Inference Engine Plugin Architecture for Custom Data Processing
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Solution Overview
Problem
Conventional data processing approaches are inadequate for running inferences with user-defined logic and generating outputs that differ in format from the input dataset, failing to adapt to customized processes and format changes.
Innovation Solution
A system that accepts an input dataset, accesses predefined logic plugins to process and generate outputs with different formats, including issue, schema, typeclass inference, ontology mapping, or security controls, while validating plugins and managing conflicts with other processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data processing approaches are used, then processing is simple and fast, but the system cannot adapt to user-defined logic or generate outputs in different formats
Solution Approach 1:
The system implements a plugin architecture where multiple logic plugins can be loaded to perform different processing functions. The inference engine can dynamically select and execute appropriate plugins based on user requirements, enabling the system to handle diverse processing tasks including format conversion, data validation, and custom logic execution without requiring separate systems for each function.
Solution Approach 2:
The processing system is divided into independent components: the inference engine, logic plugins, data format handlers, and output generators. Each component performs a specific function and can be independently configured, validated, and executed. This segmentation allows the system to maintain complexity management while providing versatile processing capabilities through modular assembly of components.
2Adaptability or versatility
If format conversion from input dataset to different output formats is implemented, then output versatility is improved, but processing complexity increases
Solution Approach 1:
The system introduces an inference engine as an intermediary component that sits between the input data and output generation. This engine coordinates the conversion process by selecting appropriate logic plugins, managing data flow between different format handlers, and ensuring proper transformation. The intermediary abstracts the complexity of multiple format conversions behind a unified interface.
3Ease of operation
If multiple logic plugins are allowed for customization, then processing flexibility is improved, but validation and conflict management complexity increases
Solution Approach 1:
The system implements validation mechanisms that provide feedback on plugin correctness before execution. The inference engine validates each logic plugin's input-output contracts, data types, and processing logic. Conflict detection mechanisms monitor plugin interactions and provide feedback when incompatible plugins are detected, allowing the system to prevent erroneous executions while maintaining flexible plugin selection.
Data Source
AI summary
Systems and methods are provided for processing an input dataset or running an inference. The systems and methods may be configured to accept an input dataset, access one or more predefined logic plugins for processing the input dataset, process the input dataset based at least in part on a first predefined logic plugin, and generate the one or more outputs based at least in part of the processing of the input dataset. The one or more outputs may have a different format than a format of the input dataset.