Dynamic Database Query Construction via Virtual Engine
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Solution Overview
Problem
Current database systems require complex queries to be preprogrammed and recompiled for changes, limiting user accessibility and resource efficiency, especially when using aggregate functions.
Innovation Solution
A computer-implemented method and system that enables users to dynamically construct database instructions using a virtual query generation engine, allowing users to input parameters through a presentation interface, matching user-generated data with template instructions to generate database queries without reengineering system code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If database queries are preprogrammed with specific structures, then query execution is efficient and reliable, but any changes require system code modification and recompilation
Solution Approach 1:
The patent implements dynamic query construction by allowing users to modify query parameters and structures at runtime without recompilation. The system dynamically generates and executes SQL queries based on user input, making the query structure adaptable while maintaining execution reliability through controlled validation and parameter binding.
Solution Approach 2:
The patent introduces an intermediary layer between the user interface and the database engine. This intermediary parses user-friendly query expressions, converts them into proper SQL instructions, and manages parameter binding. This mediator enables adaptability at the user level while preserving reliability in the database execution layer.
2Reliability
If complex database queries are preprogrammed, then query execution is reliable, but user accessibility requires programming knowledge
Solution Approach 1:
The patent provides pre-defined query templates that users can select and customize. These templates represent common query patterns that have been pre-validated for reliability. Users can copy these templates and modify parameters without needing to understand the underlying complex SQL structure, making operation easier while maintaining execution reliability.
Solution Approach 2:
The patent introduces an intermediary layer that translates user-friendly query expressions into proper SQL instructions. This mediator handles the complexity of query construction, allowing users to interact with simple parameter forms while the system generates reliable SQL queries in the background.
3Adaptability or versatility
If system code is modified to retrieve new information, then the database can access new data, but development time and resource requirements increase
Solution Approach 1:
The patent implements dynamic query construction that allows the system to adapt to new data retrieval needs without code modification. Users can define new query parameters and expressions at runtime, and the system dynamically generates the appropriate SQL instructions, eliminating the need for development cycles and recompilation.
Solution Approach 2:
The patent creates a universal query construction framework that can handle various types of database queries through a single system. The template-based approach with parameter substitution allows the same infrastructure to retrieve different types of information, making the system multi-functional without requiring separate code paths for each query type.
4Adaptability or versatility
If database queries use aggregate functions, then data analysis capability is enhanced, but query construction becomes more complex
Solution Approach 1:
The patent provides pre-defined query templates that include common aggregate function patterns (SUM, COUNT, AVG, etc.). Users can select these templates and customize parameters without needing to understand the complex syntax and semantics of aggregate functions, reducing construction complexity while maintaining enhanced data analysis capability.
Solution Approach 2:
The patent introduces an intermediary that automatically handles the complexity of aggregate function construction. The mediator translates high-level user specifications into proper SQL aggregate functions, managing the complexity of data analysis operations while presenting a simplified interface to users.
Data Source
AI summary
A system and method of the subject technology automatically constructs a database instruction from a template instruction, including one or more variable tokens, and one or more document object representations, each including an object type and a user-generated parameter. The one or more variable tokens in the template instruction are substituted by the user-generated parameter of a corresponding document object representation when the type of a respective variable token is matched to the type of the corresponding document object representation.