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Integrating SQL with Machine Learning for Predictive Insights
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
The use of SQL with the help of machine learning allows achieving better results in data analysis by integrating the obligatory data management which takes place in the course of SQL and the possibility of making predictive analytics with the help of machine learning. It makes data pipelines more straightforward by enabling data preprocessing, designing, and evaluation on SQL data structures. These are in-database machine learning, SQL connection to external libraries, stored procedures and Cloud platform services. Such approaches supply scalable, secure along with efficient predictive analysis which accelerate complicated data processing.
Keywords
Structured Query Language, Analytical models, predictive models, analytics
References
[1] Armbrust, M., Xin, R. S., Lian, C., Huai, Y., Liu, D., Bradley, J. K., ... & Zaharia, M. (2015, May). Spark sql: Relational data processing in spark. In Proceedings of the 2015 ACM SIGMOD international conference on management of data (pp. 1383-1394). https://doi.org/10.1145/2723372.2742797
[2] Chaube, S. D. Business Intelligence & Predictive Analytics In Big Data For Big Insights. https://www.ijiras.com/2017/Vol_4-Issue_3/paper_79.pdf
[3] Corey, K. M., Kashyap, S., Lorenzi, E., Lagoo-Deenadayalan, S. A., Heller, K., Whalen, K., ... & Sendak, M. (2018). Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a retrospective, single-site study. PLoS medicine, 15(11), e1002701. https://doi.org/10.1371/journal.pmed.1002701
[4] Garske, T. (2018). Using deep learning on ehr data to predict diabetes (Master's thesis, University of Colorado at Denver). https://www.proquest.com/openview/94ac86106f8a66f9d693854bb2013d9b/1?pq-origsite=gscholar&cbl=18750&diss=y
[5] Golas, S. B., Shibahara, T., Agboola, S., Otaki, H., Sato, J., Nakae, T., ... & Jethwani, K. (2018). A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC medical informatics and decision making, 18, 1-17. https://doi.org/10.1186/s12911-018-0620-z
[6] Kaur, P., Sharma, M., & Mittal, M. (2018). Big data and machine learning based secure healthcare framework. Procedia computer science, 132, 1049-1059. https://doi.org/10.1016/j.procs.2018.05.020
[7] Luo, G. (2015). MLBCD: a machine learning tool for big clinical data. Health information science and systems, 3, 1-19. https://doi.org/10.1186/s13755-015-0011-0
[8] Pala, S. K. (2017). Advance Analytics for Reporting and Creating Dashboards with Tools like SSIS, Visual Analytics and Tableau. https://www.researchgate.net/profile/Sravan-Kumar-Pala/publication/378679002_Advance_Analytics_for_Reporting_and_Creating_Dashboards_with_Tools_like_SSIS_Visual_Analytics_and_Tableau/links/65e366bfc3b52a117006c436/Advance-Analytics-for-Reporting-and-Creating-Dashboards-with-Tools-like-SSIS-Visual-Analytics-and-Tableau.pdf
[9] Pop, D. (2016). Machine learning and cloud computing: Survey of distributed and saas solutions. arXiv preprint arXiv:1603.08767. https://doi.org/10.48550/arXiv.1603.08767
[10] Ruizendaal, R. (2017). The potential of deep learning in marketing: insights from predicting conversion with deep learning (Master's thesis, University of Twente). https://purl.utwente.nl/essays/73655
[11] Wiseman, O. (2016). Using machine learning to predict the winning score of professional golf events on the PGA tour (Doctoral dissertation, Dublin, National College of Ireland). https://norma.ncirl.ie/id/eprint/2493
How to cite this paper
@article{1700644,
author = {Sai Krishna Shiramshetty},
title = {Integrating SQL with Machine Learning for Predictive Insights},
journal = {Iconic Research And Engineering Journals},
year = {2018},
volume = {1},
number = {10},
pages = {287-292},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1700644.pdf},
abstract = {The use of SQL with the help of machine learning allows achieving better results in data analysis by integrating the obligatory data management which takes place in the course of SQL and the possibility of making predictive analytics with the help of machine learning. It makes data pipelines more straightforward by enabling data preprocessing, designing, and evaluation on SQL data structures. These are in-database machine learning, SQL connection to external libraries, stored procedures and Cloud platform services. Such approaches supply scalable, secure along with efficient predictive analysis which accelerate complicated data processing.},
keywords = {Structured Query Language, Analytical models, predictive models, analytics},
month = {April},
}