Polyglot Query Adapter for Unified Code Execution
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Solution Overview
Problem
Conventional techniques for producing predictive analytics models in polyglot computing environments face challenges due to misalignment between technical and data science resources, leading to inefficiencies and unsatisfactory results in real-time big data processing and machine learning applications.
Innovation Solution
A polyglot analysis system that adapts instructions between different programming languages, enabling native execution of code in a single execution environment, allowing seamless referencing and execution across language boundaries without translation, and facilitating easy composition and customization of tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional ETL and coordinate release processes are used between data scientists and application developers, then predictive analytics models can be produced, but large amounts of overhead time and coordination effort are required
Solution Approach 1:
The patent introduces a polyglot query adapter as an intermediary component that automatically translates and adapts queries between different programming languages (e.g., Python, R, SQL) and the underlying database system. This intermediary eliminates the need for manual coordination between data scientists and application developers, as the adapter handles language compatibility automatically, thereby reducing overhead time while maintaining model production capability.
2Adaptability or versatility
If data scientists use specialized statistical tools like RTM SaSTM and MATLABTM, then sophisticated predictive analytics models can be built, but the models cannot be directly integrated into online transaction systems using different programming languages
Solution Approach 1:
The polyglot query adapter is designed with universal functionality to handle multiple programming languages and query types simultaneously. It can adapt queries from Python, R, SQL, and other languages to the underlying database system using a single unified interface, eliminating the need for separate integration processes for each language and reducing overall integration complexity.
Solution Approach 2:
The adapter dynamically changes parameters such as query syntax, data types, and execution modes based on the source language being used. By automatically adjusting these parameters according to the input language, the system maintains compatibility with specialized statistical tools while enabling seamless integration into online transaction systems without requiring complex manual translation processes.
3Reliability
If manual translation of analyses from statistical tools to online transaction system languages is performed, then models can be deployed, but the process requires iterative coordination to avoid mismatches between developer interpretation and data scientist intention
Solution Approach 1:
The polyglot query adapter serves as a reliable intermediary that preserves the original intent of data scientists by automatically translating queries without manual intervention. This eliminates interpretation mismatches because the adapter maintains semantic equivalence between the source query and the executed query, ensuring reliability while enabling rapid deployment through automated translation.
Solution Approach 2:
The adapter creates accurate copies of the original statistical queries in the target language, preserving the exact logic and intent of the data scientist's analysis. This copying mechanism ensures that the deployed model faithfully represents the original analysis while enabling fast deployment through automated translation rather than manual rewriting.
Data Source
AI summary
Systems, methods, and devices for polyglot computing may include, in a single execution, adapting at least one first instruction in a first language and at least one second instruction in the first language into a code comprising the at least one first instruction in the first language and the at least one second instruction in a second language; producing a first result in the first language based on executing the code using a dataset in a data store, wherein the producing the first result in the first language is performed in the single execution; producing a second result in the second language based on executing the code using the dataset; and adapting the second result in the second language into the second result in the first language.