Efficient Machine Learning for Big Data: A Review

Big Data Research

Volumn 2, Issue 3, 2015, Pages 87-93

  Al Jarrah, Omar Y ^a   Yoo, Paul D ^b   Muhaidat, Sami ^c   Karagiannidis, George K ^a,d   Taha, Kamal ^a  

^a KHALIFA UNIVERSITY   (United Arab Emirates)

^b BOURNEMOUTH UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF SURREY   (United Kingdom)

^d ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Big data; Computational modeling; Efficient machine learning; Green computing

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL EFFICIENCY; ENERGY EFFICIENCY; GREEN COMPUTING; LEARNING SYSTEMS;

CLASSIFICATION ACCURACY; COMPUTATIONAL COSTS; COMPUTATIONAL MODELLING; COMPUTER ENGINEERS; CURRENT DATA; DATA INTENSIVE; EFFICIENT MACHINE LEARNING; EMERGING TECHNOLOGIES; MACHINE-LEARNING; SUSTAINABLE COMPUTING;

BIG DATA;

EID: 84955413763     PISSN: None     EISSN: 22145796     Source Type: Journal    
DOI: 10.1016/j.bdr.2015.04.001     Document Type: Review

References (63)

1. Pettey C. Gartner estimates ict industry accounts for 2 percent of global co2 emissions online, April 2007, accessed on Aug. 2014. http://www.gartner.com/newsroom/id/503867.
2. Lohr S. I.B.M. effort to focus on saving energy online, May 2007. http://www.nytimes.com/2007/05/10/technology/10blue.html?pagewanted=print&_r=0.
3. Koomey J.G., et al. Estimating total power consumption by servers in the U.S. and the world 2007, Technical report, Stanford University.
4. Strathman M. Model-based energy management reduces energy costs. E&P 2010, online. http://www.epmag.com/guests/2010/06/16/model-based-energy-management-reduces-energy-costs.
5. J. Nil, Managing data ood is industry challenge, 2005.
6. Yoo P., Ng J., Zomaya A. An energy-efficient kernel framework for large-scale data modeling and classification. 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum May 2011, 404-408.
7. Cheng H., Tan P.-N., Jin R. Efficient algorithm for localized support vector machine. IEEE Trans. Knowl. Data Eng. 2010, 22(4):537-549.
8. Lamb D.D.P.P., Jurdak R. Csiro ict centre and csiro sensors and sensor networks tcp online. http://www.csiro.au/.
9. M. Lautenschlager, Model and Data, Max-Planck-Institute for Meteorology, Hamburg, Germany.
10. Energy U.D. Insights learned from the human DNA sequence, what has been learned from analysis of the working draft sequence of the human genome? What is still unknown? online, accessed on 2nd May 2011. http://www.ornl.gov/hgmis.
11. A.J. Hey, A.E. Trefethen, The data deluge: an e-science perspective, 2003.
12. NCBI Genebank statistics online, accessed on 2nd Aug. 2014. http://www.ncbi.nlm.nih.gov/genbank/genbankstats.html.
13. Uniprotkb/swiss-prot protein knowledgebase release 2011_04 statistics online, accessed on 10th April 2011. http://expasy.org/sprot/relnotes/relstat.html.
14. Uniprotkb/trembl protein knowledgebase release 2011_04 statistics online, accessed on 10th April 2011. http://www.ebi.ac.uk/uniprot/TrEMBLstats.
15. Baluja T. Electronic patient records will soon end doctors scrawl on paper, the globe and mail online, accessed on 13th April 2011. http://www.theglobeandmail.com/news/national/toronto/electronic-patient-records-will-soon-end-doctors-scrawl-on-paper/article1982647.
16. Insiteone official website online, accessed on 10th April 2011. http://www.insiteone.com/.
17. Nuclear Cardiology Markets 2007, TriMark Publications, LLC.
18. EMR, News E. Dell launches new cloud-based services for hospitals and physician practices online, accessed on 3rd April 2011. http://www.emrandhipaa.com/news/2011/02/21/dell-launches-new-cloud-based-services-for-hospitals-and-physician-practice.
19. McPherson A.M., et al. Vista: project status. SPIE 6267, Ground-based and Airborne Telescopes, 626707 2006, vol. 6267. http://spie.org/Publications/Proceedings/Paper/10.1117/12.671352, 10.1117/12.671352.
20. The dark energy survey online, accessed on 20th April 2011. http://www.darkenergysurvey.org/.
21. The sloan digital sky survey accessed on 20th April 2011. http://www.sdss.org/.
22. Kaiser N., et al. The pan-starrs survey telescope project. Bull. Am. Astron. Soc. 2007, 37:1409.
23. Ref S. Lsst receives $30 million from Charles Simonyi and Bill Gates online, accessed on 25th April 2011. http://www.spaceref.com/news/viewpr.html?pid=24409.
24. Marwah M., Shah A., Bash C., Patel C., Ramakrishnan N. Using data mining to help design sustainable products. Computer 2011, 44(8):103-106.
25. Sundaravaradan N., Patnaik D., Ramakrishnan N., Marwah M., Shah A. Discovering life cycle assessment trees from impact factor databases. AAAI 2011.
26. Sundaravaradan N., Marwah M., Shah A., Ramakrishnan N. Data mining approaches for life cycle assessment. IEEE ISSST 2011, vol. 11.
27. Patnaik D., Marwah M., Sharma R.K., Ramakrishnan N. Data mining for modeling chiller systems in data centers. Advances in Intelligent Data Analysis 2010, vol. IX:125-136. Springer.
