data streams is of interest recently. However, it is not easy for users to determine a proper frequency threshold. It is more reasonable to ask users to ...
Raymond Chi-Wing Wong, Ada Wai-Chee Fu
CIDR
2009
157views Algorithms» more  CIDR 2009»
13 years 8 months ago
Capturing Data Uncertainty in High-Volume Stream Processing
We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuse...
Yanlei Diao, Boduo Li, Anna Liu, Liping Peng, Char...