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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.14874188 |
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| _version_ | 1866902091599970304 |
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| author | Nusrat Yasmin Nadia MD Shadikul Bari Mohammed Majid Bakhsh Ankur Sarkar S A Mohaiminul Islam |
| author_facet | Nusrat Yasmin Nadia MD Shadikul Bari Mohammed Majid Bakhsh Ankur Sarkar S A Mohaiminul Islam |
| contents | <p>This requirement has become more so as BI applications expand in terms of functionality and volume where TDM has been deemed more important. Currently, there is a lot of hassle with regard to both the generation, management and validation of test data used in traditional testing processes, that does not adequately address the adaptive requirements of contemporary BI solutions leading to decreased efficiency, inadequate quality and incoherent outcomes as well as unrealistic data fidelity. Finally, this article aims to look at how AI technology can rise to the occasion of these Alarge-scale BI applications by analyzing the case of AI-assisted TDM. It continues with the discussion of test data generation using automated means and presents some practical AI methods as machine learning or the application of neural networks and generative models in this concern. In addition, we explore the ways in which BI systems can incorporate AI to solve a number of questions concerning large quantities of data, data selection, and the validation of the results achieved through synthetic data. Main characteristics of AI-based TDM platform such as large scale processing, integration of synthetic data, and optimized data handling are described together with case studies that illustrate benefits for software testing effectiveness and BI app quality. Moreover, the paper presents the limitations and drawbacks of the AI-based solutions, including the possibilities of data privacy and ethical issues and how to overcome them and the barriers to organizational adoption of AI and its solutions. Finally, possibilities and the general trend in the future of AI in BI testing are discussed, and the future development of creating new and more efficient applications of AI in BI testing for enhancing analytics and reporting is recommended.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14874188 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Driven Test Data Management for Large-Scale BI Applications Nusrat Yasmin Nadia MD Shadikul Bari Mohammed Majid Bakhsh Ankur Sarkar S A Mohaiminul Islam <p>This requirement has become more so as BI applications expand in terms of functionality and volume where TDM has been deemed more important. Currently, there is a lot of hassle with regard to both the generation, management and validation of test data used in traditional testing processes, that does not adequately address the adaptive requirements of contemporary BI solutions leading to decreased efficiency, inadequate quality and incoherent outcomes as well as unrealistic data fidelity. Finally, this article aims to look at how AI technology can rise to the occasion of these Alarge-scale BI applications by analyzing the case of AI-assisted TDM. It continues with the discussion of test data generation using automated means and presents some practical AI methods as machine learning or the application of neural networks and generative models in this concern. In addition, we explore the ways in which BI systems can incorporate AI to solve a number of questions concerning large quantities of data, data selection, and the validation of the results achieved through synthetic data. Main characteristics of AI-based TDM platform such as large scale processing, integration of synthetic data, and optimized data handling are described together with case studies that illustrate benefits for software testing effectiveness and BI app quality. Moreover, the paper presents the limitations and drawbacks of the AI-based solutions, including the possibilities of data privacy and ethical issues and how to overcome them and the barriers to organizational adoption of AI and its solutions. Finally, possibilities and the general trend in the future of AI in BI testing are discussed, and the future development of creating new and more efficient applications of AI in BI testing for enhancing analytics and reporting is recommended.</p> |
| title | AI-Driven Test Data Management for Large-Scale BI Applications |
| url | https://doi.org/10.5281/zenodo.14874188 |