METHODS FOR DATA ARHIVING AND RECOVERY USING ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.68302/std2026.vol3.82Keywords:
Artificial Intelligence (AI), Data Backup and Recovery, OptimizationAbstract
In the face of digital renewal, data is one of the most valuable stockpiles for any organization. They not only facilitate operational activity, but also play an important role in strategic decision-making. This data can include everything from clinical trials and client databases, to reports and internal communications. Through data processing, companies can identify weak links in their operations and offer solutions to improve them.
Incorporating artificial intelligence into data archiving processes represents a big step in information management. Methods using artificial intelligence are automated, intelligently indexed, and offer search, which significantly increases the efficiency and accuracy of archival systems.
The report presents methods that compare the evolution and improvement of data archiving and recovery processes through artificial intelligence, while addressing potential challenges and limitations. An assessment of the risk of data loss in artificial intelligence systems was made using the Weibull model to predict failures. A Bayesian distribution flowchart has been developed, demonstrating the efficiency of a data backup system using implemented AI.
Supporting Agencies
This report was financed by the Ministry of Education and Science under the National Science Program "Security and Defense", carried out in implementation of the National Strategy for the Development of Scientific Research 2017-2030 and adopted by Decision of the Council of Ministers No. 731 of October 21, 2021. The material reflects only the author's opinion and the Ministry of Education and Science is not responsible for the content.Downloads
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Copyright (c) 2026 Elena Shtereva
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