Paradigms for realizing machine learning algorithms

Big Data

Volumn 1, Issue 4, 2013, Pages 207-214

  Agneeswaran, Vijay Srinivas ^a,b   Tonpay, Pranay ^a   Tiwary, Jayati ^a  

^a Impetus Infotech India Private Limited Bangalore   (India)

^b Pritech Park SEZ   (India)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84991666919     PISSN: 21676461     EISSN: 2167647X     Source Type: Journal    
DOI: 10.1089/big.2013.0006     Document Type: Review

References (14)

1. Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Communications of the ACM 51, 1. 2008, pp. 107-113.
2. Ekstrand MD, Ludwig M, Konstan JA, Riedl JT. Rethinking the recommender research ecosystem: Reproducibility, openness, and LensKit. Proceedings of the fifth ACM conference on Recommender systems (RecSys ¢11). ACM, New York: 2011, pp.133-140.
3. Srirama SN, Jakovits P, Vainikko E. Adapting scientific computing problems to clouds using MapReduce. Future Gener Comput Syst 2012; 28: 184-192.
4. Ekanayake J, Li H, Zhang B, et al. A runtime for iterative MapReduce. Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing. Chicago, IL: 2010.
5. Venkataraman S, Roy I, Young AA, Schreiber RS. Using R for iterative, and incremental processing. Proceedings of the 4th USENIX Conference on Hot Topics in Cloud Computing (HotCloud 12). Berkeley, CA: USENIX Association, 2012, p. 11.
6. Zaharia M, Chowdhury M, Franklin MJ, et al. Spark: cluster computing with working sets. Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing (HotCloud 10). Berkeley, CA: USENIX Association, 2010, p. 10.
7. Bu Y, Howe B, Balazinska M, Ernst MD. HaLoop: Efficient iterative data processing on large clusters. Proceedings VLDB Endowment 2010; 3: 285-296.
8. Malewicz G, Malewicz G, Austern MH, et al. Pregel: A system for large-scale graph processing. Proceedings of SIGMOD International Conference on Management of Data (SIGMOD 10). New York: Association for Computing Machinery (ACM), 2010, pp. 135-146.
9. Seo S, Yoon EJ, Kim J, et al. HAMA: An efficient matrix computation with the MapReduce framework. Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology, and Science (CLOUDCOM 10). Washington, DC: IEEE Computer Society, 2010, pp. 721-726.
10. Odersky M, Spoon L, Venners B. Programming in Scala, 2nd ed. Walnut Creek, CA: Artima Publishers, 2010.
11. Yu Y, Isard M, Fetterly D, et al. DryadLINQ: A system for general-purpose distributed data-parallel computing using a high-level language. Proceedings of the 8th USENIX Conference on Operating Systems Design, and Implementation (OSDI-08). Berkeley, CA: USENIX Association, 2008, pp. 1-14.
12. Low Y, Bickson D, Gonzalez J, et al. Distributed GraphLab: A framework for machine learning, and data mining in the cloud. Proceedings of the VLDB Endowment 2012; 5: 716-727.
13. Gonzalez J, Low Y, Gretton A, Guestrin C. Parallel gibbs sampling: From colored fields to thin junction trees. AISTATS 2011; 15: 324-332.
14. Gonzalez JE, Low Y, Gu H, et al. PowerGraph: Distributed graph-parallel computation on natural graphs. Proceedings of the 10th USENIX Symposium on Operating Systems Design, and Implementation (OSDI 12). Berkeley, CA: USENIX Association, 2012, pp. 17-30.