Implementation of Supervised Learning towards Optimizing Queries in Database Systems
Zdzislaw Polkowski1, Mohanty Anita2, Mishra Sambit Kumar3

1Zdzislaw Polkowski*, Wroclaw University of Economics and Business, Poland.
2Mohanty Anita, Department of Computer Sc. & Engg. Ajay Binay Institute of Technology, Cuttack, Affiliated to Biju Patnaik Uniersity of Technology, Rourkela, Odisha, India.
3Sambit Kumar Mishra, Department of Computer Sc. & Engg, Gandhi Institute for Education and Technology, Baniatangi, Affiliated to Biju Patnaik Uniersity of Technology, Rourkela, Odisha, India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1182-1187 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3531129219/2020©BEIESP | DOI: 10.35940/ijeat.B3531.129219
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Abstract: Machine learning is a technology which with accumulated data provides better decisions towards future applications. It is also the scientific study of algorithms implemented efficiently to perform a specific task without using explicit instructions. It may also be viewed as a subset of artificial intelligence in which it may be linked with the ability to automatically learn and improve from experience without being explicitly programmed. Its primary intention is to allow the computers learn automatically and produce more accurate results in order to identify profitable opportunities. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes human intervention or assistance and adjust actions accordingly. It may enable analyzing the huge data of information. It may also be linked to algorithm driven study towards improving the performance of the tasks. In such scenario, the techniques can be applied to judge and predict large data sets. The paper concerns the mechanism of supervised learning in the database systems, which would be self driven as well as secure. Also the citation of an organization dealing with student loans has been presented. The paper ends discussion, future direction and conclusion.
Keywords: Join enumeration, Join optimization, Query plan, Supervised learning, Symbolic learning.