Universal self-learning database recovery

Abstract

A self-learning, self-healing database-management system determines that erroneous input, such as an improper SQL query, has caused a critical database condition. The system parses each input statement into a set of tokens, and then translates each set into lines of a failure script. The script is consolidated by cross-referencing each line to infer relationships between input statements. The system then searches historical database logs for previously entered, error-free input similar to the erroneous input. A degree of similarity between the erroneous input and each previously entered input is determined by a computational method that quantifies similarities between the failure script and each script generated from a previously entered input. The system revises the erroneous input to more closely match the most-similar previously entered input and resubmits the corrected input. The results of the resubmission are used to train the system to more accurately correct future input errors.