Software-Defined Systems Integration for Multi-Source Data Unification
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Solution Overview
Problem
Conventional systems integration methods are time-consuming, resource-intensive, and inefficient, struggling with integrating disparate data from various sources into a unified format, leading to inefficiencies, increased computational requirements, and vendor lock-in risks.
Innovation Solution
A software-defined systems integration (SDSI) framework that uses machine learning models, particularly large language models, to transform and unify data from diverse sources into a common format, enabling seamless integration across applications and hardware components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems integration methods are used to integrate disparate data from various sources, then data integration is achieved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent introduces a software-defined systems integration framework that acts as an intermediary layer between disparate data sources and target applications. This framework includes data adapters, correlation engines, and integration modules that automatically transform and unify data from different formats, eliminating the need for time-consuming manual integration processes and reducing integration time significantly
Solution Approach 2:
The system dynamically changes data format parameters by transforming diverse data formats into a unified common format through automated correlation processes. The framework modifies data structure parameters, encoding formats, and schema definitions to enable seamless integration, thereby improving productivity while reducing the time required for format conversion
2Productivity
If conventional systems integration methods are used to integrate disparate data from various sources, then data integration is achieved, but computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring data adapters, correlation rules, and integration templates that automatically handle data transformation. The system performs preliminary data validation, format standardization, and correlation mapping before actual integration, reducing the computational burden during runtime and improving overall integration efficiency
Solution Approach 2:
The software-defined integration framework provides universal functionality by handling multiple data formats, sources, and target systems through a single unified platform. The correlation engine and data adapters can process various data types and formats using the same underlying mechanisms, reducing redundant computational resources and improving integration efficiency
3Adaptability or versatility
If data from multiple sources in different formats is integrated, then unified data asset is generated, but data format differences create complexity
Solution Approach 1:
The patent segments the integration system into modular components including data adapters, correlation engines, integration modules, and output generators. Each segment handles specific aspects of data integration independently, making the system adaptable to different data sources while managing complexity through clear separation of concerns and standardized interfaces
Solution Approach 2:
The framework introduces intermediary components such as data adapters and correlation engines that mediate between diverse data sources and target applications. These intermediaries handle format conversion, validation, and standardization, enabling high data source compatibility while keeping the overall system complexity manageable through abstraction
4Adaptability or versatility
If customized software code is developed for systems integration, then specific integration requirements are met, but development time and resources increase
Solution Approach 1:
The patent uses copying by providing pre-built integration templates, data adapter patterns, and correlation rule templates that can be replicated and adapted for different integration scenarios. Instead of developing custom code from scratch, the system copies and configures existing templates to meet specific requirements, significantly reducing development time while maintaining adaptability
Solution Approach 2:
The framework provides universal integration capabilities through reusable data adapters, correlation engines, and integration modules that can handle multiple integration scenarios. This multi-functional approach allows the system to adapt to different customization requirements without requiring separate custom code development for each case, reducing both development time and resource consumption
Data Source
AI summary
In some examples, systems and methods for systems integration are provided. For example, a method includes: receiving a first data asset in a first data format from a first data source; receiving a second data asset in a second data format from a second data source, the second data format being different from the first data format, the second data source being different from the first data source; performing a correlation process to merge the first data asset in the first data format and the second data asset in the second data format to generate a unified data asset in a common data format, the common data format being different from the first data format, the common data format being different from the second data format; and providing the unified data asset in the common data format to a plurality of software applications.