Integration of Data Mining Techniques to PostgreSQL Database Manager System

TLDR

Data mining extracts patterns or models from data and is widely used across biology, education, finance, industry, policing, and politics, with rule induction and decision trees among the most common techniques. The study aims to analyze decision‑tree data‑mining techniques and induction rules for integration into the PostgreSQL database management system. The authors incorporated several algorithms from these techniques into PostgreSQL, enabling native execution of decision‑tree and rule‑induction operations. Experiments showed that integrating these algorithms into PostgreSQL improved response times and yielded higher-quality results.

Abstract

Data mining is a technique that allows to obtain patterns or models from the gathered data. This technique is applied in all kind of environments such as in the biological field, educational and financial applications, industry, police, and political processes. Within data mining there are several techniques, among which are the induction of rules and decision trees which, according to various studies carried out, are among the most used. This research analyzes decision tree data mining techniques and induction rules to integrate several of its algorithms into PostgreSQL database management system (DBMS). Through an experiment, it was found that when the algorithms are integrated to the manager, the response times and the results obtained are higher.