AI Database Operation Classification for Storage Allocation
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Solution Overview
Problem
Conventional storage management techniques are labor-intensive and error-prone, leading to increased costs and temporal delays in protecting and accessing data, often due to the selection of inappropriate or sub-optimal storage solutions.
Innovation Solution
The implementation of automated database operation classification using artificial intelligence techniques, which involves obtaining queries related to database operations, processing information about storage resources, and performing automated actions based on classification to optimize storage resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional storage management techniques are used, then storage resources can be allocated, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The system enables storage resources to self-assign to database operations by automatically analyzing query characteristics and matching them with appropriate storage resources based on pre-defined criteria, eliminating manual intervention while maintaining intelligent allocation decisions
Solution Approach 2:
The patent replaces manual mechanical storage management processes with an automated AI-based system that uses machine learning models to analyze database queries and determine optimal storage resource allocation, substituting human decision-making with automated intelligent systems
2Productivity
If manual storage resource allocation is performed, then storage decisions can be made, but temporal delays and errors increase
Solution Approach 1:
The system performs preliminary analysis of database query characteristics and pre-determines optimal storage resource allocations before actual data storage occurs, enabling proactive resource assignment that eliminates delays and reduces errors associated with reactive manual allocation
Solution Approach 2:
The automated system operates continuously to monitor and allocate storage resources in real-time as database operations occur, eliminating the interruptions and delays inherent in manual processes while maintaining consistent and accurate storage decisions
3Reliability
If inappropriate storage solutions are selected, then storage allocation occurs, but data protection and access challenges are exacerbated
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor storage performance and data access patterns, using this information to refine and improve the accuracy of future storage resource matching decisions, thereby enhancing both reliability and measurement precision over time
Solution Approach 2:
The patent dynamically adjusts storage allocation parameters based on analyzed query characteristics such as data access patterns, volume, and operational requirements, enabling precise matching of storage resources to specific database operation needs and improving both accuracy and protection
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automated database operation classification using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one query related to one or more database operations; obtaining information pertaining to a set of multiple storage resources; classifying the at least one query as associated with at least one of multiple subsets of storage resources among the set of multiple storage resources by processing at least a portion of the query and at least a portion of the information pertaining to the set of multiple storage resources using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the classifying of the at least one query.