Cover
Vol. 14 No. 2 (2018)

Published: December 31, 2018

Pages: 127-138

Original Article

Current Big Data Issues and Their Solutions via Deep Learning: An Overview

Asif Ali Banka 1 Roohie Naaz Mir
1Department of Computer Science and Engineering, National Institue of Technology, Srinagar, Jammu and Kashmir 190006 India
History
  • Received: October 31, 2018
  • Available Online: December 31, 2018
DOI

https://doi.org/10.37917/ijeee.14.2.5

Abstract

The advancements in modern day computing and architectures focus on harnessing parallelism and achieve high performance computing resulting in generation of massive amounts of data. The information produced needs to be represented and analyzed to address various challenges in technology and business domains. Radical expansion and integration of digital devices, networking, data storage and computation systems are generating more data than ever. Data sets are massive and complex, hence traditional learning methods fail to rescue the researchers and have in turn resulted in adoption of machine learning techniques to provide possible solutions to mine the information hidden in unseen data. Interestingly, deep learning finds its place in big data applications. One of major advantages of deep learning is that it is not human engineered. In this paper, we look at various machine learning algorithms that have already been applied to big data related problems and have shown promising results. We also look at deep learning as a rescue and solution to big data issues that are not efficiently addressed using traditional methods. Deep learning is finding its place in most applications where we come across critical and dominating 5Vs of big data and is expected to perform better.

