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Local Decode and Update for Big Data Compression

Abstract : This paper investigates data compression that simultaneously allows local decoding and local update. The main result is a universal compression scheme for memoryless sources with the following features. The rate can be made arbitrarily close to the entropy of the underlying source, contiguous fragments of the source can be recovered or updated by probing or modifying a number of codeword bits that is on average linear in the size of the fragment, and the overall encoding and decoding complexity is quasilinear in the blocklength of the source. In particular, the local decoding or update of a single message symbol can be performed by probing or modifying a constant number of codeword bits. This latter part improves over previous best known results for which local decodability or update efficiency grows logarithmically with blocklength.
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Contributor : Aslan Tchamkerten Connect in order to contact the contributor
Submitted on : Tuesday, October 1, 2019 - 5:42:16 PM
Last modification on : Thursday, December 10, 2020 - 4:47:16 PM


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Shashank Vatedka, Aslan Tchamkerten. Local Decode and Update for Big Data Compression. IEEE Transactions on Information Theory, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TIT.2020.2999909⟩. ⟨hal-02302639⟩



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