The AI-Powered Evolution of Big Data
The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10176 |
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| author | Yulia Kumar Jose Marchena Ardalan H. Awlla J. Jenny Li Hemn Barzan Abdalla |
| author_facet | Yulia Kumar Jose Marchena Ardalan H. Awlla J. Jenny Li Hemn Barzan Abdalla |
| author_sort | Yulia Kumar |
| collection | DOAJ |
| description | The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes. |
| format | Article |
| id | doaj-art-cccf0a438cdc4412bcc14c7883ee7d6c |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-cccf0a438cdc4412bcc14c7883ee7d6c2024-11-26T17:47:45ZengMDPI AGApplied Sciences2076-34172024-11-0114221017610.3390/app142210176The AI-Powered Evolution of Big DataYulia Kumar0Jose Marchena1Ardalan H. Awlla2J. Jenny Li3Hemn Barzan Abdalla4Department of Computer Science and Technology, Kean University, Union, NJ 07083, USADepartment of Computer Science and Technology, Kean University, Union, NJ 07083, USADepartment of Computer Science, Cihan University Sulaimaniya, Sulaymaniyah City 46001, IraqDepartment of Computer Science and Technology, Kean University, Union, NJ 07083, USADepartment of Computer Science, Wenzhou-Kean University, Wenzhou 325060, ChinaThe rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes.https://www.mdpi.com/2076-3417/14/22/10176AI-enhanced data analyticsRAG-based AI agentsBig D analytical botAI in Big Data management |
| spellingShingle | Yulia Kumar Jose Marchena Ardalan H. Awlla J. Jenny Li Hemn Barzan Abdalla The AI-Powered Evolution of Big Data Applied Sciences AI-enhanced data analytics RAG-based AI agents Big D analytical bot AI in Big Data management |
| title | The AI-Powered Evolution of Big Data |
| title_full | The AI-Powered Evolution of Big Data |
| title_fullStr | The AI-Powered Evolution of Big Data |
| title_full_unstemmed | The AI-Powered Evolution of Big Data |
| title_short | The AI-Powered Evolution of Big Data |
| title_sort | ai powered evolution of big data |
| topic | AI-enhanced data analytics RAG-based AI agents Big D analytical bot AI in Big Data management |
| url | https://www.mdpi.com/2076-3417/14/22/10176 |
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