A method includes generating a plurality of parity blocks from a plurality of lines of data blocks. The plurality of lines of data blocks are stored in data sections of memory of a cluster of computing devices of the computing system by distributing storage of individual data blocks of the plurality of lines of data blocks among unique data sections of the cluster of computing devices. The plurality of parity blocks are stored in parity sections of memory of the cluster of computing devices by distributing storage of parity blocks of the plurality of parity blocks among unique parity sections of the cluster of computing devices.

A computing device includes a computing device controller hub and a plurality of parallelized nodes coupled to the computing device controller hub. Each node of the plurality of parallelized nodes includes a central processing module, a main memory, and at least one disk memory. The plurality of computing devices is operable to collectively execute query requests against at least one database table stored by the plurality of computing devices based on each node of each computing device performing corresponding operations independently from other nodes of the plurality of parallelized nodes.

A data processing system and method are provided. The data processing system includes: a data acquisition unit, configured to acquire a plurality pieces of data related to a target object; and a data processing unit, configured to receive the plurality pieces of data and set a plurality of adjacent regions in a two-dimensional spatial representation of the plurality pieces of data according to a tolerable compression error. The plurality of regions include an adjacent first region and second region, respectively covering a plurality pieces of data. The data processing unit is configured to forwardly expand the second region to obtain the expanded second region overlapping the first region, calculate a compression error of data covered by the expanded second region, reset the first region and compress the data covered by the reset first region. The data processing system can reduce or minimize the data compression error.

A method includes generating, by a processing entity of a computing system, a plurality of parity blocks from a plurality of lines of data blocks. A first number of parity blocks of the plurality of parity blocks is generated from a first line of data blocks of the plurality of lines of data blocks. The method further includes storing, by the processing entity, the plurality of lines of data blocks in data sections of memory of a cluster of computing devices of the computing system in accordance with a read/write balancing pattern and a restricted file system. The method further includes storing, by the processing entity, the plurality of parity blocks in parity sections of memory of the cluster of computing devices in accordance with the read/write balancing pattern and the restricted file system.

A memory includes, in one embodiment, one or more storage elements; read/write circuitry; and compressed bit circuitry. The read/write circuitry is configured to read a set of hard bits from the one or more storage elements, and sense a set of soft bits while reading the set of hard bits from the one or more storage elements, the set of soft bits having a first fixed size, and the set of soft bits indicating a reliability of the set of hard bits. The compressed soft bit circuitry is configured to generate, with a fixed size soft bit lossy compression algorithm, a fixed size compressed soft bits by compressing the set of soft bits, the fixed size compressed soft bits having a second fixed size that is smaller than the first fixed size, and output the fixed size compressed soft bits to a memory-to-controller bus.

There is provided a system, method and device for dynamically compressing an actigraphy signal at a source device. The method comprises receiving the actigraphy signal related to a user's physical activity from an accelerometer sensor on the source device and for compressing the actigraphy signal by determining regions of interest in the actigraphy signal to capture, said compressing performed by: computing a rapid change factor value indicating a drastic change in movement activity in said actigraphy signal, said rapid change factor computed based on determining a spurious free dynamic range of a second order difference signal of the actigraphy signal and subsequently determining the step size of the actigraphy signal, the step size indicating the interval with which the actigraphy signal instantaneously changes its value from one sample to another; automatically scanning the second order difference signal to locate samples in the second order difference signal having a value greater than the rapid change factor value, said located samples defining primary segment boundaries; -extracting frames of the encoded actigraphy signal between two consecutive primary segment boundaries and discarding outlying regions of the encoded actigraphy signal; and —outputting only the extracted frames representing a compressed actigraphy signal to an external computing device for subsequent processing.

A method includes receiving a data set that includes a plurality of data records, where a data record includes a first data field containing a first fixed length data value and a second data field containing a first variable length data value. The method further includes accessing a compression dictionary for the second data field, where a first entry of the compression dictionary includes a key field storing a first fixed length index value and a value field storing the first variable length data value, and where the key field has a smaller data size than the value field. The method further includes creating a storage data set based on the compression dictionary and sending the storage data set to a storage sub-system for storage, where the first variable length data value of the second data field of the data record is replaced with the first fixed length index value.

Methods and apparatuses of neural network model compression/decompression are described. In some examples, an apparatus of neural network model decompression includes receiving circuitry and processing circuitry. The processing circuitry can be configured to receive a dependent quantization enabling flag from a bitstream of a compressed representation of a neural network. The dependent quantization enabling flag can indicate whether a dependent quantization method is applied to model parameters of the neural network. The model parameters of the neural network can be reconstructed based on the dependent quantization method in response to the dependent quantization enabling flag indicating the dependent quantization method is used for encoding the model parameters of the neural network.

A method for decoding at least one encoded image within an encoded data stream, the image being split into blocks of elements. The decoding method includes: obtaining from the stream a piece of information representative of a so-called prediction function, the prediction function belonging to a predetermined list of prediction functions; and decoding at least one element of a block of elements, using a differential pulse-code modulation, from an already processed neighbouring element and using the prediction function.

A method includes receiving, by a first computing entity of a database system, data that is organized in rows and columns. The method further includes determining, by the first computing entity, one or more key columns from the columns based on a desired sort criteria for the data. The method further includes sorting, by the first computing entity, other columns of the columns based on the one or more key columns to produce sorted other columns. The method further includes sending, by the first computing entity, the one or more key columns to a second computing entity of the database system for storage in a first storage location associated with the second computing entity. The method further includes sending, by the first computing entity, the sorted other columns to the second computing entity for storage in a second storage location associated with the second computing entity.