  The persistent storage of big data requires advanced error correction
schemes. The classical approach is to use error correcting codes (ECCs). This
work studies an alternative approach, which uses the redundancy inherent in
data itself for error correction. This type of redundancy, called Natural
Redundancy (NR), is abundant in many types of uncompressed or even compressed
files. The complex structures of Natural Redundancy, however, require machine
learning techniques. In this paper, we study two fundamental approaches to use
Natural Redundancy for error correction. The first approach, called
Representation-Oblivious, requires no prior knowledge on how data are
represented or compressed in files. It uses deep learning to detect file types
accurately, and then mine Natural Redundancy for soft decoding. The second
approach, called Representation-Aware, assumes that such knowledge is known and
uses it for error correction. Furthermore, both approaches combine the decoding
based on NR and ECCs. Both experimental results and analysis show that such an
integrated scheme can substantially improve the error correction performance.
