Semantic Query Matching for Automated Metric Retrieval
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Solution Overview
Problem
There is a need to provide users without prerequisite knowledge in data analysis to search and retrieve accurate and actionable metrics and insights from data sources, as manual creation of metrics is time-consuming and inefficient, and relies on extensive knowledge of data source syntax and structure.
Innovation Solution
A method is executed on a computer system to receive a natural language query directed to a data source, generate insights based on predefined types of analyses, and semantically match the query with predefined questions or generated insights, allowing for the selection and display of relevant insights and questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual creation of metrics is performed by data analysts, then accuracy of metrics is improved, but time consumption and inefficiency increase
Solution Approach 1:
The system enables business users to automatically generate and retrieve metrics through natural language queries without requiring data analysts. The automated metric generation system processes user queries, matches them with predefined questions, and returns relevant insights autonomously, eliminating the need for manual metric creation while maintaining accuracy through structured data source connections.
Solution Approach 2:
An automated metric generation system acts as an intermediary between business users and data sources. This intermediary translates natural language queries into structured metric retrieval operations, matching user intent with predefined questions and automatically generating accurate metrics without direct human analysis intervention.
2Ease of operation
If users without data analysis knowledge search for metrics, then accessibility is improved, but accuracy of retrieved metrics may deteriorate
Solution Approach 1:
The automated metric generation system serves as an intelligent intermediary that translates unstructured natural language queries from business users into precise metric retrieval operations. It matches user intent with predefined questions in the database, ensuring accurate metric retrieval even though users lack data analysis expertise.
Solution Approach 2:
The system replaces the mechanical process of manual metric construction (requiring syntax and structure knowledge) with an automated semantic matching process. Natural language queries are processed through AI-driven text matching algorithms that automatically connect user intent with appropriate metrics, eliminating the need for users to understand data source syntax while maintaining retrieval accuracy.
3Speed
If natural language queries are processed without semantic matching, then processing speed is improved, but relevance and accuracy of results deteriorate
Solution Approach 1:
Predefined questions and their associated metrics are prepared and structured in advance within the system. When a natural language query arrives, the system performs rapid semantic matching against this pre-organized knowledge base, significantly reducing processing time compared to generating metrics from scratch while ensuring high relevance through targeted matching.
Data Source
AI summary
A system receives a natural language query directed to a data source, and in response generates insights associated with predefined questions for the data source based on predefined types of analyses. In accordance with a determination that the natural language query semantically matches a respective predefined question, the computer system selects the respective predefined question and associated respective generated insights. In accordance with a determination that the natural language query semantically matches a respective generated insight, selecting the respective generated insight and associated respective predefined question. The system generates instructions for displaying on a display communicatively connected to the system the selected representative predefined question and associated respective generated insights and/or the selected representative generated insight and associated predefined question.