Advanced Strategies for Big Data Resource and Storage Optimization: An AI Perspective

The increasing use of advanced technologies with artificial intelligence in our daily lives has become an urgent necessity to facilitate tasks in a fluid and simple way, which leads to the generation of huge amounts of data. This data comes from various sources: media, social networks, connected objects., online transactions, and smart devices, among other sources. These data are generally organized into three categories: structured, unstructured, and semi-structured. This data is therefore called Big Data. This data is characterized by its enormous size and fast flow, as well as by the diversity of its sources. The importance of data lies in its ability to provide future perspectives and improve the decision-making process. To get the most out of this data, it must be stored and processed, but current technologies face many challenges and are often insufficient to cope with the huge amounts of data generated. It is necessary to look for advanced and highly efficient technologies, capable of storing the entirety of the data and processing it faster. We can also rely on artificial intelligence to help improve the use of storage and processing resources by compressing data or deleting excess data, thus saving storage space. This study discusses various approaches for optimizing Big Data processing, such as the use of AI compression techniques, the PSNR-SSIM method, and many others. The compression ratio for these algorithms is around 90%. With these technologies, it is possible to optimize the use of storage space, ensuring efficient and optimized management.

@article{Sghir2025,
  title     = {Advanced Strategies for Big Data Resource and Storage Optimization: An AI Perspective},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ayoub Sghir and Ayoub Allali and Najat Rafalia and Jaafar Abouchabaka},
  doi       = {10.14569/IJACSA.2025.0160896},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160896}
}