NIMBLE: A toolkit for the implementation of parallel data mining and machine learning algorithms on mapReduce

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2011, Pages 334-342

  Ghoting, Amol ^a   Kambadur, Prabhanjan ^a   Pednault, Edwin ^a   Kannan, Ramakrishnan ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Data mining; Machine learning; MapReduce; Tasks

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; DIGITAL STORAGE; LEARNING ALGORITHMS; LEARNING SYSTEMS; MESSAGE PASSING; OPEN SOURCE SOFTWARE; PARALLEL PROGRAMMING;

DATA MINING ALGORITHM; LARGE-SCALES; MACHINE DATA; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; MAP-REDUCE; PARALLEL DATA MINING; PARALLEL MACHINE; PROGRAMMING PARADIGMS; TASK;

MAPREDUCE;

EID: 80052651384     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020408.2020464     Document Type: Conference Paper

References (24)

1. Hadoop. http://hadoop.apache.org.
2. HBase. http://hadoop.apache.org/hbase.
3. Hive. http://hadoop.apache.org/hive.
4. IBM Parallel Machine Learning Toolbox. http://www.alphaworks.ibm.com/ tech/pml.
5. Intel Threading Building Blocks. http://www.threadingbuildingblocks.org.
6. JAQL. http://www.jaql.org.
7. Mahout. http://lucene.apache.org/mahout/.
8. MPI. http://www.mpi-forum.org.
9. OpenMP. http://www.openmp.org.
10. PThreads. https://computing.llnl.gov/tutorials/pthreads.
11. R. Agrawal et al. Mining association rules between sets of items in large databases. ACM SIGMOD, 22(2), 1993.
12. L. Breiman. Bagging predictors. Machine Learning, 24(2), 1996.
13. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001.
14. C. Chu et al. Map-reduce for machine learning on multicore. In NIPS, 2007.
15. W. Fan et al. A general framework for accurate and fast regression by data summarization in random decision trees. In ACM SIGKDD, 2006.
16. A. Ghoting et al. Fast mining of distance-based outliers in high-dimensional datasets. DMKD, 16(3), 2008.
17. M. Isard et al. Dryad: distributed data-parallel programs from sequential building blocks. In SIGOPS Operating System Review, 2007.
18. R. Jin and G. Agrawal. Shared Memory Parallelization of Data Mining Algorithms: Techniques, Programming Interface, and Performance. In SDM, 2002.
19. P. Kambadur et al. PFunc: Modern Task Parallelism For Modern High Performance Computing. In SC, 2009.
20. Y. LeCun et al. Gradient-based learning applied to document recognition. In Intelligent Signal Processing, 2001.
21. H. Li et al. Pfp: parallel fp-growth for query recommendation. In ACM RecSys, 2008.
22. C. Olston et al. Pig latin: a not-so-foreign language for data processing. In ACM SIGMOD, 2008.
23. B. Panda et al. PLANET: massively parallel learning of tree ensembles with MapReduce. Proceedings of the VLDB Endowment, 2(2), 2009.
24. Y. Yu et al. DryadLINQ: A System for General-Purpose Distributed Data-Parallel Computing Using a High-Level Language. In OSDI, 2008.