A computer device provides an on-demand technique for compressing the rows of a dataset separately from all other rows of data in the dataset. Users are presented with a list of predetermined compression techniques, and select one of the techniques. The computer then executes the selected compression technique to compress the dataset on a row-by-row basis. As each row of data is being compressed, the dataset remains on-line such that users still have access to the other rows of data in the dataset. Decompression of the rows of data in the dataset are also implemented on a row-by-row basis.

The system includes a correlation extraction means for extracting at least one candidate for a correlation from a collected given data set, based on a relationship between units of data in the given data set; a correlation verification means for verifying whether or not the units of data in the given data set satisfy the correlation extracted by the correlation extraction means; and a data compression means for compressing the given data set with use of the correlation, based on the result of verification by the correlation verification means.

A method of data compression includes obtaining binary sensor data having rows with multi-bit data samples. The rows are divided into data groups each including two or more samples. A precedent value is selected for the rows or respective precedent values are selected for each data group. A compressed row of compressed sensor data is generated from each row by calculating differences between the data sample and the precedent value for its associated data groups. A Compression Information Packet (CIP) is generated for each row including information for returning the binary sensor data that includes a compressed predicate indicating whether each data group is stored compressed, a data group size being a multi-bit value that stores a group size used for row compression, and a compressed word size that stores a dynamic range of the row compression. The compressed rows are stored as stored compressed data along with the CIPs.

A matrix compression/decompression accelerator (MCA) system/method that coordinates lossless data compression (LDC) and lossless data decompression (LDD) transfers between an external data memory (EDM) and a local data memory (LDM) is disclosed. The system implements LDC using a 2D-to-1D transformation of 2D uncompressed data blocks (2DU) within LDM to generate 1D uncompressed data blocks (1DU). The 1DU is then compressed to generate a 1D compressed superblock (CSB) in LDM. This LDM CSB may then be written to EDM with a reduced number of EDM bus cycles. The system implements LDD using decompression of CSB data retrieved from EDM to generate a 1D decompressed data block (1DD) in LDM. A 1D-to-2D transformation is then applied to the LDM 1DD to generate a 2D decompressed data block (2DD) in LDM. This 2DD may then be operated on by a matrix compute engine (MCE) using a variety of function operators.

A first storage system compresses data relating to read and write by a primary site and stores the data in a first physical volume. A second storage system compresses data relating to read and write by a secondary site and stores the data in a second physical volume. When performing replication for transferring the data stored in the first physical volume of the first storage system to the second storage system and storing the data in the second physical volume, the first storage system and the second storage system determine, based on a compression scheme executable by the first storage system and a compression scheme executable by the second storage system, a compression scheme to be applied to transfer target data and transfer the transfer target data compressed by the determined compression scheme.

Systems, apparatuses and methods provide technology that compresses first data based on a first compression scheme to generate second data, where the first data is associated with a first machine learning model. The technology stores the second data into a memory, adjusts a first entry of a lookup table to correspond to the first compression scheme based on the first data being compressed based on the first compression scheme, provide the second data from the memory to processing elements of a processing array during execution of the first machine learning model, and decompresses, at the processing array, the second data based on the lookup table to obtain the first data.

A deployment of radiologic beacons detect radiation levels for emitted radiation around a particular geographic area such as a town, city or campus environment. In a deployment of beacons for detecting and gathering radiological gamma-ray spectral data, each beacon periodically generates a set of values indicative of radiation at a particular energy level, and assembles a vector of the set of values ordered according to increasing energy levels. Each of the beacons transmits the vector as a stream or periodic sequence of data to a common aggregation location. Each beacon encodes the data according to a compression mechanism based on a Poisson distribution of the spectral data. A running average of the values for each energy level is maintained for the sequence of vectors, and encoding/decoding mechanisms are selected based on the average value to be encoded.

To facilitate storage of data from plural data sources of an industrial automation control system, power distribution system or power generation system, a decision making device executes a machine learning algorithm to determine a compression technique in dependence on the data source from which data originates.

A device and a method for compressing data by a device are provided, which relate to the storage field and are used to resolve a prior-art problem that a compression ratio at which data in a data block is compressed by a device is relatively low. The method includes: parsing, by a device, an information block in a data block, to obtain a file type of data in the data block and a data sub-block that is included in the data block; determining a characteristic of data in the data sub-block according to the file type; selecting, according to the characteristic, a target compression algorithm that is used to compress the data in the data sub-block; and compressing the data in the data sub-block by using the target compression algorithm. Embodiments of the present disclosure are used to compress data.

Techniques and computing devices for compression memory coloring are described. In one embodiment, for example, an apparatus may include at least one memory, at least on processor, and logic for compression memory coloring, at least a portion of the logic comprised in hardware coupled to the at least one memory and the at least one processor, the logic to determine whether data to be written to memory is compressible, generate a compressed data element responsive to determining data is compressible, the data element comprising a compression indicator, a color, and compressed data, and write the compressed data element to memory. Other embodiments are described and claimed.