Data Model Algorithm Interpreter for Production Translation
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Solution Overview
Problem
Existing data model algorithms developed in Python for development mode often require significant rework and reimplementation in production environments, leading to different behaviors and inefficiencies, especially in cluster computing environments, due to the lack of seamless translation tools, which increases costs and complexity and affects data analytics quality.
Innovation Solution
A computing device mechanism comprising an object searcher, interpreter, and translator to search, interpret, and translate data attributes and type information from a development mode data model algorithm into a production-ready data schema, allowing for comparison and validation within a clustered computing environment, enabling seamless transition and efficient data analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If data model algorithms are developed in Python for development mode, then ease of development and rapid prototyping are improved, but translation to production environment requires significant rework and reimplementation
Solution Approach 1:
The patent introduces an intermediary translation system that automatically converts Python data model algorithms into production-ready code in languages like Java or Scala. This intermediary translator handles the complexity of cross-environment translation, allowing developers to write code in Python while automatically generating production-compatible code, thus resolving the contradiction between development ease and translation complexity.
Solution Approach 2:
The translation system performs preliminary actions by automatically generating production environment code during the development phase itself. Instead of requiring separate reimplementation later, the system pre-generates the production code from Python algorithms, maintaining consistency and reducing the need for manual rework while transitioning to production environments.
2Power
If data model algorithms are recreated for production mode using other programming languages, then suitability for processing larger workloads is improved, but behavioral differences and data analytics quality deteriorate
Solution Approach 1:
The translation system creates accurate copies of the Python data model algorithms in production languages like Java or Scala. Rather than manually rewriting logic that could introduce behavioral differences, the system automatically copies and translates the original Python code structure and logic, ensuring that the production version maintains the same behavioral characteristics while gaining the processing power of the target language.
Solution Approach 2:
The system incorporates feedback mechanisms that compare the behavior of translated code against the original Python algorithms. This feedback loop ensures that the translation process maintains behavioral consistency, allowing the system to verify that production code produces the same analytical results as development code while leveraging the superior processing capabilities of production languages.
3Adaptability or versatility
If manual reimplementation is performed for production environment, then adaptability to production requirements is improved, but time consumption and costs increase
Solution Approach 1:
The translation system automatically adjusts parameters such as data types, variable names, and code structure to adapt Python algorithms for production environments. Instead of manual reimplementation, the system dynamically changes code parameters to match production requirements, maintaining adaptability while dramatically reducing the time and resources required for the translation process.
4Productivity
If Python is used for development mode, then productivity and rapid development cycles are improved, but direct deployment to cluster computing environments deteriorates
Solution Approach 1:
The patent employs an intermediary translation layer that bridges Python development mode and cluster computing production environments. This intermediary automatically translates Python code into formats suitable for cluster execution, allowing developers to maintain high productivity by coding in Python while ensuring seamless deployment compatibility with production cluster environments through automated code generation and adaptation.
Data Source
AI summary
A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.