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» TCAM-conscious Algorithms for Data Streams
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SIGMOD
2005
ACM
162views Database» more  SIGMOD 2005»
14 years 9 months ago
Fast and Approximate Stream Mining of Quantiles and Frequencies Using Graphics Processors
We present algorithms for fast quantile and frequency estimation in large data streams using graphics processor units (GPUs). We exploit the high computational power and memory ba...
Naga K. Govindaraju, Nikunj Raghuvanshi, Dinesh Ma...
IPPS
2006
IEEE
14 years 3 months ago
Supporting self-adaptation in streaming data mining applications
There are many application classes where the users are flexible with respect to the output quality. At the same time, there are other constraints, such as the need for real-time ...
Liang Chen, Gagan Agrawal
IWNAS
2008
IEEE
14 years 3 months ago
Storage Aware Resource Allocation for Grid Data Streaming Pipelines
Data streaming applications, usually composed with sequential/parallel tasks in a data pipeline form, bring new challenges to task scheduling and resource allocation in grid envir...
Wen Zhang, Junwei Cao, Yisheng Zhong, Lianchen Liu...
CIS
2004
Springer
14 years 2 months ago
Knowledge Maintenance on Data Streams with Concept Drifting
Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning ap...
Juggapong Natwichai, Xue Li
DASFAA
2008
IEEE
149views Database» more  DASFAA 2008»
13 years 10 months ago
A Test Paradigm for Detecting Changes in Transactional Data Streams
A pattern is considered useful if it can be used to help a person to achieve his goal. Mining data streams for useful patterns is important in many applications. However, data stre...
Willie Ng, Manoranjan Dash