Design issues of big data parallelisms

Advances in Intelligent Systems and Computing

Volumn 434, Issue , 2016, Pages 209-217

  Mondal, Koushik ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Big Data; Data intensive computing; Data science; High dimensional data; Machine learning; Scalable framework; Semi stochastic; X informatics

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; DATA HANDLING; DATA MINING; DECISION MAKING; INFORMATION SYSTEMS; LEARNING SYSTEMS; STOCHASTIC SYSTEMS; SYSTEMS ANALYSIS;

DATA SCIENCE; DATA-INTENSIVE COMPUTING; HIGH DIMENSIONAL DATA; INFORMATICS; SCALABLE FRAMEWORK; SEMI-STOCHASTIC;

BIG DATA;

EID: 84958985233     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-81-322-2752-6_20     Document Type: Conference Paper

References (22)

1. Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute, May 2011.
2. Apache Hadoop. http://hadoop.apache.org.
3. J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters, Communications of ACM 51, Vol. 1, 2008, pp. 107–113.
4. C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins, “Pig latin: A Not-So-Foreign Language for Data Processing,” Proc. of the SIGMOD International Conference on Management of Data. ACM, 2008, pp. 1099–1110.
5. Apache Hive. https://hive.apache.org.
6. S. Seo, E.J. Yoon, J. Kim, S. Jin, J.-S. Kim, S. Maeng, Hama: an efficient matrix computation with the mapreduce framework, in 2nd International Conference on Cloud Computing Technology and Science, IEEE, 2010, pp. 721–726.
7. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, J.M. Hellerstein, Graphlab: a new parallel framework for machine learning, in: Conference on Uncertainty in Artificial Intelligence (UAI), Catalina Island, California, 2010.
8. Apache Giraph, http://giraph.apache.org.
9. Apache Mahout, http://mahout.apache.org/.
10. U. Kang, C.E. Tsourakakis, C. Faloutsos, Pegasus: a peta-scale graph mining system implementation and observations, in the Ninth IEEE International Conference on Data Mining, ICDM’09, IEEE, 2009, pp. 229–238.
11. M.F. Pace, BSP vs MapReduce, Procedeeings of Computer Science, Vol. 9, 2012, pp. 246–255.
12. Y. Bu, B. Howe, M. Balazinska, M.D. Ernst, HaLoop: efficient iterative data processing on large clusters, Proceedings of VLDB Endowments, Vol. 3 (1–2), (2010), pp. 285–296.
13. L.G. Valiant, A bridging model for parallel computation, Communnications of ACM, Vol. 33 (8), 1990, pp. 103–111.
14. L. Bottou and O. Bousquet, The tradeoffs off of large scale learning, Advance Neural Information Process Systems, Vol. 20, 2008, pp. 161–168.
15. R.H. Byrd, G.M. Chin, J. Nocedal, Y. Wu, Sample size selection in optimization methods for machine learning, Mathematical Programming 134:1, 2012, pp. 127–155.
16. Koushik Mondal, Big Data Parallelism: Issues in different X-Information Paradigms, Elsevier Procedia Computer Science, Special Issue on Big Data, Cloud and Computing Challenges, ISSN 1877–0509, Vol. 50, pp. 395-400, 2015.
17. Koushik Mondal, Paramartha Dutta, Big Data Parallelism: Challenges in different computational Paradigms, IEEE Third International conference on Computer, Communication, Control and Information Technology, ISBN: 978-1-4799-4446-0, 2015.
18. B. Recht, C. Re, S Wright, F Niu, Hogwild: A lock-free approach to parallelizing stochastic grapient descent, Advance Neural Information Processing System,s Vol. 24, 2011, pp. 693–701.
19. Drew Schmidt and George Ostrouchov, Programming with Big Data in R, user 2014 summit, http://r-pbd.org/.
20. RHadoop: Open Source Project, http://projects.revolutionanalytics.com/rhadoop/.
21. KNIME: Open for Innovation Project, https://www.knime.org/.
22. Bonnet L., Laurent A., Sala M., Laurent B., Sicard N., Reduce, You Say: What NoSQL Can Do for Data Aggregation and BI in Large Repositories, 22nd International Workshop on Database and Expert Systems Applications, pp. 483–488, 2011.