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2020 | OriginalPaper | Chapter

3. Smart Data

Authors : Julián Luengo, Diego García-Gil, Sergio Ramírez-Gallego, Salvador García, Francisco Herrera

Published in: Big Data Preprocessing

Publisher: Springer International Publishing

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Abstract

The term Smart Data refers to the challenge of transforming raw data into quality data that can be appropriately exploited to obtain valuable insights. Big Data is focused on volume, velocity, variety, veracity, and value. The idea of Smart Data is to separate the physical properties of the data (volume, velocity, and variety), from the value and veracity of the data. This transformation is the key to move from Big to Smart Data. Without value and veracity, Big Data becomes an accumulation of raw data that is not accessible in order to extract knowledge. Therefore, Smart Data discovery is tasked to extract useful information from data, in the form of a subset (big or not), which poses enough quality for a successful data mining process. The impact of Smart Data discovery in industry and academia is two-fold: higher quality data mining and reduction of data storage costs. In this chapter we give an insight of the state of Smart Data. Next, we provide a discussion on how to move from Big to Smart Data. We finish with an introduction to Smart Data and its relation with the Internet of Things.

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previous chapter Big Data: Technologies and Tools
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Literature
1.
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.CrossRef
2.
Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.CrossRef
3.
Baldassarre, M. T., Caballero, I., Caivano, D., Rivas Garcia, B., & Piattini, M. (2018). From big data to smart data: A data quality perspective. In Proceedings of the 1st ACM SIGSOFT International Workshop on Ensemble-Based Software Engineering (pp. 19–24). New York: ACM.CrossRef
4.
Chen, J., Dosyn, D., Lytvyn, V., & Sachenko, A. (2017). Smart data integration by goal driven ontology learning. In Advances in Intelligent Systems and Computing (vol. 529, pp. 283–292).
5.
del Río, S., López, V., Benítez, J. M., & Herrera, F. (2014). On the use of MapReduce for imbalanced big data using random forest. Information Sciences, 285, 112–137.CrossRef
6.
Fan, J., & Fan, Y. (2008). High dimensional classification using features annealed independence rules. Annals of Statistics, 36(6), 2605–2637.MathSciNetCrossRef
7.
Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National Science Review, 1(2), 293–314.CrossRef
8.
Fernández, A., del Río, S., Chawla, N. V., & Herrera, F. (2017). An insight into imbalanced big data classification: outcomes and challenges. Complex & Intelligent Systems, 3(2), 105–120.CrossRef
9.
Frénay, B., & Verleysen, M. (2014). Classification in the presence of label noise: A survey. IEEE Transactions on Neural Networks and Learning Systems, 25(5), 845–869.CrossRef
10.
García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining. Berlin: Springer.CrossRef
11.
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.CrossRef
12.
García-Gil, D., Luengo, J., García, S., & Herrera, F. (2019). Enabling smart data: Noise filtering in big data classification. Information Sciences, 479, 135–152.CrossRef
13.
Iafrate, F. (2014). A journey from big data to smart data. Advances in Intelligent Systems and Computing, 261, 25–33.CrossRef
14.
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.CrossRef
15.
Lenk, A., Bonorden, L., Hellmanns, A., Roedder, N., & Jaehnichen, S. (2015). Towards a taxonomy of standards in smart data. In Proceedings: 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 1749–1754).
16.
17.
Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., et al. (2016). MLlib: Machine learning in apache spark. Journal of Machine Learning Research, 17(34), 1–7.MathSciNetMATH
18.
Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., et al. (2016). Cyber-physical systems in manufacturing. CIRP Annals, 65(2), 621–641.CrossRef
19.
Peralta, D., del Río, S., Ramírez-Gallego, S., Triguero, I., Benitez, J. M., & Herrera, F. (2016). Evolutionary feature selection for big data classification: A MapReduce approach. Mathematical Problems in Engineering, 2015, 1–11, Article ID 246139
20.
Raja, P. V., Sivasankar, E., & Pitchiah, R. (2015). Framework for smart health: Toward connected data from big data. Advances in Intelligent Systems and Computing, 343, 423–433.CrossRef
21.
Ramírez-Gallego, S., García, S., Mouriño-Talín, H., Martínez-Rego, D., Bolón-Canedo, V., Alonso-Betanzos, A., et al. (2016). Data discretization: taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 6(1), 5–21.
22.
Ramírez-Gallego, S., Lastra, I., Martínez-Rego, D., Bolón-Canedo, V., Benítez, J. M., Herrera, F., et al. (2017). Fast-mRMR: Fast minimum redundancy maximum relevance algorithm for high-dimensional big data. International Journal of Intelligent Systems, 32(2), 134–152.CrossRef
23.
Rastogi, A. K., Narang, N., & Siddiqui, Z. A. (2018). Imbalanced big data classification: A distributed implementation of smote. In Proceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking (p. 14). New York: ACM.
24.
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.CrossRef
25.
Tan, M., Tsang, I. W., & Wang, L. (2014). Towards ultrahigh dimensional feature selection for big data. Journal of Machine Learning Research, 15, 1371–1429.MathSciNetMATH
26.
Teng, H., Liu, Y., Liu, A., Xiong, N. N., Cai, Z., Wang, T., et al. (2019). A novel code data dissemination scheme for internet of things through mobile vehicle of smart cities. Future Generation Computer Systems, 94, 351–367.CrossRef
27.
Triguero, I., del Río, S., López, V., Bacardit, J., Benítez, J. M., & Herrera, F. (2015). ROSEFW-RF: the winner algorithm for the ECBDL14 big data competition: an extremely imbalanced big data bioinformatics problem. Knowledge-Based Systems, 87, 69–79.CrossRef
28.
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.
29.
Triguero, I., Peralta, D., Bacardit, J., García, S., & Herrera, F. (2015). MRPR: A MapReduce solution for prototype reduction in big data classification. Neurocomputing, 150, 331–345.CrossRef
30.
Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), 22–32.CrossRef
Metadata
Title
Smart Data
Authors
Julián Luengo
Diego García-Gil
Sergio Ramírez-Gallego
Salvador García
Francisco Herrera
Copyright Year
2020
Book
Big Data Preprocessing

Print ISBN: 978-3-030-39104-1

Electronic ISBN: 978-3-030-39105-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39105-8_3

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