Error Correction by Natural Redundancy for Long Term Storage




Abstract: NVMs are increasingly important for big-data storage. However, their long-term reliability has significant challenges. This work studies how to use the natural redundancy (NR) in data for error correction. The NR can be combined with error-correcting codes (ECCs) to effectively improve data reliability. This work studies several aspects of NR: effective discovery of NR in compressed data, error-correction capability of ECCs with NR, and efficient decoding of random codes that model data with NR.

Bio: Anxiao (Andrew) Jiang is an Associate Professor at Texas A&M University. His research interests include information theory, data storage, networks and algorithm design. He is a recipient of the NSF CAREER Award in 2008 for his research on information theory for flash memories and a recipient of the 2009 IEEE Communications Society Data Storage Technical Committee (DSTC) Best Paper Award in Signal Processing and Coding for Data Storage.