Database Configuration Item Updates Without Full Discovery
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Solution Overview
Problem
Existing methods for modifying configuration items (CIs) in computing systems are time-consuming, prone to errors, and lack user-friendly interfaces, leading to data inconsistencies and operational inefficiencies due to the need for full discovery cycles and manual data entry.
Innovation Solution
A database management service utilizing a machine learning model, such as a neural network, to parse input information and match it to predefined commands for modifying CIs, allowing for quick synchronization and creation of shallow CIs without relying on complete discovery cycles, while ensuring data integrity and accessibility through a command line interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If full discovery cycles are performed to modify configuration items, then data accuracy is improved, but time consumption increases
Solution Approach 1:
The patent applies partial action by performing discovery only for specific configuration items that need modification rather than executing complete discovery cycles across the entire database. The system identifies and modifies only the relevant CIs based on input parameters, thereby maintaining data accuracy for modified items while avoiding the time cost of comprehensive discovery.
Solution Approach 2:
The patent segments the configuration item database into manageable units that can be independently modified. By breaking down the modification process into discrete CI-level operations rather than system-wide discovery cycles, the system achieves precise updates with minimal time investment.
2Adaptability or versatility
If manual data entry is used to modify configuration items, then flexibility is improved, but error rate increases
Solution Approach 1:
The patent implements self-service by enabling users to modify configuration items directly through automated commands and interfaces without requiring manual data entry. The system automatically retrieves, validates, and updates CI data based on user input, eliminating human error while maintaining operational flexibility.
Solution Approach 2:
The patent replaces the mechanical process of manual data entry with automated computational systems. Machine learning models and command-line interfaces automatically process modification requests, validate data integrity, and execute updates, substituting human manual operations with error-free automated mechanisms.
3Stability of the object's composition
If comprehensive discovery is performed to ensure data integrity, then data consistency is improved, but operational efficiency decreases
Solution Approach 1:
The patent applies partial action by performing discovery and validation only for the specific configuration items being modified rather than executing comprehensive discovery across the entire database. This selective approach maintains data consistency for affected CIs while preserving operational efficiency by avoiding unnecessary system-wide scans.
Solution Approach 2:
The patent performs preliminary validation and pattern matching on input parameters before executing modifications. By pre-validating data integrity and checking for required patterns upfront, the system ensures data consistency is maintained without requiring subsequent comprehensive discovery cycles, thereby improving operational efficiency.
Data Source
AI summary
Various implementations disclosed herein include obtaining data indicative of a request to modify a configuration item (CI) of a database and one or more portions of a pattern applicable to a service based, at least in part, on the request. A command is identified based on the one or more portions and one or more values of the CI are modified using the command.