Temporal Data Convolution for Storage Efficiency
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Solution Overview
Problem
Conventional data storage techniques face inefficiencies in managing disk space due to the time and cost associated with convolution and deconvolution of data chunks across geographically distributed locations, particularly when dealing with stale data that requires storage and backup.
Innovation Solution
Implementing temporal analysis to determine permissions for convolving or deconvolving data chunks based on their lifetime, allowing for reduced data transfer by convolving chunks with similar temporal features, thereby optimizing disk space management and minimizing data transfer costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data chunks are convolved and deconvolved across geographically distributed locations to enable backup and redundancy, then data reliability is improved, but data transfer time and cost increase
Solution Approach 1:
The patent applies parameter changes by introducing temporal metadata (lifetime, creation time, modification time) to data chunks and using this temporal information to determine convolution/deconvolution operations. By changing the parameter space to include time-based attributes, the system can intelligently select which data to transfer and when, reducing unnecessary data transfer while maintaining reliability through temporal-aware redundancy management.
Solution Approach 2:
The system performs preliminary analysis of temporal metadata before executing convolution or deconvolution operations. By pre-evaluating the lifetime and creation times of data chunks, the system determines in advance which operations are necessary, avoiding unnecessary data transfers and optimizing the timing of redundancy operations to minimize time loss.
2Quantity of substance
If data chunks are convolved to reduce disk space usage, then storage efficiency is improved, but data transfer cost increases
Solution Approach 1:
The patent introduces temporal parameters (lifetime, creation time) to the data chunk metadata, enabling the system to make informed decisions about convolution based on time-based criteria. This allows the system to convolve only when and where it is beneficial, reducing unnecessary data transfers and optimizing the balance between storage efficiency and transfer cost.
Solution Approach 2:
The system uses temporal metadata as feedback to continuously optimize convolution decisions. By monitoring the lifetime and creation times of data chunks, the system receives feedback about which data is most suitable for convolution, allowing dynamic adjustment of storage operations to minimize transfer costs while maintaining space efficiency.
3Reliability
If stale data is stored and managed in chunks, then data reliability is maintained, but disk space management complexity increases
Solution Approach 1:
The patent simplifies disk space management by introducing temporal parameters (lifetime, creation time, modification time) as key characteristics for identifying and managing stale data. This parameter-based approach provides a clear, time-driven framework for determining when data becomes stale and should be reclaimed, reducing the complexity of managing stale data compared to more complex algorithms.
Solution Approach 2:
The system segments data management by creating distinct temporal zones for different data chunks based on their creation and modification times. This segmentation allows the system to handle stale data in manageable groups, applying reclamation policies to specific temporal segments rather than managing all data uniformly, thereby reducing overall management complexity.
Data Source
AI summary
Time sensitive data convolution and de-convolution is disclosed. Data stored in chunks of memory can be convolved, based on temporal aspects of the data stored on the chunks, to conserve used memory. Convolved chunks can be de-convolved according to several schema, wherein the schema are selected based temporal aspects of the original data blocks being determined to have satisfied one or more rule(s). In an aspect, the schema can reduce an amount of data transfer between data storage devices of different zones in regard to convolving or de-convolving data blocks. In an embodiment the convolution can comprise an ‘exclusive or’ operation of a first chunk and second chunk to form a third chunk, wherein the several chunks are stored in different zones.