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Using machine learning in data recovery

Although backup tools manage majority if not all corporate data and applications. With their complete access to the lifeblood of a company, backup providers are building ways for corporations to not just manage but understand the data at hand. Adopting advances in artificial intelligence, machine learning these solutions can now make administrators faster and more efficient at their jobs. However, least expected, data recovery solutions are also at the forefront of machine learning and AI technology invention and adoption!

First things first

Artificial Intelligence (AI) – computer systems being able to perform tasks that normally require human “intelligence”;
Machine learning – computer systems that have the ability to learn without being explicitly programmed;
Big data – data sets that are too large or complex for traditional data-processing application software to adequately deal with. – Wikipedia

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Data Analyzers LLC is investing heavily in these technologies. Our recent research and development project titled “Advanced data recovery techniques: Using machine learning in data recovery” has been published.

Machine learning and artificial intelligence algorithms are finding their way into the data storage technology. Recent developments in data storage devices suggest machine learning have tremendous potential in solid state drives (SSD) and flash controllers. On the other side most of the latest hard disk drives are hybrids, although not directly used for data storage NAND flash memory has been widely popular among hard disk drive manufacturers as a worthy cache solution. In this paper we will try to elaborate ways we can use ML and AI technology in order to recover data once a data storage device failed for reasons other than hardware. Machine learning and AI based technologies appears to be extremely useful for certain operations that data recovery engineers use on a daily basis such as: sorting, carving or analysis.

Machine learning and AI based technologies appears to be extremely useful for certain operations that data recovery engineers use on a daily basis such as: sorting, carving or analysis. Building on the existing Data Analyzers data recovery capabilities, ML is being developed to automatically perform complex tasks improving capabilities of our lab.

To learn more about this paper click here.

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