Reviewing the Challenges of Big Data Use in Smart Industries

Authors

  • Ehsan Sabet * Wolfson School of Mechanical, Electrical and Manufacturing Engineering Loughborough University, Leicestershire LE11 3TU, UK

DOI:

https://doi.org/10.52547/ijimes.1.4.66

DOR:

https://dorl.net/dor/20.1001.1.27832678.2021.1.4.6.2

Keywords:

Big Data, Smart Industries, Big Data Challenges, Hierarchical Analysis

Abstract

Purpose: In recent years, data has been growing on a large scale, and the development of Internet applications, mobile applications, and network-connected sensors has also increased dramatically. These programs and extensive Internet communications continuously generate large volumes of data that are diverse and structurally different, called big data.

Methodology: This article first reviews big data and defines its features, and then discusses the challenges it faces in the smart industry. Finally, using fuzzy hierarchical analysis, the most important challenges of using big data in smart industries have been prioritized.

Findings: Due to the increasing volume of information transfer in the space of industrial generation, big data problem, import and storage of large volume of data information items and its management, preprocessing and post-processing, speed, accuracy and security of information are very important. It has gained a lot of attention and has attracted the attention of many researchers and experts in the field of information technology and active in the industry.

Originality/Value: This article review the challenges of big data use in smart industries.

Downloads

Download data is not yet available.

References

Fathi, M., Haghi Kashani, M., Jameii, S. M., & Mahdipour, E. (2021). Big data analytics in weather forecasting: A systematic review. Archives of Computational Methods in Engineering, 1-29. https://doi.org/10.1007/s11831-021-09616-4

Nozari, H., Fallah, M., Kazemipoor, H., & Najafi, S. E. (2021). Big data analysis of IoT-based supply chain management considering FMCG industries. Бизнес-информатика, 15(1 (eng)). DOI: 10.17323/2587-814X.2021.1.78.96

Nahr, J. G., Nozari, H., & Sadeghi, M. E. (2021). Green supply chain based on artificial intelligence of things (AIoT). International Journal of Innovation in Management, Economics and Social Sciences, 1(2), 56-63. https://doi.org/10.52547/ijimes.1.2.56

Aliahmadi, A., Jafari-Eskandari, M., Mozafari, A., & Nozari, H. (2016). Comparing linear regression and artificial neural networks to forecast total productivity growth in Iran. International Journal of Information, Business and Management, 8(1), 93.

Aliahmadi, A., Jafari-Eskandari, M., Mozafari, M., & Nozari, H. (2013). Comparing artificial neural networks and regression methods for predicting crude oil exports. International Journal of Information, Business and Management, 5(2), 40-58.

Nozari, H., Fallah, M., & Szmelter-Jarosz, A. (2021). A conceptual framework of green smart IoT-based supply chain management. International Journal of Research in Industrial Engineering, 10(1), 22-34. DOL: 10.22105/riej.2021.274859.1189

Das, T. K., & Kumar, P. M. (2013). Big data analytics: A framework for unstructured data analysis. International Journal of Engineering Science & Technology, 5(1), 153.

Das, T. K., Acharjya, D. P., & Patra, M. R. (2014, January). Opinion mining about a product by analyzing public tweets in Twitter. In 2014 International Conference on Computer Communication and Informatics (pp. 1-4). IEEE. DOI: 10.1109/ICCCI.2014.6921727

Fallah, M., & Nozari, H. (2020). Quantitative Analysis of Cyber Risks in IoT-Based Supply Chain (FMCG Industries). Journal of Decisions & Operations Research, 5(4).

Nozari, H., & Sadeghi, M. E. (2021). Artificial intelligence and Machine Learning for Real-world problems (A survey). International Journal of Innovation in Engineering, 1(3), 38-47.

Mei, H., Guan, H., Xin, C., Wen, X., & Chen, W. (2020). Datav: Data visualization on large high-resolution displays. Visual Informatics, 4(3), 12-23. https://doi.org/10.1016/j.visinf.2020.07.001

Fallah, M., Sadeghi, M. E., & Nozari, H. (2021). Quantitative analysis of the applied parts of Internet of Things technology in Iran: an opportunity for economic leapfrogging through technological development. Science and Technology Policy Letters.

Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., ... & De-La-Hoz-Valdiris, E. (2022). Trends and Future Perspective Challenges in Big Data. In Advances in Intelligent Data Analysis and Applications (pp. 309-325). Springer, Singapore. https://doi.org/10.1007/978-981-16-5036-9_30

Ding, Y., Jin, M., Li, S., & Feng, D. (2021). Smart logistics based on the internet of things technology: an overview. International Journal of Logistics Research and Applications, 24(4), 323-345. https://doi.org/10.1080/13675567.2020.1757053

Sadeeq, M. M., Abdulkareem, N. M., Zeebaree, S. R., Ahmed, D. M., Sami, A. S., & Zebari, R. R. (2021). IoT and Cloud computing issues, challenges and opportunities: A review. Qubahan Academic Journal, 1(2), 1-7. https://doi.org/10.48161/qaj.v1n2a36

Dagdia, Z. C., Avdeyev, P., & Bayzid, M. S. (2021). Biological computation and computational biology: survey, challenges, and discussion. Artificial Intelligence Review, 1-67. https://doi.org/10.1007/s10462-020-09951-1

von Burg, V., Low, G. H., Häner, T., Steiger, D. S., Reiher, M., Roetteler, M., & Troyer, M. (2021). Quantum computing enhanced computational catalysis. Physical Review Research, 3(3), 033055.

Piattini, M., Peterssen, G., & Pérez-Castillo, R. (2021). Quantum computing: A new software engineering golden age. ACM SIGSOFT Software Engineering Notes, 45(3), 12-14.

Downloads

Published

2021-12-12

How to Cite

Sabet, E. (2021). Reviewing the Challenges of Big Data Use in Smart Industries. International Journal of Innovation in Management, Economics and Social Sciences, 1(4), 66–72. https://doi.org/10.52547/ijimes.1.4.66

Issue

Section

Original Research