Information Reduction Using ECC and Hashing Tradeoffs
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Solution Overview
Problem
Current data reduction methods face challenges in finding a balance between processing requirements, data reduction amount, and the usefulness of the reduced data, particularly in machine learning and big data environments, where efficient information representation is necessary for effective operations.
Innovation Solution
The implementation of information reduction services that convert data representations from higher-dimensional spaces to lower-dimensional spaces using error correcting codes, hashing, and compression techniques, allowing for flexible tradeoffs in processing and data reduction while maintaining operational effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is reduced from higher-dimensional spaces to lower-dimensional spaces, then data processing efficiency is improved, but information loss occurs
Solution Approach 1:
The patent transforms data from higher-dimensional spaces to lower-dimensional spaces by changing the dimensional parameters of data representation. This dimensional reduction enables more efficient processing while preserving essential information through careful selection of reduction techniques that maintain data utility for machine learning operations.
2Reliability
If error correcting codes are used for data reduction, then data usefulness is preserved, but processing complexity increases
Solution Approach 1:
The patent introduces error correcting codes as an intermediary mechanism between the original data and the reduced representation. These codes act as a mediator that preserves data usefulness and enables reliable reconstruction or verification of reduced data, maintaining reliability while managing processing complexity through established coding techniques.
3Speed
If hashing techniques are applied for information reduction, then processing speed is improved, but precision of data representation decreases
Solution Approach 1:
The patent employs hashing techniques to create condensed representations of data that enable rapid processing and comparison. The hash functions generate simplified data copies that preserve key characteristics for identification and matching operations, achieving high processing speed while maintaining sufficient precision for the intended applications through careful hash function selection.
Data Source
AI summary
Information reduction in data processing environments includes at least one of: one or more Error Correcting Codes that decode n-vectors into k-vectors and utilize said decoding to information-reduce data from a higher dimensional space into a lower dimensional space. The information reduction further provides for a hierarchy of information reduction allowing a variety of information reductions. Transformations are provided to utilize available data space, and data may be transformed using several techniques including windowing functions, filters in the time and frequency domains, or any numeric processing on the data.