Modular Data Path Workflow for Flexible Query Processing
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Solution Overview
Problem
Existing AI systems for data searching are inflexible and limited to specific tasks, unable to handle varied queries or integrate with other systems, leading to inconsistent results and inefficiencies in data processing.
Innovation Solution
A computer-executed method and system that converts user queries into data path schemes by selecting appropriate datatypes and linking data modules, allowing for complex task accomplishment through a modular and adaptable AI engine with a learning layer that optimizes performance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single inflexible general method is used to interpret and answer questions in natural language, then the system can handle a broad class of problems, but the quality of query results varies widely because the method is not well-suited to different types of queries
Solution Approach 1:
The system segments the data processing workflow into multiple specialized modules (data retrieval module, data transformation module, data analysis module, data visualization module), each designed to handle specific tasks. This segmentation allows the system to maintain versatility while improving result quality by routing queries to appropriate specialized modules rather than using a single general method.
Solution Approach 2:
The system dynamically selects and chains together different modules based on the specific query requirements. The module selection process adapts to each query's needs, creating a flexible workflow that optimizes result quality for each specific query type while maintaining overall system versatility.
2Manufacturing precision
If data processing and calculations are made so specific to solve a particular problem, then the system can precisely solve that problem, but the system becomes not extensible to other mission requirements and cannot integrate with other systems
Solution Approach 1:
By dividing the system into independent, standardized modules with defined input and output interfaces, the system achieves both precision for specific problems and extensibility for new requirements. Each module can be precisely optimized for its specific function while the modular architecture allows easy integration with other systems.
Solution Approach 2:
The modules are designed with universal interfaces that allow them to work across different mission requirements. For example, the data retrieval module can fetch data from various sources, and the data transformation module can adapt to different data formats, enabling the same modular components to serve multiple purposes.
3Reliability
If fixed workflows with predefined query languages are used, then the system can provide consistent and reliable results for predefined queries, but the system cannot handle varied or unsupported queries
Solution Approach 1:
The system employs natural language processing to automatically interpret and translate user queries into appropriate module chains without requiring predefined query languages. This self-service capability allows the system to handle varied queries while maintaining reliability through automated query understanding and workflow selection.
Solution Approach 2:
The system dynamically adapts its workflow based on the interpreted query meaning rather than following fixed predefined paths. The natural language processing component continuously learns and adapts to different query patterns, enabling the system to maintain reliability for known query types while adapting to new and varied queries.
Data Source
AI summary
A computer system receives a query and converts the query into one or more input data types each associated with a respective piece of input constraint data, and one or more output datatypes corresponding to desired data. A data path workflow scheme is created from the datatypes and a catalog of data path modules each of which has defined datatype of its input(s) and output(s) by building candidate data paths forward from the input and rearward from the output of the data path until data paths from all of the outputs connect with at least one of the data path inputs. The data path is then optimized and converted to an executable code data procedure and then run with the constraint data as the input to the procedure. The results can be displayed to a user display device by presentation modules based on the datatype of the output.