Application of Artificial Intelligence Technologies for Effective Organization of User Queries in a Database

In this research, the explored topic is the integration of Artificial Intelligence (AI) and databases to create a user-friendly system capable of suggesting queries to users. The experiment utilizes the SQLite database management system integrated into Google Colab, with the Chinook database as a representative sample. The AI system stores and recalls user-specific queries in a dedicated database for suggestions, and certain similarity measures such as Levenshtein distance, Jaro-Winkler distance and Cosine similarity enhances the accuracy of query suggestions. Despite some limitations in data security and preprocessing techniques, this research provides valuable insights into creating efficient and personalized query suggestions for enhancing user experiences in database systems. By bridging the gap between AI and databases, the mentioned systems envision a future where users can seamlessly navigate and retrieve information with ease and precision.