Review on Recent Advances in Data Management Techniques and Their Applications in Various Fields

Authors

  • Kumar Rahul Assistant Professor, Department of Basic and Applied Science, NIFTEM, Sonipat, India 131028
  • Rohitash Kumar Banyal Associate Professor, Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India 324010

Keywords:

Internet of vehicles (IoV), Vehicular ad hoc network (VANET), Intelligent transportsystem (ITS), Trust management

Abstract

With the emerging role of data as business capital, businesses are realizing what digital entrepreneurs as well as disruptors already know: Data is a powerful tool for recognizing patterns, making choices, and taking action before competition. The changing position of the data in the value chain is pushing firms to aggressively explore better methods to create value from this new capital. This paper reviews the recent developments in the field of data management tools and technologies and also the use of Artificial Intelligence, Machine learning as well as deep learning technologies for data management. At the same time it also covers the challenges of the present day data management systems and their applications in various fields.

References

[1] L. AlSuwaidan, "The role of data management in the Industrial Internet of Things," Concurr. Comput. Pract. Exp., vol. 33, no. 23, pp. 1-13, 2021, https://doi.org/10.1002/cpe.6031

[2] P. Zhang, C. Wang, C. Jiang, and Z. Han, "Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT," IEEE Trans. Ind. Informatics, vol. 3203, no. c, 2021, https://doi.org/10.1109/TII.2021.3064351

[3] K. L. Wilms, S. Stieglitz, B. Ross, and C. Meske, "A value-based perspective on supporting and hindering factors for research data management," Int. J. Inf. Manage., vol. 54, no. June, p. 102174, 2020, https://doi.org/10.1016/j.ijinfomgt.2020.102174

[4] S. Huang, G. Wang, Y. Yan, and X. Fang, "Blockchain-based data management for digital twin of product," J. Manuf. Syst., vol. 54, no. August 2019, pp. 361-371, 2020, https://doi.org/10.1016/j.jmsy.2020.01.009

[5] B. Diène, J. J. P. C. Rodrigues, O. Diallo, E. H. M. Ndoye, and V. V. Korotaev, "Data management techniques for Internet of Things," Mech. Syst. Signal Process., vol. 138, 2020, https://doi.org/10.1016/j.ymssp.2019.106564

[6] V. C. F. Gomes, G. R. Queiroz, and K. R. Ferreira, "An overview of platforms for big earth observation data management and analysis," Remote Sens., vol. 12, no. 8, pp. 1-25, 2020, https://doi.org/10.3390/rs12081253

[7] J. Potthoff, P. Tremouilhac, P. Hodapp, B. Neumair, S. Bräse, and N. Jung, "Procedures for systematic capture and management of analytical data in academia," Anal. Chim. Acta X, vol. 1, pp. 1-7, 2019, https://doi.org/10.1016/j.acax.2019.100007

[8] T. P. Raptis, A. Passarella, and M. Conti, "Data management in industry 4.0: State of the art and open challenges," IEEE Access, vol. 7, pp. 97052-97093, 2019, https://doi.org/10.1109/ACCESS.2019.2929296

[9] H. Y. Paik, X. Xu, H. M. N. D. Bandara, S. U. Lee, and S. K. Lo, "Analysis of data management in blockchain-based systems: From architecture to governance," IEEE Access, vol. 7, pp. 186091-186107, 2019, https://doi.org/10.1109/ACCESS.2019.2961404

[10] S. Hossmann et al., "Data management of clinical trials during an outbreak of Ebola virus disease," Vaccine, vol. 37, no. 48, pp. 7183-7189, 2019, https://doi.org/10.1016/j.vaccine.2017.09.094

Downloads

Published

2022-04-16

How to Cite

[1]
Kumar Rahul and Rohitash Kumar Banyal 2022. Review on Recent Advances in Data Management Techniques and Their Applications in Various Fields. AG Volumes. (Apr. 2022), 01–10.