Data Storage Model Optimization with Keyed Raw and Change Data
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Solution Overview
Problem
Existing methods for storing data suffer from low storage efficiency and waste of resources due to the sequential writing of data in each cycle without considering parameter consistency and missing parameters.
Innovation Solution
A method and apparatus for storing data that involves acquiring a to-be-stored data set and a storage model, determining storage key values based on parameter consistency, identifying and adding missing parameters, and storing original and change data efficiently using these key values, while optimizing storage models based on data features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored sequentially in each writing cycle, then the storage process is simple, but storage efficiency is low and storage resources are wasted
Solution Approach 1:
The patent segments data storage by identifying original data and change data based on storage key values. This segmentation allows the system to store only necessary data portions, improving storage efficiency while maintaining a manageable process through structured data classification
Solution Approach 2:
The patent performs preliminary actions by acquiring storage key values and identifying original/change data before actual storage. This pre-processing enables more efficient storage operations by preparing data in advance, reducing the complexity of the storage process itself
2Manufacturing precision
If parameter consistency is not checked, then storage operations are faster, but storage accuracy and data integrity deteriorate
Solution Approach 1:
The patent performs parameter consistency checks and identifies missing parameters as preliminary actions before data storage. This ensures storage accuracy by verifying data integrity upfront, while the automated nature of these checks minimizes the time penalty
Solution Approach 2:
The system performs self-service by automatically detecting and handling missing parameters through the identified workflow. This automation maintains high storage accuracy without requiring manual intervention, balancing precision with efficiency
3Reliability
If missing parameters are not added, then storage operations are simpler, but data completeness and reliability are compromised
Solution Approach 1:
The system implements self-service by automatically identifying and adding missing parameters to the storage model. This automated approach ensures data completeness and reliability while keeping the process manageable through systematic parameter verification and supplementation
Data Source
AI summary
A method and apparatus for storing data are provided. The method comprises: obtaining a dataset to be stored and a storage model, wherein said dataset comprises a source storage parameter, and the storage model comprises a target storage parameter; in response to determining that the source storage parameter is consistent with the target storage parameter, obtaining a storage key value of said dataset; obtaining raw data that is written into said dataset for the first time and corresponds to the source storage parameter and change data that is in said dataset and corresponds to the source storage parameter; and storing the raw data and the change data in the storage model according to the storage key value.