A control apparatus includes a reception unit which receives distribution data which contains compressed update data and a header which includes information to designate any one of a plurality of update systems, a decompression unit which decompresses the update data from the distribution data received by the reception unit, and a restoration unit which restores a new program after updating according to an update system designated in the header using the update data decompressed by the decompression unit. The decompression unit switches a decompression method when the update data is decompressed on the basis of the update system designated in the header.

A method, a system, and a computer program product for decompressing data. One or more compressed blocks in a set of stored compressed blocks responsive to a request to access data in the set of stored compressed blocks are identified. String prefixes inside the identified compressed blocks are decompressed using front coding. String suffixes inside the identified compressed blocks are decompressed using a re-pair decompression. Uncompressed data is generated.

A method, a system, and a computer program product for decompressing data. One or more compressed blocks in a set of stored compressed blocks responsive to a request to access data in the set of stored compressed blocks are identified. String prefixes inside the identified compressed blocks are decompressed using front coding. String suffixes inside the identified compressed blocks are decompressed using a re-pair decompression. Uncompressed data is generated.

Methods and devices for encoding a sparse signal x to generate a compressed encoded signal. The methods employ directionless grammar-based dimension coding. Using labelled subsets and the finding of disjoint repeated subsets in order to build a directionless grammar, the non-zero positions of the sparse signal are encoded in a directionless grammar-based dimension encoder. Element values are encoded in a conditional non-zero encoder. The coding process facilitates random access.

A log processing device and a log processing method thereof are provided. The log processing device divides the original log data into a plurality of block data, transforms a numeric variable of each of the block data into a representative code, and determines whether to perform a combination process for continuous block data to generate a plurality of combinational block data according to a data integrity of each of the block data. The log processing data takes the combinational block data as a log template, and each of the combinational block data corresponds to an event.

A log processing device and a log processing method thereof are provided. The log processing device divides the original log data into a plurality of block data, transforms a numeric variable of each of the block data into a representative code, and determines whether to perform a combination process for continuous block data to generate a plurality of combinational block data according to a data integrity of each of the block data. The log processing data takes the combinational block data as a log template, and each of the combinational block data corresponds to an event.

A big data processing method based on direct computation of compressed data. The method includes 1) compressing, based on a modified Sequitur compression method, original input data according to a smallest compression granularity given by an user, and transforming them into a directed acyclic graph, DAG, consisting of digits; and 2) determining an optimal traversal pattern, and performing a top-downward traversal or a bottom-upward traversal on the DAG in the step 1) based on the determined optimal traversal pattern so as to enable direct processing of the compressed data. By providing a modified Sequitur algorithm and top-downward and bottom-upward traversal strategies in the disclosure, direct processing of compressed data is enabled, significant improvement in time and space has been gained with broad applicability, and certain representations with respect to more advanced document analytics can still be derived on the basis of these.

A big data processing method based on direct computation of compressed data. The method includes 1) compressing, based on a modified Sequitur compression method, original input data according to a smallest compression granularity given by an user, and transforming them into a directed acyclic graph, DAG, consisting of digits; and 2) determining an optimal traversal pattern, and performing a top-downward traversal or a bottom-upward traversal on the DAG in the step 1) based on the determined optimal traversal pattern so as to enable direct processing of the compressed data. By providing a modified Sequitur algorithm and top-downward and bottom-upward traversal strategies in the disclosure, direct processing of compressed data is enabled, significant improvement in time and space has been gained with broad applicability, and certain representations with respect to more advanced document analytics can still be derived on the basis of these.

An information handling system may include at least one processor and a memory coupled to the at least one processor. The information handling system may be configured to receive data comprising a plurality of data chunks; perform deduplication on the plurality of data chunks to produce a plurality of unique data chunks; determine a compression ratio for respective pairs of the unique data chunks; determine a desired compression order for the plurality of unique data chunks based on the compression ratios; combine the plurality of unique data chunks in the desired compression order; and perform data compression on the combined plurality of unique data chunks.

A method, a system, and a computer program product for executing a database compression. A compressed string dictionary having a block size and a front coding bucket size is generated from a dataset. Front coding is applied to one or more buckets of strings in the dictionary having the front coding bucket size to generate one or more front coded buckets of strings. One or more portions of the generated front coded buckets of strings are concatenated to form one or more blocks having the block size. Each block is compressed. A set of compressed blocks is stored. The set of the compressed blocks stores all strings in the dataset.