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KDD
2009
ACM
198views Data Mining» more  KDD 2009»
14 years 8 months ago
Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data
All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a coll...
Srivatsava Daruru, Nena M. Marin, Matt Walker, Joy...
AOSD
2007
ACM
13 years 11 months ago
Generating parallel applications for distributed memory systems using aspects, components, and patterns
Developing and debugging parallel programs particularly for distributed memory architectures is still a difficult task. The most popular approach to developing parallel programs f...
Purushotham V. Bangalore
ICCS
2007
Springer
14 years 1 months ago
DDDAS/ITR: A Data Mining and Exploration Middleware for Grid and Distributed Computing
We describe our project that marries data mining together with Grid computing. Specifically, we focus on one data mining application - the Minnesota Intrusion Detection System (MIN...
Jon B. Weissman, Vipin Kumar, Varun Chandola, Eric...
IPPS
1999
IEEE
13 years 11 months ago
Parallel Out-of-Core Divide-and-Conquer Techniques with Application to Classification Trees
Classification is an important problem in the field of data mining. Construction of good classifiers is computationally intensive and offers plenty of scope for parallelization. D...
Mahesh K. Sreenivas, Khaled Alsabti, Sanjay Ranka
CLUSTER
2008
IEEE
14 years 1 months ago
Enabling lock-free concurrent fine-grain access to massive distributed data: Application to supernovae detection
—We consider the problem of efficiently managing massive data in a large-scale distributed environment. We consider data strings of size in the order of Terabytes, shared and ac...
Bogdan Nicolae, Gabriel Antoniu, Luc Bougé