Multi-parameter Data Type Framework for Database Systems
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Solution Overview
Problem
Existing techniques for determining an appropriate data type for a set of data values are inadequate, as they often rely on frequency of data types and fail to consider more complex formats and sizes, leading to inefficiencies in data processing and analysis.
Innovation Solution
A multi-parameter data type framework that systematically determines an appropriate data type by considering multiple statistical parameters such as coverage and space, as well as user-defined preferences, and allows for parallel processing to improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing frequency-based techniques are used to determine data types, then the process is simple, but the determination accuracy and ability to handle complex formats is insufficient
Solution Approach 1:
The patent transitions from single-parameter (frequency) data type determination to multi-parameter determination by introducing statistical parameters (coverage, density, cardinality) and format parameters (data format, size, structure). This allows the system to evaluate candidate data types comprehensively using multiple criteria, significantly improving determination accuracy for complex data formats while maintaining manageable system complexity through systematic parameter integration.
2Extent of automation
If manual data type specification is required for complex data sets, then accuracy can be ensured, but the ease of operation and automation level decreases
Solution Approach 1:
The system enables automatic self-determination of data types by analytically discerning the appropriate data type for each column in a data set. The multi-parameter framework automatically evaluates candidate data types using statistical and format parameters, eliminating the need for manual specification while maintaining high accuracy through systematic automated analysis of data characteristics.
Solution Approach 2:
By introducing multiple evaluation parameters (coverage, density, cardinality, data format, size), the system transforms manual determination into automated multi-criteria evaluation. The analytical process automatically compares candidate data types against these parameters and selects the optimal match, achieving both automation and precision simultaneously.
3Adaptability or versatility
If traditional data type determination methods are used, then processing speed is maintained, but the ability to handle growing data sizes and complex formats is limited
Solution Approach 1:
The system performs preliminary statistical analysis on data sets to compute parameters such as coverage, density, and cardinality before data type determination. This pre-computation of statistical characteristics enables efficient evaluation of candidate data types, allowing the system to handle growing data sizes and complex formats without sacrificing processing efficiency, as the heavy lifting of data characterization is done in advance.
Data Source
AI summary
A multi-parameter data type framework can, among other things, provide a more comprehensive, systematic, and/or formal mechanisms for determining an appropriate data type for a data set. For example, the multi-parameter data type framework can be used to allow analytic tools to virtually automatically figure out an appropriate data type for a set of data values.