Distributed Codebook Encoding for Previously-Unseen Data Compaction
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Solution Overview
Problem
Current data storage technologies are insufficient to keep pace with rapidly increasing data storage demand, as data is being produced at a much faster rate than physical storage capacity can be manufactured, and existing data compression methods either fail to efficiently handle previously-unseen data or result in data degradation.
Innovation Solution
A system and method for data compaction utilizing distributed codebook encoding, which improves entropy encoding methods by accounting for and efficiently handling previously-unseen data, enabling distributed encoding and decoding capabilities, and allowing for parametrized codebook encoding methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional entropy encoding methods are used, then data compaction is achieved, but previously-unseen data cannot be efficiently handled leading to encoding failures or inefficiencies
Solution Approach 1:
The codebook is made dynamic and adaptive rather than static. The system continuously learns from incoming data streams and updates the codebook structure to incorporate newly observed patterns and previously-unseen data, allowing the encoding method to adapt its behavior based on the specific characteristics of the data being encoded.
Solution Approach 2:
The system performs preliminary learning and analysis on training data sets before actual encoding operations. By pre-processing training data to identify patterns and build an initial codebook structure, the system prepares in advance to handle various data types including previously-unseen data more effectively during runtime.
2Quantity of substance
If data compression is applied to increase storage capacity, then storage efficiency improves, but data degradation occurs with lossy compression or limited space savings with lossless compression
Solution Approach 1:
The system changes the fundamental parameter of data representation by transitioning from raw data storage to codebook-based symbolic representation. Data is transformed into references to codebook entries, achieving high compression ratios while maintaining exact reconstruction capability through the distributed codebook encoding system, thus avoiding data degradation.
3Quantity of substance
If physical storage capacity is increased to meet demand, then storage space availability improves, but manufacturing capacity cannot keep pace with exponential data growth
Solution Approach 1:
The invention fundamentally changes the storage parameter from physical capacity to logical compression ratio. By implementing distributed codebook encoding, the system achieves extreme compression ratios that effectively multiply available storage capacity without requiring additional physical manufacturing, allowing existing storage infrastructure to handle exponentially growing data demands.
4Ease of operation
If symmetric codebooks are used for encoding and decoding, then simplicity is maintained, but flexibility and parametrized control are limited
Solution Approach 1:
The codebook system is segmented into multiple distributed codebooks rather than a single symmetric codebook. This segmentation allows different codebooks to serve different purposes, handle different data types, and be configured with various parameters, providing flexibility while maintaining operational simplicity through modular design.
Solution Approach 2:
The distributed codebook system achieves multi-functionality where a single encoding framework can handle diverse data types and scenarios by selecting and configuring appropriate codebooks from the distributed set. This universal approach maintains ease of operation while providing extensive adaptability through parameterized codebook selection and configuration.
Data Source
AI summary
A system and method for data compaction utilizing distributed codebook encoding to improve entropy encoding methods to account for, and efficiently handle, previously-unseen data in data to be compacted, allow for distributed encoding and decoding capabilities, and allow for parametrized codebook encoding methods. Training data sets are analyzed to determine the frequency of occurrence of each sourceblock in the training data sets. A mismatch probability estimate is calculated comprising an estimated frequency at which any given data sourceblock received during encoding will not have a codeword in the codebook. Further, a codebook and a behavior codebook may both be maintained or altered in a distributed fashion across multiple devices or services, for widespread, or permission-based, or parametrized codebook encoding.