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Final Thoughts: From Big Data to Smart Data

  • Julián Luengo
  • Diego García-Gil
  • Sergio Ramírez-Gallego
  • Salvador García
  • Francisco Herrera
Chapter
  • 59 Downloads

Abstract

Throughout this book we have presented a complete vision about Big Data preprocessing and how it enables Smart Data. Data is only as valuable as the knowledge and insights we can extract from it. Referring to the well-known “garbage in, garbage out” principle, accumulating vast amounts of raw data will not guarantee quality results, but poor knowledge. In this last chapter we aim to provide a couple of final thoughts on the importance of data preprocessing, how different it is to carry out data preprocessing compared to classical datasets, and some perspectives for the commonalities between Deep Learning and Big Data preprocessing.

References

  1. 1.
    Castillo, A., Tabik, S., Pérez, F., Olmos, R., & Herrera, F. (2019). Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning. Neurocomputing, 330, 151–161.CrossRefGoogle Scholar
  2. 2.
    García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining. Berlin: Springer.CrossRefGoogle Scholar
  3. 3.
    García, S., Luengo, J., & Herrera, F. (2016). Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowledge-Based Systems, 98, 1–29.CrossRefGoogle Scholar
  4. 4.
    George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326.CrossRefGoogle Scholar
  5. 5.
    Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press.zbMATHGoogle Scholar
  6. 6.
    LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.CrossRefGoogle Scholar
  7. 7.
    Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint. arXiv:1712.04621.Google Scholar
  8. 8.
    Triguero, I., García-Gil, D., Maillo, J., Luengo, J., García, S., & Herrera, F. (2019). Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(2), e1289.Google Scholar
  9. 9.
    Wong, S. C., Gatt, A., Stamatescu, V., & McDonnell, M. D. (2016). Understanding data augmentation for classification: when to warp? In 2016 international conference on digital image computing: techniques and applications (DICTA) (pp. 1–6). IEEE.Google Scholar
  10. 10.
    Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018). A survey on deep learning for big data. Information Fusion, 42, 146–157.CrossRefGoogle Scholar
  11. 11.
    Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied Artificial Intelligence, 17(5–6), 375–381.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Julián Luengo
    • 1
  • Diego García-Gil
    • 1
  • Sergio Ramírez-Gallego
    • 2
  • Salvador García
    • 1
  • Francisco Herrera
    • 1
  1. 1.Department of Computer Science and AIUniversity of GranadaGranadaSpain
  2. 2.DOCOMO Digital EspañaMadridSpain

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