28. Yang P., Hwa Yang Y., Zhou B.B., Zomaya A.Y. A review of ensemble methods in bioinformatics. Current Bioinform. 2010, 5(4):296-308.
29. Opitz D., Maclin R. Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 1999, 11:169-198.
30. Polikar R. Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 2006, 6(3):21-45.
31. Tang Y., Wang Y., Cooper K., Li L. Towards big data Bayesian network learning - an ensemble learning based approach. 2014 IEEE International Congress on Big Data (BigData Congress) June 2014, 355-357.
32. Wikipedia Ensemble in numerical weather prediction online, accessed on 23rd Feb. 2011. http://en.wikipedia.org/wiki/Ensemble_forecasting.
33. Blais J.A.R. Estimation and Spectral Analysis 1988, Univ. Calgary Press, Canada.
34. Elkan C. Naive Bayesian learning 1997, Department of Computer Science and Engineering, University of California, San Diego.
35. Joachims T. Training linear svms in linear time. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006, 217-226. ACM.
36. Yoo P.D., Ho Y.S., Zhou B.B., Zomaya A.Y. Siteseek: post-transltional modification analysis using adaptive locality-effective kernel methods and new profiles. BMC Bioinform. 2008, 9(1):272.
37. Bottou L., Vapnik V. Local learning algorithms. Neural Comput. 1992, 4(6):888-900.
38. Gilardi N., Bengio S. Local machine learning models for spatial data analysis. J. Geogr. Inform. Dec. Anal. 2000, 4:11-28. EPFL-ARTICLE-82651.
39. Zhang H., Berg A.C., Maire M., Malik J. Svm-knn: discriminative nearest neighbor classification for visual category recognition. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2006, vol. 2:2126-2136. IEEE.
40. Lau K., Wu Q. Local prediction of non-linear time series using support vector regression. Pattern Recognit. 2008, 41(5):1539-1547.
41. Yoo P.D., Sikder A.R., Taheri J., Zhou B.B., Zomaya A.Y. Domnet: protein domain boundary prediction using enhanced general regression network and new profiles. IEEE Trans. Nanobiosci. 2008, 7(2):172-181.
42. Gersho A., Gray R.M. Vector Quantization and Signal Compression 1992, Springer Science & Business Media.
43. Hinton G., Osindero S., Teh Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006, 18(7):1527-1554.
44. Bengio Y., Courville A., Vincent P. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 35(8):1798-1828.
45. Chen X.-W., Lin X. Big data deep learning: challenges and perspectives. IEEE Access 2014, 2:514-525.
46. Deng L. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inform. Process. 2014, 3. p. e2.
47. Hinton G.E., Salakhutdinov R.R. Reducing the dimensionality of data with neural networks. Science 2006, 313(5786):504-507.
48. Lv Y., Duan Y., Kang W., Li Z., Wang F.-Y. Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 2014, PP(99):1-9.
49. Ngiam J., Coates A., Lahiri A., Prochnow B., Le Q.V., Ng A.Y. On optimization methods for deep learning. Proceedings of the 28th International Conference on Machine Learning 2011, 265-272.
50. Deng L., Yu D. Deep convex net: a scalable architecture for speech pattern classification. Proceedings of the Interspeech 2011.
51. Wolpert D.H. Stacked generalization. Neural Netw. 1992, 5(2):241-259.
52. Murphy K.P. Machine Learning: A Probabilistic Perspective 2012, MIT Press.
53. Baldominos A., Albacete E., Saez Y., Isasi P. A scalable machine learning online service for big data real-time analysis. 2014 IEEE Symposium on Computational Intelligence in Big Data Dec. 2014, 1-8.
54. Huang H., Liu H. Big data machine learning and graph analytics: current state and future challenges. 2014 IEEE International Conference on Big Data (Big Data) Oct. 2014, 16-17.
55. Bifet A., Morales G.D.F. Big data stream learning with samoa. 2014 IEEE International Conference on Data Mining Workshop Dec. 2014, 1199-1202.
56. Apache Hadoop http://hadoop.apache.org.
57. Gu R., Yang X., Yan J., Sun Y., Wang B., Yuan C., Huang Y. Shadoop: improving mapreduce performance by optimizing job execution mechanism in hadoop clusters. J. Parallel Distrib. Comput. 2014, 74(3):2166-2179.
58. Ding L., Xin J., Wang G., Huang S. Commapreduce: an improvement of mapreduce with lightweight communication mechanisms. Database Systems for Advanced Applications 2012, 150-168. Springer.
59. Isard M., Budiu M., Yu Y., Birrell A., Fetterly D. Dryad: distributed data-parallel programs from sequential building blocks. ACM SIGOPS Operating Systems Review 2007, vol. 41:59-72. ACM.
60. Power R., Li J. Piccolo: building fast, distributed programs with partitioned tables. OSDI 2010, vol. 10:1-14.
61. Chen C.P., Zhang C.Y. Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 2014, 275:314-347.
62. Le Q.V. Building high-level features using large scale unsupervised learning. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing 2013, 8595-8598. IEEE.
63. K. Zhang, X. Chen, Large-scale deep belief nets with mapreduce, 2014.