Metadata Rule Repository for Dynamic Updates Without Redeployment
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Solution Overview
Problem
Existing systems face inefficiencies in generating and storing metadata, requiring complex programming and system redeployment for rule updates, leading to high processing power and memory usage.
Innovation Solution
A method and system for generating metadata representations using a content management server, allowing dynamic rule execution and storage in a repository, decoupling rules from application code, enabling updates without downtime and reducing processing power and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rules are embedded in application code, then system reliability is maintained, but system adaptability deteriorates because rule updates require redeployment
Solution Approach 1:
The patent segments the rule system from the application code by introducing a separate rule repository and rule engine. Rules are stored as independent metadata objects in the repository, while the application code contains only the rule engine that processes these metadata. This segmentation allows rules to be updated independently without requiring application redeployment, directly resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent introduces a rule engine as an intermediary component between the application code and the rules. The rule engine reads rules from the metadata repository, processes them, and executes the appropriate actions. This intermediary layer decouples the rigid application code from the flexible rules, enabling dynamic rule updates without affecting the application code structure or requiring redeployment.
2Measurement precision
If complex rule processing is performed, then measurement precision is improved, but processing power consumption increases
Solution Approach 1:
The patent performs preliminary action by pre-compiling and validating rules during the metadata generation phase. The rule engine pre-processes rule criteria, data field selections, and operator definitions before execution, organizing them into an optimized structure. This preliminary preparation reduces the computational complexity during actual rule evaluation, maintaining measurement precision while reducing real-time processing power consumption.
3Adaptability or versatility
If metadata representations are stored in a repository, then system adaptability is improved, but memory usage increases
Solution Approach 1:
The patent uses copying by creating lightweight metadata representations of rules rather than storing complex executable code. Each rule is represented as a structured metadata object containing essential elements (rule criteria, data fields, operators, actions) in a compact format. This metadata copying approach enables flexible storage and retrieval of rules in the repository while minimizing memory consumption compared to storing full application code copies.
Data Source
AI summary
A method for generating and storing a metadata representation includes receiving a first rule associated with a data object and including a trigger action, a rule criteria, a first rule outcome action associated with the rule criteria being fulfilled, and a second rule outcome action associated with the rule criteria not being fulfilled. The method includes generating and storing the metadata representation. The method further includes receiving a request to execute an action on the data object. The method includes selecting, in response to the trigger action matching the action of the request, the metadata representation from the first repository. The method includes extracting the rule from the metadata representation. The method includes executing the action on the data object. The method includes determining the rule criteria is fulfilled. The method includes executing, in response to the rule criteria being fulfilled, the first rule outcome action.