Natural Language Query Management for Enterprise BI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Business intelligence systems require substantial upfront work by database experts to set up and maintain, making it time-consuming and expensive for business users to query and analyze enterprise data using complex query languages like SQL, especially when dealing with ambiguous table joins and changing reporting requirements.
Innovation Solution
A natural language query management system that enables business users to pose queries in plain language, automatically identifying relevant data sets, grouping them into query domains, prioritizing, and loading data efficiently, thus simplifying data retrieval and analysis without requiring database expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex query languages like SQL are used to query enterprise data, then data retrieval precision is improved, but ease of operation deteriorates due to requiring database expertise
Solution Approach 1:
The patent introduces a semantic layer as an intermediary between business users and the complex database schema. This semantic layer translates natural language queries into executable database queries automatically, allowing users to interact with data using familiar business terminology without needing to understand underlying database structures or SQL syntax.
Solution Approach 2:
The patent replaces the mechanical system of manual SQL query construction with an automated semantic processing system. The system uses natural language processing, ontology mapping, and automated query generation to substitute the manual mechanical process of writing and debugging SQL queries, thereby eliminating the need for database expertise while maintaining query precision.
2Reliability
If database experts perform upfront work to set up and maintain the system, then reliability is improved, but loss of time increases for business users
Solution Approach 1:
The patent performs preliminary action by pre-building and maintaining a comprehensive semantic model and ontology that captures the entire enterprise data landscape before business users need to query data. This pre-configured semantic layer includes pre-established relationships, mappings, and metadata that enable immediate, reliable translation of natural language queries into accurate database queries without requiring time-consuming setup work during actual data exploration.
3Ease of operation
If automated query management is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex query management system into distinct modular components: natural language processing module, semantic processing module, ontology mapping module, and query generation module. Each component handles a specific aspect of the query translation process independently, making the overall complex system more manageable and easier to operate through clear separation of concerns while maintaining automated functionality.
Data Source
AI summary
Systems and methods may automate management of natural language queries of enterprise data. In one example, a method includes performing natural language processing and semantic processing on a natural language query to identify data sets relevant to the natural language query. The method further includes grouping the data sets into one or more query domains based at least in part on one or more relationships among the data sets. The method further includes prioritizing the query domain sets. The method further includes loading one or more of the query domain sets in an order based on the prioritizing of the query domain sets.