Data Query Sub-Partitioning for Precision-Based Retrieval
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Solution Overview
Problem
Current databases are unable to meet user requirements for different data precision levels during queries, as they store data based on time, value range, or hash value, leading to inefficiencies in retrieving data that meets specific precision needs for analysis or calculations.
Innovation Solution
A data query method and apparatus that determines a data partition based on query conditions and precision, allowing for the selection of high-precision or low-precision sub-partitions to provide data that meets user-defined precision requirements, using techniques such as the swinging door trending SDT compression algorithm and linear interpolation to classify and retrieve data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored in partitions according to time, value range, or hash value, then storage efficiency is improved, but the ability to meet query requirements of different precision is lost
Solution Approach 1:
The patent segments data within each partition into multiple sub-partitions based on precision levels (e.g., high-precision sub-partition and low-precision sub-partition). This segmentation allows the database to store the same data in multiple precision versions, enabling flexible query responses to different precision requirements while maintaining the original partitioning structure for storage efficiency.
Solution Approach 2:
The patent applies local quality by assigning different precision characteristics to different sub-partitions within the same partition. Each sub-partition is optimized for specific precision levels, allowing the system to provide locally optimized data quality for different query scenarios without compromising overall storage efficiency.
2Measurement precision
If high-precision data is queried for all purposes, then calculation accuracy is improved, but query time increases for operations that do not require high precision
Solution Approach 1:
The patent implements partial action by providing exactly the precision level needed for each query operation. Instead of always retrieving high-precision data, the system queries only the appropriate precision level (high or low) based on the specific query requirements, reducing unnecessary data retrieval and processing time while maintaining sufficient accuracy for each use case.
Solution Approach 2:
The patent changes the precision parameter of data based on query requirements. By maintaining multiple sub-partitions with different precision levels and selecting the appropriate sub-partition based on the query's precision parameter, the system dynamically adjusts data precision to match operational needs, optimizing both accuracy and query performance.
3Speed
If low-precision data is used for tendency analysis, then query speed is improved, but data completeness for detailed analysis is reduced
Solution Approach 1:
The patent changes the precision parameter of stored data to create different versions suitable for different analysis types. Low-precision sub-partitions provide aggregated or summarized data for fast tendency analysis, while high-precision sub-partitions retain complete detailed data when needed, allowing the system to optimize query speed without permanently losing information.
Data Source
AI summary
Embodiments of the present disclosure provide a data query method and apparatus, which implement a technical effect that data meeting a precision requirement is queried according to a user's requirement of for data precision. The method includes: receiving a query instruction that includes a query condition and query precision; determining a data partition that meets the query condition; determining a data sub-partition corresponding to the query precision from the data partition; and querying data in the data sub-partition to obtain a query result.