Error-Bound Floating-Point Compression for Variable IoT Data
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Solution Overview
Problem
Existing lossy floating point compression techniques fail to provide satisfactory compression for IoT data with significant variances in consecutive values, leading to inefficient resource usage in data storage.
Innovation Solution
A method that rounds floating point values to a specified number of decimal places within an error bound, followed by further integer compression, to achieve improved error-bound compression by reducing the difference in the sum total of rounded values compared to the original values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If known lossy floating point compression is applied to IoT data with significant variances, then processing speed is improved, but compression ratio deteriorates
Solution Approach 1:
The patent applies preliminary rounding of floating-point values to a specified number of decimal places before compression. This preprocessing step groups values that are within the error bound, creating runs of identical rounded values that can be efficiently compressed by subsequent compression algorithms, thereby achieving both fast processing and high compression ratios.
Solution Approach 2:
The patent changes the parameter representation by rounding floating-point values to a controlled number of decimal places. This parameter transformation maintains values within the error bound while creating patterns amenable to compression, resolving the contradiction between processing speed and compression ratio.
2Ease of manufacture
If known lossy floating point compression is applied to IoT data with significant variances, then processing simplicity is improved, but storage efficiency deteriorates
Solution Approach 1:
The patent introduces a preliminary rounding step that simplifies the data structure by creating runs of identical values. This maintains processing simplicity while dramatically improving storage efficiency, as compressed representations of rounded values require far less space than uncompressed original values with significant variances.
3Loss of substance
If floating point values are rounded to fewer decimal places, then compression ratio is improved, but measurement precision deteriorates
Solution Approach 1:
The patent optimally determines the number of decimal places to round to, balancing compression ratio and measurement precision. By controlling the rounding precision parameter, the system achieves satisfactory compression while maintaining values within the acceptable error bound, thus resolving the contradiction between compression ratio and measurement precision.
Data Source
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AI summary
A system includes reception of a plurality of original floating point values, rounding of each of the plurality of original floating point values to a respective rounded floating point value, where each rounded floating point value is within an error bound of its respective original floating point value, compression of the plurality of rounded floating point values, and storage of the plurality of compressed rounded floating point values in the data storage system.