Keywords

References

  1. S. Sakr, F. M. Orakzai, I. Abdelaziz, Z. Khayyat, Large-Scale Processing Using Apache Giraph, Springer, 2017.
  2. E. Dumbill, What is big data (2012).
  3. M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, E. Muharemagic, Deep learning applications and challenges in big data analytics, Journal of Big Data 2 (1) (2015) 1.
  4. E. Alpaydin, Introduction to machine learning, MIT press, 2014.
  5. N. R. Council, et al., Frontiers in massive data analysis, National Academies Press, 2013.
  6. K. Slavakis, G. B. Giannakis, G. Mateos, Modeling and optimization for big data analytics:(statistical) learning tools for our era of data deluge, Signal Processing Magazine 31 (5) (2014) 18–31.
  7. X.-W. Chen, X. Lin, Big data deep learning: challenges and perspectives,
  8. T. M. Mitchell, Machine learning and data mining, Communications of the ACM 42 (11) (1999) 30–36.
  9. S. Russell, P. Norvig, A. Intelligence, A modern approach, Artificial Prentice-Hall, Egnlewood Cli ↵ s 25 (1995) 27.
  10. V. Cherkassky, F. M. Mulier, Learning from data: concepts, theory, and methods, John Wiley & Sons, 2007.
  11. T. M. Mitchell, The discipline of machine learning, Vol. 9, Carnegie Mellon University, School of Computer Science, Machine Learning Department, 2006.
  12. C. Rudin, K. L. Wagsta ↵ , Machine learning for science and society, Machine Learning 95 (1) (2014) 1–9.
  13. B. Adam, I. F. Smith, Reinforcement learning for structural control, Journal of Computing in Civil Engineering 22 (2) (2008) 133–139.
  14. N. Jones, The learning machines, Nature 505 (7482) (2014) 146.
  15. J. Langford, Tutorial on practical prediction theory for classification, Journal of machine learning research 6 (Mar) (2005) 273–306.
  16. R. Bekkerman, R. El-Yaniv, N. Tishby, Y. Winter, Distributional word clusters vs. words for text categorization, Journal of Machine Learning Research 3 (Mar) (2003) 1183–1208.
  17. Y. Bengio, A. Courville, P. Vincent, Representation learning: A review and new perspectives, IEEE transactions on pattern analysis and machine
  18. F. Huang, A. Yates, Biased representation learning for domain adaptation, in: Proceedings of the 2012 Joint Conference on Empirical Methods Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics, 2012, pp. 1313–1323.
  19. A. Bordes, X. Glorot, J. Weston, Y. Bengio, Joint learning of words and meaning representations for open-text semantic parsing., in: AISTATS, Vol. 22, 2012, pp. 127–135.
  20. N. Boulanger-Lewandowski, Y. Bengio, P. Vincent, Modeling temporal de- pendencies in highdimensional sequences: Application to polyphonic music generation and transcription, arXiv preprint arXiv:1206.6392.
  21. K. Dwivedi, K. Biswaranjan, A. Sethi, Drowsy driver detection using representation learning, in: Advance Computing Conference (IACC), 2014 995–999.
  22. D. Yu, L. Deng, Deep learning and applications to signal and dsp], Signal Processing Magazine 28 (1) (2011) 145–154.
  23. G. E. Dahl, D. Yu, L. Deng, A. Acero, Context-dependent pre-trained deep neural networks for largevocabulary speech recognition, IEEE Trans- actions on Audio, Speech, and Language Processing 20 (1) (2012) 30–42.
  24. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al., Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, IEEE Signal Processing Magazine 29 (6) (2012) 82–97.
  25. D. C. Cire ̧san, U. Meier, L. M. big, simple neural nets for handwritten digit recognition, Neural computation 22 (12) (2010) 3207–3220.
  26. D. Peteiro-Barral, B. GuijarroBerdin ̃as, A survey of methods for distributed machine learning, Progress in Artificial Intelligence 2 (1) (2013) 1–11.
  27. H. Chen, T. Li, C. Luo, S.-J. Horng, for updating decision rules on attribute values coarsening and refining, IEEE Transactions on Knowledge and Data Engineering 26 (12) (2014) 2886– 2899.
  28. J. Chen, C. Wang, R. Wang, Using stacked generalization to combine svms in magnitude and shape feature spaces for classification of hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing 47 (7) (2009) 2193–2205.
  29. E. Leyva, A. Gonz ́alez, R. P ́erez, A set of complexity measures designed for applying meta-learning to instance selection, Transactions on Knowledge and Data Engineering 27 (2) (2015) 354–367.
  30. E. W. Xiang, B. Cao, D. H. Hu, Q. Yang, Bridging domains using world wide knowledge for transfer learning, Data Engineering 22 (6) (2010) 770– 783.
  31. S. J. Pan, Q. Yang, A survey on transfer learning, IEEE Transactions on knowledge and data engineering 22 (10) (2010) 1345–1359.
  32. J. Qiu, Q. Wu, G. Ding, Y. Xu, S. Feng, A survey of machine learning for big data processing, EURASIP Journal on Advances in Signal Processing 2016 (1) (2016) 1–16.
  33. R. Raina, A. Y. Ng, D. Koller, Constructing informative priors using trans- fer learning, in: Proceedings of the 23rd international conference on Ma- chine learning, ACM, 2006, pp. 713–720.
  34. J. Zhang, Deep transfer learning via restricted boltzmann machine for document classification, in: Machine Learning and Applications and Workshops (ICMLA), 2011 10th
  35. Y. Fu, B. Li, X. Zhu, C. Zhang, Active learning without knowing label homogeneity query approach, Data Engineering 26 (4) (2014) 808– 822.
  36. B. Settles, Active learning literature survey, University of Wisconsin, Madison 52 (55-66) (2010) 11.
  37. M. M. Haque, L. B. Holder, M. K. Skinner, D. J. Cook, Generalized query- based active learning to identify differentially methylated regions in dna, IEEE/ACM Transactions on Computational Biology and Bioinformatics 10 (3) (2013) 632–644.
  38. D. Tuia, M. Volpi, L. Copa, M. Kanevski, J. Munoz-Mari, A survey of active learning algorithms for supervised remote sensing image classification, Journal of Selected Topics in Signal Processing 5 (3) (2011) 606–617.
  39. G. Ding, Q. Wu, Y.-D. Yao, J. Wang, Y. Chen, Kernel-based learning for statistical signal processing cognitive radio networks: Theoretical foundations, example applications, and future directions, IEEE Signal Processing Magazine 30 (4) (2013) 126–136.
  40. C. Li, M. Georgiopoulos, G. C. Anagnostopoulos, A unifying framework for typical multitask multiple kernel learning problems, Transactions on Neural Networks and Learning Systems 25 (7) (2014) 1287–1297.
  41. K. Slavakis, S. Theodoridis, I. Yamada, Online kernel-based classification using adaptive projection algorithms, Transactions on Signal Pro- cessing 56 (7) (2008) 2781–2796.
  42. J. Dean, S. Ghemawat, Mapreduce: simplified data processing on large clusters, Communications of the ACM 51 (1) (2008) 107–113.
  43. J. Dean, S. Ghemawat, Mapreduce: a flexible data processing tool, Communications of the ACM 53 (1) (2010) 72–77.
  44. S. Shalev-Shwartz, et al., Online learning and online convex optimization, Foundations and Trends R in Machine Learning 4 (2) (2012) 107–194.
  45. J. Wang, P. Zhao, S. C. Hoi, R. Jin, Online feature selection and its applications, IEEE Transactions on Knowledge and Data Engineering 26 (3) (2014) 698–710.
  46. J. Kivinen, A. J. Smola, R. C. Williamson, et al., Online learning with kernels, in: NIPS, 2001, pp. 785– 792.
  47. M. Bilenko, S. Basil, M. Sahami, Adaptive product normalization: Using online learning for record linkage in comparison shopping, in: Data Mining, Fifth IEEE International Conference on, IEEE, 2005, pp. 8–pp.
  48. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: theory and applications, Neurocomputing 70 (1) (2006) 489–501.
  49. W. Yang, X. Liu, L. Zhang, L. T. Yang, Big data real-time processing based on storm, in: Trust, Security and Privacy Computing and Communications (TrustCom), 2013 12th IEEE International Conference on, IEEE, 2013, pp. 1784–1787.
  50. R. Ranjan, Streaming big data processing in datacenter clouds, IEEE Cloud Computing 1 (1) (2014) 78–83.
  51. I. Goodfellow, H. Lee, Q. V. Le, A. Saxe, A. Y. Ng, Measuring invariances neural processing systems, 2009, pp. 646–654.
  52. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From data mining to knowledge discovery in databases, AI magazine 17 (3) (1996) 37.
  53. X. Wu, X. Zhu, G.-Q. Wu, W. Ding, Data mining with big data, IEEE transactions on knowledge and data engineering 26 (1) (2014) 97–107.
  54. J. Kelly III, S. Hamm, Smart Machines: IBMO ̃s Watson and the Era of Cognitive Computing, Columbia University Press, 2013.
  55. G. E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets, Neural computation 18 (7) (2006) 1527–1554.
  56. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, et al., Greedy layerwise training of deep networks, Advances neural processing systems 19 (2007) 153.
  57. G. Dahl, A.-r. Mohamed, G. E. Hinton, et al., Phone recognition with the mean-covariance restricted boltzmann machine, in: Advances in neural processing systems, 2010, pp. 469–477.
  58. A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, processing systems, 2012, pp. 1097– 1105.
  59. Gheisari, Mehdi, Guojun Wang, and Md Zakirul Alam Bhuiyan. "A survey on deep learning in big data." Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), 2017 2, pp. 173-180. IEEE, 2017.
  60. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
  61. Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018). A survey on deep learning for big data. Information Fusion, 42, 146-157.