PROVISIONING LARGE-SCALED DATA WITH PARAMETERIZED QUERY PLANS: A CASE STUDY
- Details
- Hits: 1603
Volume 4 (1), June 2021, Pages 3-14
Zdzislaw Polkowski1, Sambit Kumar Mishra2
1WSG University, Bydgoszcz, Poland, This email address is being protected from spambots. You need JavaScript enabled to view it.
2Gandhi Institute for Education and Technology, Baniatangi, Bhubaneswar, affiliated to Biju Patnaik University of Technology, Rourkela, Odisha, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
In a general scenario, the approaches linked to the innovation of large-scaled data seem ordinary; the informational measures of such aspects can differ based on the applications as these are associated with different attributes that may support high data volumes high data quality. Accordingly, the challenges can be identified with an assurance of high-level protection and data transformation with enhanced operation quality. Based on large-scale data applications in different virtual servers, it is clear that the information can be measured by enlisting the sources linked to sensors networked and provisioned by the analysts. Therefore, it is very much essential to track the relevance and issues with enormous information. While aiming towards knowledge extraction, applying large-scaled data may involve the analytical aspects to predict future events. Accordingly, the soft computing approach can be implemented in such cases to carry out the analysis. During the analysis of large-scale data, it is essential to abide by the rules associated with security measures because preserving sensitive information is the biggest challenge while dealing with large-scale data. As high risk is observed in such data analysis, security measures can be enhanced by having provisioned with authentication and authorization. Indeed, the major obstacles linked to the techniques while analyzing the data are prohibited during security and scalability. The integral methods towards application on data possess a better impact on scalability. It is observed that the faster scaling factor of data on the processor embeds some processing elements to the system. Therefore, it is required to address the challenges linked to processors correlating with process visualization and scalability.
Keywords:
Scalability, Meta-heuristic, Gradient Value, Supervised learning, Parameterized query
DOI: https://doi.org/10.32010/26166127.2021.4.1.3.14
Reference
Bachmann, D. (2017). Contextual Model-Based Collaborative Filtering For Recommender Systems, M.S. thesis, Dept. Elect. Comput. Eng, Univ. Western Ontario, London, ON, Canada.
Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514-525.
Gruber, S., Logan, R. W., Jarrín, I., Monge, S., & Hernán, M. A. (2015). Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets. Statistics in medicine, 34(1), 106-117.
Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016). A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing, 2016(1), 1-16.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 1-10.
Saeedi, R., Ghasemzadeh, H., & Gebremedhin, A. H. (2016, December). Transfer learning algorithms for autonomous reconfiguration of wearable systems. In 2016 IEEE International Conference on Big Data (Big Data) (pp. 563-569). IEEE.
Singh, D., & Reddy, C. K. (2015). A survey on platforms for big data analytics. Journal of big data, 2(1), 1-20.
Tang, Y., Xu, Z., & Zhuang, Y. (2016, April). Bayesian network structure learning from big data: A reservoir sampling based ensemble method. In International Conference on Database Systems for Advanced Applications (pp. 209-222). Springer, Cham.
Tarasyev, A. M., Vasilev, J., Turygina, V. F., & Panchenko, A. D. (2021, March). Analysis of data on sources of official statistics, development strategy of the Kurgan region. In AIP Conference Proceedings (Vol. 2333, No. 1, p. 150009). AIP Publishing LLC.
Than, M. (2015). Billion Devices Will Wirelessly Connect to the Internet of Everything in 2020, ABI Research.
Wu, Y., Schuster, M., Chen, Z., et al. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
Yang, L., Chu, Y., Zhang, J., Xia, L., Wang, Z., & Tan, K. L. (2015, October). Transfer learning over big data. In 2015 Tenth International Conference on Digital Information Management (ICDIM) (pp. 63-68). IEEE.
