Applying Machine Learning to Data Recovery
In the latest video share from Data Analyzers Data Recovery we presented our latest research and development project: Applying machine learning to data recovery. In the paper we published in January 2019 – “Using Machine Learning in Data Recovery” David Edwards and myself outlined basic idea behind this project. This presentation was recorded in early August 2019 and has been recently published on Data Analyzers YouTube Channel.
Machine learning (ML) and artificial intelligence (AI) are being applied to a growing number of data storage technologies. Solid state drives (SSD), flash controllers, and the NAND flash cache on shingled magnetic recording hard disk drives are just some of the places AI/ML will impact storage. AI/ML holds promise as a tool for aiding difficult data recovery tasks, such as determining dynamic XOR scrambling patterns, sorting, carving, and other analysis. Recent results, challenges, and opportunities for AI/ML in these areas are detailed for storage devices that have logical, not physical, failures.
I’d like to thank Andrew von Ramin Mapp and Data Analyzers Team for making this project possible. I’d also like to thank Advanced Data Recovery Analytics Team for their inputs and Dejan Jankovic for his work.
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