Dynamic Transform Schema for API Data Processing

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Solution Overview

Problem

Current data transformation methods through application programming interfaces (APIs) are inflexible, prone to errors, and inefficient, particularly when dealing with dynamic data structures and multi-thread computing, as they rely on static code and serial processing, making them difficult to configure for dynamic optimization and recursive operations.

Innovation Solution

The implementation of a data transformation system using a transform schema defined in meta-language, interpreted during the transformation process, which dynamically directs the transformation and allows for recursive and parallel processing, enabling the transformation of data between various formats and structures through a transformation engine that navigates hierarchical data structures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If static code is used for data transformation, then the transformation process is simple to implement, but it cannot adapt to dynamic data structures and formats

Engineering Contradiction:

Improveadaptability to dynamic data structuresVSAvoidcomplexity of transformation system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies dynamics by implementing a transform schema that can be dynamically configured and modified at runtime. The transformation system uses a metadata-driven approach where transform definitions are stored in a configurable schema that allows dynamic adaptation to different data structures and formats without requiring code changes. This enables the system to handle varying data types, nested structures, and transformation rules flexibly.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent utilizes parameter changes by employing a transform schema that defines transformation parameters in a configurable format. The system allows modification of transformation parameters such as data type mappings, format specifications, and transformation rules through the schema definition. This enables dynamic adaptation to different data structures by changing parameters rather than rewriting the entire transformation logic.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If serial processing is used for data transformation, then the implementation is straightforward, but it cannot take advantage of multi-thread computing for optimization

Engineering Contradiction:

Improvetransformation processing speedVSAvoidcomplexity of processing architecture

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing the data transformation process into independent, manageable units that can be processed in parallel. The transform schema defines discrete transformation operations that can be executed as separate tasks. This segmentation enables the system to distribute transformation operations across multiple threads, allowing simultaneous processing of different data elements or transformation steps, thereby improving productivity through multi-thread computing.

Inventive Principle:

Principle #1Segmentation

3Reliability

If static transformation code is used, then debugging is simpler, but errors are harder to detect and debug in dynamic scenarios

Engineering Contradiction:

Improveerror detection capabilityVSAvoiddifficulty of debugging

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent implements feedback by incorporating validation rules and error checking mechanisms within the transform schema. The system provides feedback during the transformation process by validating input data against the schema definition, detecting errors early, and providing diagnostic information. This feedback mechanism enables reliable error detection and simplifies debugging by providing clear information about what went wrong and where, even in dynamic transformation scenarios.

Inventive Principle:

Principle #23Feedback

4Adaptability or versatility

If metadata-driven transformation is used, then flexibility is improved, but the system complexity increases

Engineering Contradiction:

Improveflexibility in handling different formatsVSAvoidcomplexity of transformation system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies universality by designing a transform schema that serves multiple functions within a single unified framework. The schema definition handles various transformation tasks including data type mapping, format conversion, validation, and error handling through a common structure. This universal approach provides flexibility in handling different data formats and structures while managing system complexity by consolidating multiple transformation functions into a single cohesive system rather than requiring separate mechanisms for each function.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS7970779B2Application interface including dynamic transform definitions

Publication Date: 2011.06.28 ORACLE INT CORP

AI summary

An application programming interface configured to transform data responsive to a transform process definition. The transform process definition is interpreted concurrently with the transformation process. This interpretation dynamically determines navigation within, and processing of, the data to be transformed. Under control of the transform process definition, a transform engine is configured to transform input data using recursive calls to a transformation process. The recursive approach is responsive to data structures within the transform process definition and simplifies operations such as parallel processing, load-balancing of transformation tasks, nesting, un-nesting, filtering, and the like.