Database Query Translation System for Technical Experts
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Solution Overview
Problem
The complexity of graph query language makes it difficult for technical experts to efficiently retrieve and utilize data from databases, requiring collaboration with data analytics experts, which hinders the effective use of data analytics methods in fields like continuous flow engines and compressors.
Innovation Solution
A method and system that convert instructions and data between user interfaces and graph query language, allowing technical experts to simplify search strategies and automate data processing, utilizing machine learning and natural language interfaces to facilitate data retrieval and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If graph query language is used to retrieve data from the database, then data retrieval capability is improved, but the complexity of the query language makes it difficult for technical experts to use
Solution Approach 1:
The patent introduces an intermediary system that translates natural language queries from technical experts into graph query language. This mediator includes components for parsing natural language, converting to graph query syntax, and executing the translated queries against the database, thereby bridging the gap between user-friendly input and powerful data retrieval capabilities.
Solution Approach 2:
The patent replaces the mechanical requirement for experts to manually learn and write complex graph query language with an automated translation system. The system uses natural language processing and machine learning algorithms to automatically convert simple user queries into optimized graph queries, eliminating the need for experts to directly interact with the complex query language.
2Productivity
If technical experts collaborate with data analytics experts to use data analytics methods, then data analysis capability is improved, but the workflow complexity and time required increase
Solution Approach 1:
The patent enables technical experts to independently perform data analytics tasks through the automated system. The system provides self-service capabilities by automatically translating their natural language queries into graph queries, executing them, and presenting results without requiring collaboration with data analytics experts, thereby empowering users to conduct their own analyses.
Solution Approach 2:
The patent implements preliminary action by pre-compiling and optimizing the translation rules and query templates in advance. The system prepares the translation mechanisms and data access pathways beforehand, so that when technical experts submit their queries, the complex translation and execution processes can proceed efficiently without ad-hoc coordination or delays.
3Ease of manufacture
If manual data structuring and maintenance is performed, then small amounts of data can be managed, but it becomes problematic when dealing with large amounts of data
Solution Approach 1:
The patent replaces manual data structuring and maintenance with automated systems. The system automatically parses unstructured or semi-structured data, converts it into the appropriate graph database format, and maintains data integrity through automated validation and relationship establishment, eliminating the need for manual intervention regardless of data volume.
Solution Approach 2:
The patent implements preliminary action by automatically structuring and organizing data as it is ingested into the system. The data transformation and validation processes occur in advance before the data needs to be queried or analyzed, so that when large volumes of data are present, they are already properly structured and ready for efficient retrieval without requiring manual organization.
Data Source
AI summary
A method, system, and computer program product for improving the possibilities to retrieve data from a database, includes receiving instructions from a user interface and providing data to the user interface, wherein the database contains a plurality of datasets and a plurality of relationships of the datasets. The database provides a search interface based on graph query language. Data is exchanged by the search interface based on the graph query language. Instructions entered in the user interface being at least partially not in graph query language are processed and/or data retrieved from the database being in graph query language are processed.