Self-Service Data Integration with GraphQL-Based Selective Queries
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Solution Overview
Problem
Existing data integration approaches in information processing systems, particularly in enterprise environments, lead to increased IT integration complexity, resource burden, and scalability issues due to point-to-point integrations, resulting in computer processing delays, data storage shortages, and communication network congestion.
Innovation Solution
Implementing a self-service data integration approach that allows data consumers to selectively choose the data they need, using GraphQL schema generation and consumer-driven data accrual, reducing the need for extensive design and testing and minimizing resource requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If point-to-point integration is used, then integration between applications is achieved, but IT integration complexity increases and resource burden increases
Solution Approach 1:
The patent segments the integration architecture by introducing a data contract layer that separates data publication from data consumption. Each application only needs to implement its specific data contract rather than integrate with all other applications directly, reducing overall integration complexity while maintaining connectivity.
Solution Approach 2:
The data contract serves as an intermediary between data producers and consumers. It defines a standardized interface that mediates interactions, allowing applications to communicate without direct point-to-point connections, thereby reducing integration complexity and resource requirements.
2Ease of manufacture
If point-to-point integration is used, then data exchange between applications is enabled, but compute and storage resources are overburdened
Solution Approach 1:
The patent extracts only the necessary data fields required by each consumer through selective data contract implementation. Instead of transferring complete datasets through point-to-point connections, the system extracts and transmits only relevant fields, reducing compute and storage resource consumption while maintaining data exchange functionality.
Solution Approach 2:
The data contract enables partial data retrieval where consumers only process the specific fields they need rather than receiving complete datasets. This partial action approach reduces resource consumption by avoiding unnecessary data transfer and processing while maintaining adequate data exchange capability.
3Ease of operation
If payload structure is fixed by upstream application, then data format is standardized, but any changes affect multiple downstream applications
Solution Approach 1:
The patent introduces dynamic data contracts that can be independently modified by producers without affecting consumers. Each data contract can be updated or extended selectively, allowing the system to adapt to changing requirements while maintaining backward compatibility with existing consumers through optional fields and versioning mechanisms.
Solution Approach 2:
The data contract is segmented into optional and required fields, allowing selective modification. Producers can add new optional fields to enhance functionality without breaking existing consumer applications, thereby maintaining adaptability while preserving standardized data format operations.
4Measurement precision
If comprehensive data is transferred to ensure data completeness, then data accuracy is maintained, but network congestion and processing delays occur
Solution Approach 1:
The patent extracts only the necessary data fields required by each consumer through selective data contract implementation. Instead of transferring complete datasets, the system extracts and transmits only relevant fields, reducing network bandwidth consumption and processing delays while maintaining data accuracy for the intended purposes.
Solution Approach 2:
Different data contracts can be tailored to local consumer requirements, allowing each application to receive only the specific data fields it needs. This local quality approach optimizes data transmission by avoiding unnecessary data transfer while ensuring data accuracy for each consumer's specific use case.
Data Source
AI summary
Techniques are disclosed for data management comprising self-service data integration functionalities in information processing systems. For example, a method includes accessing, by a first processing node of an information processing system associated with a data consumer, a schema for a dataset of a data publisher associated with a second processing node of the information processing system. The method utilizes, by the first processing node, the schema to select at least a subset of the dataset. The method generates, by the first processing node, a query to obtain the selected subset of the dataset from the second processing node. The method sends, by the first processing node, the query to the second processing node and then receives, at the first processing node, the subset of the dataset from the second processing node.