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@article{197763,
        author = {Anurag Kumar and Mrs. Mukta Ashish Patel},
        title = {AutoDash : An Intelligent Framework for Automated Dashboard Generation Using ETL and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7755-7761},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197763},
        abstract = {In today’s data-driven environment, organizations rely heavily on dashboards to monitor performance, analyze trends, and make informed decisions. However, traditional dashboard creation is often time-consuming, complex, and dependent on skilled professionals. This limitation becomes more significant when dealing with large-scale or real-time data.
This paper presents AutoDash, an intelligent and automated framework designed to simplify and accelerate the dashboard generation process. By integrating Extract, Transform, Load (ETL) pipelines with machine learning (ML) techniques, AutoDash eliminates the need for manual intervention across various stages, including data extraction, preprocessing, feature selection, and visualization.
The system leverages machine learning algorithms to identify important features and automatically recommend suitable visualizations based on data characteristics. It supports both structured and semi-structured data and is capable of adapting to real-time data streams.
Experimental results demonstrate that AutoDash significantly reduces dashboard creation time while improving preprocessing accuracy and overall user experience. The proposed framework provides a scalable and efficient solution for modern business intelligence applications across domains such as finance, healthcare, and retail.},
        keywords = {Automated Dashboards, ETL Pipeline, Machine Learning, Data Visualization, Business Intelligence, Real-Time Analytics},
        month = {April},
        }