Example embodiments relate to using a multi-context entropy coder for encoding adjacency lists. A system may obtain a graph having data (or multiple graphs) and may compress the data of the graph using a multi -context entropy coder. The multi-context entropy coder may encode adjacency lists within the data such that each integer is assigned to a different probability distribution. For example, operating the multi-context entropy coder may involve using a combination of arithmetic coding, Huffman coding, and ANS. The assignment of integers to the probability distributions may depend on each integer’s role and/or previous values of a similar kind. By using multi -context entropy- coding, the computing system may increase compression ratio while maintaining similar processing speed.

Entropy coding a sequence of symbols is described. A first probability model for entropy coding is selected. At least one symbol of the sequence is coded using a probability determined using the first probability model. The probability according to the first probability model is updated with an estimation of a second probability model to entropy code a subsequent symbol. The combination may be a fixed or adaptive combination.

Methods, computer program products, and/or systems are provided that perform the following operations: in a data replication environment, analyzing a database workload to generate a knowledge base of information related to compression; dividing a transfer data stream into different segments based, at least in part, on the knowledge base; obtaining candidate compression types for the transfer data stream based, at least in part, on the knowledge base; assigning respective compression types of the candidate compression types to the different segments; generating compressed segments based, at least in part, on the respective compression types assigned to the different segments; and providing the compressed segments to a replication target.

A system and method for highly efficient encoding of data that includes extended functionality for asymmetric encoding/decoding and network policy enforcement. In the case of asymmetric encoding/decoding the original data is encoded by an encoder according to a codebook and sent to a decoder, but the output of the decoder depends on data manipulation rules applied at the decoding stage to transform the decoded data into a different data set from the original data. In the case of network policy enforcement, a behavior appendix into the codebook, such that the encoder and/or decoder at each node of the network comply with network behavioral rules, limits, and policies during encoding and decoding.

A system and method for highly efficient encoding of data that includes extended functionality for asymmetric encoding/decoding and network policy enforcement. In the case of asymmetric encoding/decoding the original data is encoded by an encoder according to a codebook and sent to a decoder, but the output of the decoder depends on data manipulation rules applied at the decoding stage to transform the decoded data into a different data set from the original data. In the case of network policy enforcement, a behavior appendix into the codebook, such that the encoder and/or decoder at each node of the network comply with network behavioral rules, limits, and policies during encoding and decoding.

A system and method for encoding data using a plurality of encoding libraries. Portions of the data are encoded by different encoding libraries, depending on which library provides the greatest compaction for a given portion of the data. This methodology not only provides substantial improvements in data compaction over use of a single data compaction algorithm with the highest average compaction, but provides substantial additional security in that multiple decoding libraries must be used to decode the data. In some embodiments, each portion of data may further be encoded using different sourceblock sizes, providing further security enhancements as decoding requires multiple decoding libraries and knowledge of the sourceblock size used for each portion of the data. In some embodiments, encoding libraries may be randomly or pseudo-randomly rotated to provide additional security.

Provided are a method and device for creating a gene mutation dictionary, and a method and device for compressing genomic data using the gene mutation dictionary. The method for creating a gene mutation dictionary includes: obtaining genome sequence data of a plurality of individuals of a species and reference genome data of the species; aligning genome sequence data of each individual to the reference genome data to obtain a mutation result of the genome sequence data of each individual relative to the reference genome data; partitioning a genome of the species into a plurality of unit regions of biological significance; and generating a plurality of mutant patterns of the individuals in each unit region by statistically analyzing mutant status for each unit region based on the mutation result, and numbering the mutant patterns, to obtain the gene mutation dictionary.

The generation of symbol-encoded data from digital data, as part of the compression of the digital data into a compressed digital data, can be performed with reference to multiple alternative alphabets. A selection of a specific alphabet is made based on the digital data being compressed, the compression parameters, or combinations thereof. Information indicative of the selected alphabet is encoded into one or more headers of the resulting compressed digital data. A single alphabet can be selected for all of a set of digital data being compressed, or multiple different alphabets can be selected, with different ones of the multiple different alphabets being utilized to compress different portions of the digital data. Additionally, rather than explicitly specifying a specific selected alphabet, the header information can comprise information from which a same alphabet can be independently selected heuristically by both the compressor and the corresponding decompressor.

The generation of symbol-encoded data from digital data, as part of the compression of the digital data into a compressed digital data, can be performed with reference to multiple alternative alphabets. A selection of a specific alphabet is made based on the digital data being compressed, the compression parameters, or combinations thereof. Information indicative of the selected alphabet is encoded into one or more headers of the resulting compressed digital data. A single alphabet can be selected for all of a set of digital data being compressed, or multiple different alphabets can be selected, with different ones of the multiple different alphabets being utilized to compress different portions of the digital data. Additionally, rather than explicitly specifying a specific selected alphabet, the header information can comprise information from which a same alphabet can be independently selected heuristically by both the compressor and the corresponding decompressor.

Interleaving of variable bitrate streams for GPU implementations is described. An example of an apparatus includes one or more processors including a graphic processor, the graphics processor including a super-compression encoder pipeline to provide variable width interleaved coding; and memory for storage of data, wherein the graphics processor is to perform parallel dictionary encoding on a bitstream of symbols one of multiple workgroups, the workgroup to employ a plurality of encoders to generate a plurality of token-streams of variable lengths; create a histogram including at least tokens from the plurality of token-streams for the workgroup to generate an optimized entropy code; entropy code each of the plurality of token-streams for the workgroup into an encoded bitstream; and variably interleave the encoded bitstreams to generate an interleaved bitstream and bookkeep a size of the interleaved bitstream.