Automated Query Suggestion Engine for Data Integration
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Solution Overview
Problem
Current software development and data integration systems require users to learn specific query languages and syntaxes for different databases and APIs, making it cumbersome to perform queries across disparate systems, especially in complex business integration scenarios where data needs to be exchanged between different software applications and systems across various geographical locations.
Innovation Solution
An automated data set query suggestion system that allows users to select previously executed queries in natural language, which are then translated into proper query syntax for execution, enabling users to model and execute data integration processes without needing to understand the underlying query languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually construct queries for different databases and APIs, then query precision and data retrieval accuracy are improved, but the complexity of learning and operating across multiple systems increases
Solution Approach 1:
The system introduces a query suggestion engine as an intermediary between the user and multiple database/API systems. This engine automatically generates and provides query parameter suggestions based on previously executed queries, eliminating the need for users to manually learn and construct complex queries for each system while maintaining accurate data retrieval through automated query generation
Solution Approach 2:
The system enables self-service by automatically generating query parameters without requiring user intervention in the complex query construction process. The query suggestion engine autonomously analyzes previously executed queries and generates appropriate query parameters for current requests, allowing users to simply select from suggestions rather than manually constructing queries
2Productivity
If the system stores and analyzes previously executed queries to generate suggestions, then query generation efficiency is improved, but the complexity of managing query history and metadata increases
Solution Approach 1:
The system extracts only the essential query parameters and metadata from previously executed queries, storing them in a simplified structured format. By taking out only the necessary information (query parameters, timestamps, success status) rather than storing complete query histories, the system achieves efficient query suggestion generation while minimizing the complexity of data management and storage
Data Source
AI summary
An information handling system operating an automated data set query suggestion system may comprise a processor executing code instructions for identifying a data set through an API query, a GUI receiving a user-selected query object, and the processor generating a natural language sentence describing a suggested API query, based on node and edge values for a previously executed query database describing previously executed API queries. The suggested API query may have include a suggested query object, a suggested query operator, or a suggested API to be queried, associated in the previously executed query database with the user-selected query object. The GUI may receive a user instruction to perform the suggested API query, the processor may automatically generate a set of code instructions for future execution of the suggested API query, and a network interface device may transmit the code instructions for future execution by a runtime engine for execution at a remote location.