AI-Powered Data Visualization Automation Tool
DOI:
https://doi.org/10.15662/IJEETR.2026.0802065Keywords:
Artificial Intelligence, Data Visualization, Automated Insights, Chart Recommendation, Machine Learning, Busi- ness Intelligence, Data Analytics, Visualization AutomationAbstract
Manual data analysis and visualization remain time-consuming and error-prone processes that require significant domain expertise and technical proficiency. This paper presents an AI-powered data visualization automation tool designed to streamline the transformation of raw data into actionable insights through intelligent chart recommendations and automated analysis. The proposed system accepts multiple data formats (CSV, XLS, XLSX, JSON) and employs machine learning algorithms to automatically select optimal visualization types, generate statistical insights, and present results through an intuitive interface. Built on a Python backend with Firebase authentication and cloud storage, and MySQL for structured data management, the system integrates preprocessing pipelines, feature extraction, and AI-based recommendation engines to minimize manual intervention. Experimental evaluation demonstrates significant improvements in analysis speed, visualization accuracy, and user productivity compared to traditional manual approaches. The system addresses critical gaps in existing automated visualization tools, particularly in tabular data comprehension, user intent mapping, and end-to-end pipeline integration. Results indicate that the tool reduces visualization creation time by approximately 70% while maintaining high accuracy in chart-type recommendations. This work contributes to the growing body of research on human-AI collaborative systems for data analytics and demonstrates practical applications across business intelligence, academic research, and decision-making contexts.
References
1. Y. Wang, Z. Sun, H. Zhang, W. Cui, K. Xu, X. Ma, and D. Zhang, “HAIChart: Human and AI paired visualization system,” IEEE Trans. Vis. Comput. Graph., vol. 30, no. 1, pp. 831–841, 2024, doi: 10.1109/TVCG.2023.3326522.
2. C. Tang, S. Zhao, C. Li, N. Cao, and H. Qu, “ASTA: Automatic spreadsheet transformation and analysis with explainable AI,” ACM Trans. Interact. Intell. Syst., vol. 13, no. 2, pp. 1–28, 2023, doi: 10.1145/3577011.
3. B. Lee, A. Srinivasan, P. Eichmann, and J. Stasko, “SpotLight: Detecting anomalies in streaming graphs,” in Proc. CHI Conf. Human Factors Comput. Syst., Hamburg, Germany, 2023, pp. 1–14, doi: 10.1145/3544548.3581256.
4. L. Yuan, Y. Chen, G. Wang, X. Wang, C. Zhang, and Y. Chen, “Hierarchical prompt learning for multi-task learning on tabular data,” in Proc. Conf. Empirical Methods Natural Lang. Process. (EMNLP), Singapore, 2023, pp. 4521–4533, doi: 10.18653/v1/2023.emnlp-main.276.
5. C. Chen, J. Wu, X. Wang, S. Xie, H. Zhang, Z. Liu, J. Li, and N. Tang, “Infogen: Automatic infographic generation from text,” IEEE Trans. Vis. Comput. Graph., vol. 29, no. 12, pp. 5521–5533, 2023, doi: 10.1109/TVCG.2022.3209469.
6. R. Kumar, S. Sharma, and A. Patel, “AI-powered data visualization platform for automated insights,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 3, pp. 245–256, 2023, doi: 10.14569/IJACSA.2023.0140328.
7. M. Zhang, L. Wang, and H. Liu, “Intelligent business intelligence dashboards with predictive analytics and natural language generation,” Expert Syst. Appl., vol. 213, Art. no. 118912, 2023, doi: 10.1016/j.eswa.2022.118912.
8. S. Zaman, M. A. Islam, S. Haque, and H. A. Mohna, “A review of AI-powered data visualization in enterprise reporting: Dashboard design and interactive analytics,” American Journal of Advanced Technology and Engineering Solutions, vol. 2, no. 1, pp. 32–54, 2022.
9. Y. Zhang and X. Ma, “Visual analytics for decision support,” IEEE Computer Graphics and Applications, vol. 32, no. 5, pp. 14–21, 2012.
10. B. Shneiderman, “The eyes have it: A task by data type taxonomy,” IEEE Symposium on Visual Languages, pp. 336–343, 1996.
11. D. Bumblauskas, H. Nold, P. Bumblauskas, and A. Igou, “Big data analytics: Transforming data to action,” Business Process Management Journal, vol. 23, no. 3, pp. 703–720, 2017.
12. I. Canhoto and F. Clear, “Artificial intelligence and machine learning as business tools,” Business Horizons, vol. 63, no. 2, pp. 183–193, 2020.
13. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
14. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
15. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
16. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
17. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
18. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
19. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
20. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
21. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
22. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
23. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
24. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
25. Dang et al., “Human-centered AI for interactive data visualization,” IEEE Computer Graphics and Applications, vol. 40, no. 3, pp. 76–89, 2020.
26. Holzinger, “Human-centered AI,” Computer, vol. 52, no. 9, pp. 12–19, 2019.
27. F. Hohman et al., “Visual analytics in deep learning,” IEEE Computer Graphics and Applications, vol. 38, no. 6, pp. 84–96, 2018.
28. Perkhofer, M. Hofer, and J. M. Ja¨ger, “Cognitive load reduction in dashboard design,” Information Systems and e-Business Management, vol. 17, no. 4, pp. 737–760, 2019.
29. Satyanarayan, D. Moritz, K. Wongsuphasawat, and J. Heer, “Vega-Lite: A grammar of interactive graphics,” IEEE Trans. Visualization and Computer Graphics, vol. 23, no. 1, pp. 341–350, 2017.
