Open Access
Issue |
E3S Web Conf.
Volume 412, 2023
International Conference on Innovation in Modern Applied Science, Environment, Energy and Earth Studies (ICIES’11 2023)
|
|
---|---|---|
Article Number | 01108 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/e3sconf/202341201108 | |
Published online | 25 August 2023 |
- A. Arif, T. A. Alghamdi, Z. A. Khan, and N. Javaid, “Towards Efficient Energy Utilization Using Big Data Analytics in Smart Cities for Electricity Theft Detection,” Big Data Res., vol. 27, p. 100285, Feb. 2022, doi: 10.1016/j.bdr.2021.100285. [CrossRef] [Google Scholar]
- H. Liao, E. Michalenko, and S. C. Vegunta, “Review of Big Data Analytics for Smart Electrical Energy Systems,” Energies, vol. 16, no. 8, p. 3581, Apr. 2023, doi: 10.3390/en16083581. [CrossRef] [Google Scholar]
- O. Alotaibi and E. Pardede, “Transformation of Schema from Relational Database (RDB) to NoSQL Databases,” Data, vol. 4, no. 4, p. 148, Nov. 2019, doi: 10.3390/data4040148. [CrossRef] [Google Scholar]
- J. Xie, F. Xu, Z. Li, and X. Li, “Data Mining Method under Model-Driven Architecture (MDA),” Secur. Commun. Networks, vol. 2022, pp. 1–10, Mar. 2022, doi: 10.1155/2022/5806829. [Google Scholar]
- D. Mahajan, C. Blakeney, and Z. Zong, “Improving the energy efficiency of relational and NoSQL databases via query optimizations,” Sustain. Comput. Informatics Syst., vol. 22, pp. 120–133, Jun. 2019, doi: 10.1016/j.suscom.2019.01.017. [CrossRef] [Google Scholar]
- T. Li, G. Yu, X. Liu, and J. Song, “Analyzing the Waiting Energy Consumption of NoSQL Databases,” in 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing, 2014, pp. 277–282, doi: 10.1109/DASC.2014.56. [Google Scholar]
- F. Mehdipour, H. Noori, and B. Javadi, “Energy-Efficient Big Data Analytics in Datacenters,” 2016, pp. 59–101. [Google Scholar]
- A. H. Abed, “Recovery and Concurrency Challenging in Big Data and NoSQL Database Systems,” Int. J. Adv. Netw. Appl., vol. 11, no. 04, pp. 4321–4329, 2020, doi: 10.35444/IJANA.2020.11041. [Google Scholar]
- M. Shah, A. Kothari, and S. Patel, “A Comprehensive Survey on Energy Consumption Analysis for NoSQL,” Scalable Comput. Pract. Exp., vol. 23, no. 1, pp. 35–50, Apr. 2022, doi: 10.12694/scpe.v23i1.1971. [CrossRef] [Google Scholar]
- N. Shehata and A. H. Abed, “Big Data With Column Oriented NOSQL Database To Overcome The Drawbacks Of Relational Databases,” Int. J. Adv. Netw. Appl., vol. 11, no. 05, pp. 4423–4428, 2020, doi: 10.35444/IJANA.2020.11057. [Google Scholar]
- M. J. Suárez-Cabal, P. Suárez-Otero, C. de la Riva, and J. Tuya, “MDICA: Maintenance of data integrity in column-oriented database applications,” Comput. Stand. Interfaces, vol. 83, p. 103642, Jan. 2023, doi: 10.1016/j.csi.2022.103642. [CrossRef] [Google Scholar]
- A. Hillenbrand, U. Storl, M. Levchenko, S. Nabiyev, and M. Klettke, “Towards Self-Adapting Data Migration in the Context of Schema Evolution in NoSQL Databases,” in 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW), 2020, pp. 133–138, doi: 10.1109/ICDEW49219.2020.00013. [Google Scholar]
- S. Bjeladinovic, Z. Marjanovic, and S. Babarogic, “A proposal of architecture for integration and uniform use of hybrid SQL/NoSQL database components,” J. Syst. Softw., vol. 168, p. 110633, Oct. 2020, doi: 10.1016/j.jss.2020.110633. [CrossRef] [Google Scholar]
- T. Fouad and B. Mohamed, “Model Transformation From Object Relational Database to NoSQL Column Based Database,” in Proceedings of the 3rd International Conference on Networking, Information Systems & Security, 2020, pp. 1–5, doi: 10.1145/3386723.3387881. [Google Scholar]
- “acceleo.” [Online]. Available: https://www.eclipse.org/acceleo/. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.