Database Retrieval With Predicted Access and Checkpoint Caching
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Solution Overview
Problem
Delays occur in servicing data requests due to the storage of data as reduced-size representations, which require regeneration of inferences, increasing the likelihood of violating timeliness requirements.
Innovation Solution
Storing checkpoints associated with time-series relationships in a database and reconstructed forms of data in a cache to facilitate timely retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored as reduced-size representations to save storage space, then storage efficiency is improved, but data retrieval time increases due to regeneration requirements
Solution Approach 1:
The system performs preliminary actions by storing checkpoints in advance within the database and pre-computing/reconstructing data forms before they are actually requested. When a data request arrives, the system can quickly retrieve pre-prepared checkpoints and reconstructed forms from the cache, avoiding time-consuming regeneration processes and thus reducing data retrieval time while maintaining storage efficiency.
2Speed
If checkpoints and reconstructed data are stored in cache to reduce retrieval delays, then data retrieval speed is improved, but storage resource consumption increases
Solution Approach 1:
The system applies local quality by differentiating storage strategies for different data components. Checkpoints and frequently accessed reconstructed forms are stored in the cache with higher storage quality (faster access), while less frequently accessed data remains in the database with standard storage. This selective approach optimizes retrieval speed for critical data while minimizing overall storage resource consumption.
3Loss of time
If full-size data is stored instead of reduced-size representations, then data retrieval time is reduced, but storage space requirements increase
Solution Approach 1:
The system segments data into different representations based on access patterns and requirements. Full-size data is stored for checkpoints and frequently accessed information, while reduced-size representations are used for less frequently accessed data. This segmentation allows the system to optimize storage space while ensuring that critical data can be retrieved quickly without requiring all data to be stored in full-size format.
Data Source
AI summary
Methods and systems for managing data are disclosed. Data may be stored in a database based on a likelihood that the data is to be read from the database. Data may be stored as a reduced-size representation of the data if the data is unlikely to be read from the database. Some requests for data may be associated with timeliness requirements. If data associated with a timeliness requirement is stored as a reduced-size representation, a likelihood of violating the timeliness requirement while reconstructing the data may be increased. Therefore, data that is likely to be requested may be stored in a reconstructed full-size form in a cache of the database. By doing so, the reconstructed full-size forms of the data may be retrieved from the cache and used for providing computer-implemented services in compliance with timeliness requirements for the data.