Using machine learning to select compression algorithms for compressing binary datasets
US12111791B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 7, 2020 |
| Grant date | Oct 8, 2024 |
| Priority date | — |
| Expiry date | Jul 11, 2043 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH03M7/6064
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A data model is trained to predict compressibility of binary data structures based on component entropy and predict relative compression efficiency for various compression algorithms based on component size. A recommendation engine in a storage system uses the data model to predict compressibility of binary data and determines whether to compress the binary data based on predicted compressibility. If the recommendation engine determines that compression of the binary data is justified, then a compression algorithm is recommended based on predicted relative compression efficiency. For example, the compression algorithm predicted to yield the greatest compression ratio or shortest compression/decompression time may be recommended.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.