Device Configuration Parsing with Datamodel-Based Vendor Adaptation
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Solution Overview
Problem
Existing systems face challenges in parsing device configurations from diverse vendors due to varying API formats, requiring specialized parsing logic, complex batch processing, and maintaining feature parity, with tools like Antlr and Jolt having limitations in scalability and efficiency.
Innovation Solution
A simplified datamodel-based approach using REST API responses for parsing device configurations, employing a processor to transform and enrich data into a common domain object model, with features like grammar and enricher configurations, back-off strategies, and data aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Antlr is used for configuration parsing, then parsing capability is provided, but system complexity and learning curve increase
Solution Approach 1:
The patent introduces an intermediary layer (the parsing system with grammar files and configuration modules) that mediates between diverse vendor-specific APIs and the common domain object model. This intermediary handles the complexity of multiple parsing strategies, making the system adaptable to different vendors without increasing overall system complexity.
Solution Approach 2:
The patent segments the parsing functionality into modular components: grammar definition files, configuration modules, and parsing strategies. Each vendor-specific parsing logic is separated into independent modules that can be loaded and executed based on the target vendor, reducing the complexity burden on the core system.
2Adaptability or versatility
If Antlr is used for configuration parsing, then parsing functionality is achieved, but maintenance difficulty increases
Solution Approach 1:
The intermediary parsing system with standardized grammar files and configuration modules acts as a buffer that simplifies maintenance. When vendor APIs change, only the specific vendor's parsing module needs updating, not the entire system, making maintenance more manageable.
3Ease of manufacture
If Jolt library is used for JSON transformation, then transformation capability is provided, but data enrichment capability is insufficient
Solution Approach 1:
The patent merges the transformation capability (similar to Jolt) with additional data enrichment functionality into a unified parsing system. The system combines JSON transformation with grammar-based parsing and configuration-driven enrichment, creating a more versatile solution that handles both transformation and enrichment in an integrated manner.
4Adaptability or versatility
If custom logic is added to Jolt for data enrichment, then enrichment capability is improved, but development effort increases
Solution Approach 1:
The patent uses configuration files and grammar definitions as parameters that control the enrichment behavior. Instead of hardcoding enrichment logic, the system changes its behavior based on configurable parameters defined in vendor-specific grammar files, allowing enrichment capability to be adjusted without writing custom code for each scenario.
Solution Approach 2:
The parsing system is designed to be self-configuring through grammar files and configuration modules. When a new vendor is added, the system automatically adapts by loading the vendor's grammar file and configuration, reducing the need for manual custom logic development and lowering development effort.
5Measurement precision
If vendor-specific parsing logic is developed for each vendor, then parsing accuracy is improved, but development time increases
Solution Approach 1:
The patent segments vendor-specific parsing logic into separate, modular grammar files and configuration modules. Each vendor's parsing requirements are handled by dedicated modules that can be independently developed, tested, and maintained, improving parsing accuracy while allowing parallel development to reduce overall development time.
Solution Approach 2:
The patent creates a universal parsing framework that can handle multiple vendors through a common architecture. The framework provides universal functions for parsing, transformation, and enrichment, while vendor-specific adaptations are achieved through configurable grammar files rather than completely separate implementations, reducing overall development time.
6Productivity
If batch processing is implemented for large datasets, then data processing capability is improved, but system complexity increases
Solution Approach 1:
The patent segments large datasets into batches that are processed independently through the parsing system. The batch processing capability is implemented as a separate module that divides input data into manageable chunks, processes them through the existing parsing pipeline, and aggregates results, improving data processing capability without significantly increasing core system complexity.
Data Source
AI summary
An embodiment herein provides a method and a system for parsing device configurations using a simplified datamodel-based approach. The method includes (i) receiving a parse request from one or more sources, (ii) retrieving one or more data models and respective one or more configurations from a resource, (iii) executing communications with a user device using commands retrieved from a command repository by analyzing the retrieved one or more data models, (iv) transforming a response into parsed data by analyzing grammar configuration, (v) enhancing the parsed data by analyzing enricher configuration, and (vi) framing the enhanced data into an output response, thereby parsing device configurations.