Authors : Girish R; Swasthik K; Veera Mani Kandan S; Muralidharan S; Vinoth M; Surendar Prakash T

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/3ed77bhb

Scribd : https://tinyurl.com/y78wm5jc

DOI : https://doi.org/10.5281/zenodo.14385083

Abstract : A dynamic and highly customizable dashboard solution is being developed to address the growing need for efficient data analysis and decision- making in organizations. The platform aims to streamline data consolidation from multiple sources and automate reporting processes, offering real-time insights and enhancing the ability of stakeholders to monitor and track key performance indicators (KPIs) relevant to their specific needs. The dashboard will be designed with a user-friendly interface, enabling easy navigation and quick access to actionable data. The core features of the platform will include interactive charts and graphs, allowing users to visualize data in multiple formats, apply filters, and adjust the display based on the unique requirements of various teams. A high degree of customization will be incorporated, giving users the flexibility to tailor the dashboard’s appearance, content, and metrics, ensuring the solution adapts to diverse business scenarios and user preferences .The solution is built to scale alongside enterprise-level needs, allowing for the integration of large data sets and real-time processing. In the short term, the platform will focus on delivering an MVP (Minimum Viable Product) that includes basic data visualization, dashboard creation, and reporting capabilities. Mid-term goals will involve enhancing the user interface with advanced features such as AI-driven insights, predictive analytics, and deeper integration with third-party data sources. Long- term objectives will include the integration of machine learning models for anomaly detection, trend forecasting, and automated report generation, further elevating the decision-making process. This dashboard solution is aimed at empowering business users, managers, and decision-makers by providing them with the necessary tools to turn raw data into meaningful insights, ultimately enabling more informed, data-driven decisions.

References :

1. P. M. N. G. Ms. Naziya Shaikh, "Development of Intermediate Model for Source to Source Conversion," IOSR Journal of Computer Engineering (IOSR-JCE), pp. 1-5, 2021.
2. H. A. O. and N. I. O. Eman J. Coco, "JPT: A Simple Java-Python Translator," An International Journal (CAIJ), vol. 5, no. 2, pp. 1-18, May 2018.
3. N. F. R. J. I. M. Christie Thottam, "Intelligent Python Code Analyzer [IPCA]," International Journal of Creative Research Thoughts (IJCRT), vol. 12, no. 3, pp. 1-11, March 2024.
4. N. M. S., S. J., L. R., Z. R., F. A., R. T., N. N., H. W., and H. H. Chongzhou Fang, "Large Language Models for Code Generation and Translation," ACM Transactions on Programming Languages and Systems (TOPLAS), vol. 45, no. 7, pp. 1-15, July 2023.
5. R. D. Patel and M. K. Vyas, "Automated SQL Query Generator Using Natural Language Processing," International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), vol. 9, no. 4, pp. 34-42, August 2021.
6. Y. Liu, T. Chen, and K. Y. Lee, "AI-Driven Data Visualization and SQL Query Automation," IEEE Transactions on Big Data, vol. 9, no. 2, pp. 112-126, April 2023.
7. J. K. Varun and L. E. Susan, "Towards Efficient SQL Query Generation for Business Intelligence Systems," Journal of Applied Computational Intelligence and Soft Computing, vol. 2022, Article ID 659742, pp. 1-9, 2022.