<![CDATA[Disaster Recovery Centers (DRC) play a crucial role in ensuring the availability and continuity of database operations in enterprise environments. The process of restoring databases from production servers to DRCs is often performed manually, which can lead to errors such as selecting incorrect backups, corrupted files, and lengthy search times. The complexity increases with the growing number of databases and the variety of daily backup types.This study develops an automated system based on a Python Web Interface integrated with Advanced Information Retrieval (IR) to improve the accuracy and speed of finding relevant backups before restoration. The system employs Natural Language Processing (NLP) and multi-criteria relevance scoring, evaluating backup suitability based on fuzzy matching of database names, recency, semantic similarity, backup type, and file size.Testing was conducted using 28 backup records from 5 different databases. Results show that Advanced IR can accelerate backup searches in under 2 seconds, with relevance ranking ranging from 38% to 67%. Additionally, the automated restore process via Python achieved an average execution time of 7.49 seconds with a 100% success rate.]]>
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