Dynamic Query Transformation for Data Retrieval
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Solution Overview
Problem
Current data management systems are inflexible and require extensive developer intervention for modifying data models, leading to inefficient query optimization and data retrieval, limiting user configuration capabilities and performance.
Innovation Solution
A system and method for dynamic data retrieval that involves receiving queries in a first language, splitting them into sub-queries, transforming these sub-queries into appropriate languages for different datasets, processing them to retrieve results, and generating data presentations, allowing for on-the-fly configuration and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data models are hard coded in source code, then query optimization can be achieved, but any modification requires developer intervention which is time-consuming and reduces flexibility
Solution Approach 1:
The system dynamically configures data models at runtime through configuration files rather than hardcoding them in source code. This allows the data models to be modified and adapted without requiring developer intervention, resolving the contradiction between maintaining query optimization reliability and enabling data model flexibility.
Solution Approach 2:
The system changes the state of data models from fixed source code parameters to configurable runtime parameters. By externalizing data model definitions into configuration files, the system enables parameter changes without recompilation or redeployment, thus improving adaptability while preserving query optimization capabilities.
2Ease of operation
If pre-defined star schema is used in GUI tools, then direct SQL database connection is enabled, but the system becomes static and not easily programmable
Solution Approach 1:
The system creates a universal query processing framework that can handle multiple query types and data models through a common configuration-based approach. The query processing module can work with different data models (star schema, snowflake schema, etc.) and generate various types of queries, making the system both easy to operate and highly programmable.
Solution Approach 2:
The system introduces configuration files as an intermediary layer between the user interface and the database. This intermediary enables programmability by allowing custom query generation while maintaining ease of operation through standardized configuration formats, resolving the contradiction between static pre-defined schemas and flexible programmability.
3Adaptability or versatility
If custom analytical systems are built per use-case, then specific requirements are met, but users cannot benefit from shared infrastructure leading to low performance
Solution Approach 1:
The system implements a universal analytical platform that serves multiple use-cases through a shared infrastructure. The common query processing module, configuration management system, and data model framework enable different analytical scenarios to benefit from the same optimized codebase, improving performance while maintaining adaptability to specific requirements.
Solution Approach 2:
The system merges multiple custom analytical systems into a single unified platform by combining their common functionalities into shared modules. This consolidation eliminates redundant code and enables performance optimization to be shared across all use-cases, resolving the contradiction between use-case specificity and system performance.
4Reliability
If developers hardcode analytical systems, then query optimization is achieved, but user configuration capability is eliminated
Solution Approach 1:
The system enables users to perform query optimization and data model configuration themselves through configuration files, eliminating the need for developer intervention. Users can modify their analytical requirements directly by editing configuration parameters, maintaining query optimization reliability while providing ease of operation through self-service capabilities.
Data Source
AI summary
A method and system to retrieve data dynamically from one or more datasets are disclosed. The method comprises receiving at least one query in a first query language. Thereafter, splitting the at least one query in the first query language into a plurality of sub-queries and transforming the plurality of sub-queries into one or more second query languages, wherein the one or more second query languages correspond to the one or more datasets. Other embodiments include a method that comprises processing the one or more transformed queries to retrieve one or more query results from the one or more datasets, and processing the one or more query results to generate a data presentation.