Schema Transformation Plugin for Application Build Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing application build platforms generate diverse data formats for build processes, making it difficult to analyze and utilize data effectively for improving application builds and executions, as current approaches lack sufficient mechanisms for schema transformation and integration with machine learning models.
Innovation Solution
An application plugin is executed within the runtime environment to identify and transform data from application-internal and external modules, generating a schema transformation that maps source data to a target format compatible with machine learning models, enabling iterative improvements to the build process based on model recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is generated in different formats by different application build platforms, then data diversity and platform versatility are improved, but data analysis difficulty and integration complexity increase
Solution Approach 1:
The patent introduces schema transformation as an intermediary mechanism that mediates between diverse data formats from different build platforms and the standardized input requirements of machine learning models. The schema transformation layer acts as a mediator that automatically converts various source schemas (JSON, XML, CSV, etc.) into target schemas required by ML models, eliminating the need for manual data format adaptation and reducing integration complexity.
Solution Approach 2:
The patent applies parameter changes by dynamically transforming data structure parameters (format, schema, organization) based on the source platform and target model requirements. The system automatically adjusts data parameters through schema transformation rules, converting data from different formats (JSON, XML, CSV) into unified formats that ML models can process, thereby maintaining platform versatility while standardizing data integration.
2Measurement precision
If manual identification of actionable data is required, then data accuracy for analysis is improved, but time consumption and operational effort increase
Solution Approach 1:
The patent implements self-service through automated schema transformation that eliminates manual data identification and preparation. The system automatically performs schema mapping, data extraction, and transformation based on predefined rules and machine learning model requirements, enabling the data processing system to serve itself without human intervention while maintaining high accuracy in identifying actionable data.
Solution Approach 2:
The patent applies preliminary action by pre-defining schema transformation rules and data mapping relationships between different build platforms and ML model inputs. These transformation schemas are established in advance, allowing the system to automatically and accurately transform data without requiring manual identification during the analysis phase, thereby reducing time consumption while maintaining precision.
3Quantity of substance
If comprehensive build data is collected from multiple sources, then data completeness for analysis is improved, but data processing complexity and computational resources increase
Solution Approach 1:
The patent applies the extraction principle by selectively extracting only the relevant and actionable data elements from comprehensive build data using schema transformation rules. Instead of processing all collected data, the system extracts specific data points that match the target schema requirements for ML models, thereby reducing processing complexity while maintaining data completeness for analysis purposes.
Solution Approach 2:
The patent implements segmentation by dividing comprehensive build data into structured segments according to predefined schemas and data categories. The schema transformation process segments data from different build platforms into standardized components that can be independently processed and fed to ML models, reducing overall processing complexity through organized data segmentation.
Data Source
AI summary
Techniques for generating a schema transformation for application data to monitor and manage the application in a runtime environment are disclosed. A system runs an application plugin in a runtime environment to identify data generated by application modules in one or both of an application build process and an application execution process. The application plugin is a software program executed together with the application build process. The application plugin identifies a source schema associated with application data. The application plugin identifies a target schema associated with an analysis program or machine learning model. The application plugin generates a schema transformation to convert application runtime data into a target data set. The system applies the target data set to an analysis program, such as a machine learning model, to generate output analysis data associated with the application.