Relational Database UDF Data Normalization
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing and analyzing large volumes of relational data due to time consumption, fragile data models, and lack of inexpensive computing resources, especially when performing supercomputing calculations within relational database systems.
Innovation Solution
The integration of User-Defined Functions (UDFs) within relational database systems enables seamless supercomputing for big data by normalizing input data, performing custom calculations, and updating data models, thereby overcoming bottlenecks in data retrieval and storage, and supporting complex business analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is integrated from multiple sources with different formats, then a complete enterprise view is achieved, but maintenance and support issues increase due to fragmented development
Solution Approach 1:
The patent implements a universal data integration platform that can handle multiple data sources and formats through a common architecture. The system provides reusable integration components and standardized interfaces that work across different data sources, eliminating the need for separate one-off integration applications for each source.
Solution Approach 2:
The integration platform is divided into modular components including data source adapters, transformation rules, and centralized management modules. This segmentation allows independent development and maintenance of specific integration logic while maintaining overall system coherence, reducing the impact of changes on other parts of the system.
2Adaptability or versatility
If custom calculations are performed on large data volumes, then complex business analysis is enabled, but processing time increases
Solution Approach 1:
The system performs data preprocessing and normalization before executing custom calculations. Data is prepared in advance with appropriate transformations applied, so that when custom calculations are requested, the processing time is reduced because the raw data transformation work has already been completed.
Solution Approach 2:
The patent replaces traditional sequential mechanical processing with parallel processing capabilities. Multiple custom calculations can be executed simultaneously on different data partitions, dramatically reducing processing time while maintaining the ability to perform complex business analysis.
3Productivity
If data is normalized for UDF processing, then processing efficiency is improved, but data model complexity increases
Solution Approach 1:
The system introduces a normalization layer as an intermediary between the raw data models and the UDF processing engine. This normalization layer transforms diverse data formats into a standardized structure that the UDF engine can efficiently process, while the complexity is isolated in the normalization logic rather than propagating through the entire system.
Data Source
AI summary
Techniques for processing relational data with a user-defined function (UDF) are provided. Relational input data being requested by the UDF, from within a relational database system, is intercepted and normalized. The UDF is called with the normalized input data and as the UDF produces output data in response to the normalized input data that output data is captured and normalized. In an embodiment, the normalized output data is used to dynamically update a data model within the relational database for the input data.