Database Common Data Type Determination for Heterogeneous Storage
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Solution Overview
Problem
Existing database systems face challenges in efficiently storing and managing data with varying types, as they often require manual reconfiguration and are not adaptable to new data types, leading to inefficiencies in storage and query performance.
Innovation Solution
A method and system using a processor and memory circuitry to determine a common data type for groups of data with diverse subsets, employing a data structure like a decision tree to optimize storage and query speed by identifying a broader data type that can represent multiple data types, allowing for automatic adaptation and efficient storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reconfiguration is used to store data with varying types, then storage accuracy is maintained, but device complexity and operational difficulty increase
Solution Approach 1:
The system automatically determines data types and selects appropriate storage formats without manual intervention. The processor analyzes incoming data, identifies data types, and configures storage parameters autonomously, eliminating the need for manual reconfiguration while maintaining storage accuracy.
Solution Approach 2:
The system dynamically changes storage parameters based on data characteristics. When new data types are introduced, the system automatically adjusts storage formats, data types, and database schema parameters to accommodate the new data while maintaining compatibility with existing data.
2Productivity
If the database is configured for specific data types, then query performance is optimized, but adaptability to new data types decreases
Solution Approach 1:
The database schema and storage configuration are made dynamic rather than static. The system automatically adapts the database structure to accommodate new data types while maintaining optimized query performance for existing data types. The processor dynamically adjusts data types, indexes, and query parameters based on the actual data being stored.
Solution Approach 2:
The database system is designed to handle multiple data types through a unified storage mechanism. The system can store different data types (text, numbers, dates, etc.) in a consistent manner while maintaining optimized query performance for each type through automatic type recognition and appropriate indexing strategies.
3Device complexity
If a unified storage format is used for all data types, then device complexity is reduced, but storage efficiency and query performance deteriorate
Solution Approach 1:
The system applies different storage formats and optimization strategies to different data types while maintaining a unified overall architecture. Each data type receives tailored storage handling (e.g., specific indexing for numbers, text compression for strings) while the processor provides a consistent interface for data ingestion and query processing.
Data Source
AI summary
A method comprising, by a processor and memory circuitry, obtaining a plurality of data comprising one or more groups of data, obtaining a data structure usable to determine, for at least a first data type and a second data type, a given data type which is adapted to represent at least both data of the first and second data types for their storage, for at least one given group of data which comprises one or more subsets of data S1 to SN:for each subset of data S1 to SN, determining a data type which is adapted to represent said subset of data for its storage, andusing the given data type of each subset of data S1 to SN and the data structure to determine a common data type which is adapted to represent data belonging to subsets of data S1 to SN for their storage.