Schema-Agnostic Query Templates for Database Efficiency
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Solution Overview
Problem
Current database management systems require significant effort and expense to develop schema-specific queries for datasets with varying schemas, leading to inefficiency, especially for large organizations with numerous datasets.
Innovation Solution
A computer-implemented method and system that generates and applies schema-agnostic query templates using taxonomy tags and schema-taxonomy mappings to automatically translate these templates into schema-specific queries, enabling queries to be executed across different datasets without manual customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If schema-specific queries are manually developed for each dataset, then query accuracy and reliability are improved, but development time and effort increase significantly
Solution Approach 1:
The patent creates query templates that serve as reusable copies of query patterns. Instead of manually developing queries for each dataset, the system copies proven query templates and automatically adapts them to different schemas through schema mapping, significantly reducing development time while maintaining query accuracy through systematic adaptation rules
Solution Approach 2:
The system changes parameters by mapping generic query template elements to schema-specific components. The query templates use parameterized placeholders that are automatically replaced based on schema mappings, allowing the same template to generate accurate queries for different datasets without manual rewriting
2Reliability
If separate queries are written for each dataset schema, then query reliability is improved, but system complexity and maintenance burden increase
Solution Approach 1:
The patent implements universal query templates that can serve multiple datasets with different schemas. A single query template can be applied across numerous datasets by automatically mapping to their respective schemas, eliminating the need to maintain separate queries for each dataset and significantly reducing system complexity
Solution Approach 2:
The system introduces schema mappings as an intermediary layer between generic query templates and schema-specific datasets. This mediator automatically translates template references into schema-appropriate queries, reducing management complexity by centralizing the adaptation logic in the mapping layer rather than in each individual query
3Adaptability or versatility
If manual query customization is performed for each dataset, then query adaptability to specific schemas is improved, but productivity and scalability deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-defining query templates with schema-agnostic placeholders and pre-establishing schema mappings. This preliminary preparation allows queries to be rapidly adapted to new datasets without manual customization, significantly improving productivity while maintaining schema adaptability through the pre-configured mapping framework
Solution Approach 2:
The system segments queries into template definitions and schema mappings. By separating the query logic from schema-specific details, the system enables independent development and reuse of query templates across multiple datasets, improving productivity through modular design while maintaining adaptability through the mapping segmentation
Data Source
AI summary
The present disclosure provides systems and methods that generate query templates that are expressed in a generic schema-agnostic language. The query templates can be generated “from scratch” or can be automatically generated from existing queries, a process which may be referred to as “templatizing” the existing queries. As one example, generation of query templates can be performed through an iterative process that iteratively generates candidate templates over time to optimize a coverage over a set of existing queries. After generation of the schema-agnostic query templates, the systems and methods described herein can automatically translate/map the templatized queries into “concrete,” schema-specific queries that can be evaluated over specific customer schemas/datasets. In this manner, a query template for a given semantic query (e.g., “return the names of all employees”), is required to be written only once.