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@article{189073,
        author = {Vishant Shrivastava and Yash Bhati and Vivek Kushwaha and Rani Ranjana},
        title = {Design and Implementation of an Intelligent Virtual Assistant for INGRES Using Al Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4553-4562},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189073},
        abstract = {This paper presents the design and implementation of an intelligent virtual assistant (IVA) for the INGRES database system using modern artificial intelligence (AI) techniques. Conventional interaction with INGRES requires users to understand structured query syntax, which can pose usability challenges for non-technical users. The proposed IVA integrates Natural Language Processing (NLP), Natural Language Understanding (NLU), and Natural Language Generation (NLG) to interpret user utterances, identify intent, and translate natural language requests into executable INGRES operations. Deep learning–based NLU models enhance the system’s capability to handle diverse linguistic variations in user input. A functional prototype was developed to facilitate conversational query processing, assist users in retrieving and manipulating data, and support task-oriented interactions within INGRES. Experimental evaluation using sample user interactions indicates that approximately 75% of requests were correctly interpreted and executed. The results demonstrate that embedding AI-driven conversational intelligence into INGRES can significantly improve accessibility, user experience, and operational efficiency},
        keywords = {Intelligent Virtual Assistant (IVA), Ingres DBMS, Natural Language Processing (NLP), Text-to- SQL, Semantic Parsing, Machine Learning, Artificial Intelligence (AI) Techniques, Database Query Automation.},
        month = {December},
        }