Evaluation and Comparison of Data Mining and Machine Learning Capabilities Within Relational Database Management SystemsAntolović, Vanda (Avtor) Kukar, Matjaž (Mentor) machine learningdata miningRDBMSclassificationregressiontraining timeThe evolution of data and data science technology started bringing machine learning algorithms to the data to ease the process of training and reduce the possibility of data corruption by transfers from system to system. We picked five combinations of Relational database management systems and integrated or semi-integrated machine learning toolsets - SQLite with Python, PostgresML with Python, MariaDB with MindsDB, PostgreSQL with MindsDB, and Oracle with Oracle Machine Learning. All five combinations were compared with the help of predictive performance and the training time they have achieved over seven datasets. MariaDB with MindsDB had the slowest training time, while MindsDB in general could not evaluate datasets containing longer strings or produce qualitative measures for assessing datasets with a regression target value, such as proper measurement of squared differences between the actual values and the estimated values. Oracle with Oracle Machine Learning produced the best results, as it was able to accurately evaluate all datasets with a fast training time. Even though the same is true for Python with SQLite, data had to be optimized and transformed into numerical for the main Python machine learning library, Scikit-learn, to be able to process the data. Considering all of that, a simple decision support system was created to help make a sensible decision on which toolset to use to suit the user’s needs.20222022-12-09 14:10:00Magistrsko delo/naloga143255VisID: 34781COBISS_ID: 136461059sl