Declarative Data Filtering and Replication Without Procedural Coding
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Solution Overview
Problem
Traditional data management systems require complex procedural programming, leading to inefficiencies, errors, and a lack of integration, making it difficult to adapt to evolving data requirements or integrate new data sources and destinations.
Innovation Solution
A Declarative Compute Framework (DCF) that simplifies data filtering, control, and replication through declarative templates, allowing users to specify desired outcomes without understanding execution processes, and automates data management operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional procedural programming is used for data management, then data handling can be performed, but system complexity and error potential increase significantly
Solution Approach 1:
The patent inverts the traditional procedural approach by adopting a declarative model where users specify desired outcomes rather than execution steps. Configuration templates define what data transformations are needed without detailing the procedural logic, reversing the conventional paradigm and reducing operational complexity.
Solution Approach 2:
Configuration templates serve as intermediaries between user requirements and system execution. These templates abstract complex data management logic into reusable patterns, mediating between high-level user intentions and low-level system operations, thereby reducing both operational and system complexity.
2Reliability
If complex procedural programming is required, then data management functionality is achieved, but user expertise requirements and error rates increase
Solution Approach 1:
The system performs preliminary action by pre-defining configuration templates that encapsulate proven data management patterns. These templates are prepared in advance with best practices embedded, eliminating the need for users to write error-prone procedural code and reducing expertise requirements while improving reliability.
Solution Approach 2:
The declarative configuration system enables self-service data management where users can configure data handling through template-based declarations without deep programming knowledge. The system automatically processes these declarations, reducing errors while lowering the barrier to operation.
3Productivity
If data management and analysis processes are separated, then system modularity is maintained, but integration efficiency and decision-making speed decrease
Solution Approach 1:
The patent merges previously separated data management and analysis processes into a unified declarative framework. Configuration templates can simultaneously define data filtering, transformation, and analysis objectives, enabling integrated processing that improves productivity while the modular template structure manages system complexity.
4Adaptability or versatility
If rigid data management systems are used, then system stability is maintained, but adaptability to evolving requirements and new data sources decreases
Solution Approach 1:
The system introduces dynamics through configurable templates that can be easily modified to adapt to evolving data requirements. The declarative nature allows flexible reconfiguration of data management behavior without structural changes, enabling adaptability while maintaining system stability through consistent processing frameworks.
Solution Approach 2:
Configuration templates provide universal data management patterns that work across multiple data sources and destinations. This multi-functionality enables the system to adapt to various requirements using the same template framework, increasing versatility without compromising structural stability.
Data Source
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AI summary
A computer-implemented method for managing data in a computing environment. In one aspect, a method includes receiving a declarative input that indicates an outcome for data handling, identifying, from the declarative input, a predefined data filter configuration and a predefined data propagation configuration, filtering incoming data according to the predefined data filter configuration to generate filtered data, and replicating the filtered data to a data storage according to the predefined data propagation configuration.