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.
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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.
García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining. Berlin: Springer.
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.
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.
Perez, L., & Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint. arXiv:1712.04621.
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.
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.
Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018). A survey on deep learning for big data. Information Fusion, 42, 146–157.
Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining. Applied Artificial Intelligence, 17(5–6), 375–381.
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Luengo, J., García-Gil, D., Ramírez-Gallego, S., García, S., Herrera, F. (2020). Final Thoughts: From Big Data to Smart Data. In: Big Data Preprocessing. Springer, Cham. https://doi.org/10.1007/978-3-030-39105-8_10
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DOI: https://doi.org/10.1007/978-3-030-39105-8_10
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