The present disclosure involves a hardware-supported 3D-stacked NVM data compression method and system, involving setting a first identifier to mark a compression state of written-back data, the method at least comprising steps of: dividing the written-back data into a plurality of sub-blocks and acquiring a plurality of first output results through OR operations among the sub-blocks, respectively, or acquiring a plurality of second output results through exclusive OR operations among the sub-blocks, and determining a compression strategy for the written-back data based on the first output results or the second output results; and setting a second identifier to mark a storing means of the written-back data so that the second identifier is in pair with the first identifier, and configuring a storage strategy for the written-back data that includes at least rotating the second identifier.

The present disclosure describes methods and systems that provide for content based dynamic data compression. During a training stage a plurality of training sets are compressed using multiple data compression techniques, and each training data set is associated with a most effective compression technique. Statistical features can be extracted from each of the training data sets with consideration of the most effective compression techniques and are saved as a compression parameters file. During a working stage, individual data blocks within a telemetry data set are compressed using a data compression technique selected for the data block in light of the statistical feature presented in the compression parameters file, and the compressed data blocks are written to a compressed data set, along with encoding tags that identify the compression technique used on each data block. During a consumption stage, the compressed data blocks are decompressed using techniques identified by the encoding tags.

Data may be efficiently analyzed and compressed as part of a data compression service. A data compression request may be received from a client indicating data to be compressed. An analysis of the data or metadata associated with the data may be performed. In at least some embodiments, this analysis may be a rules-based analysis. Some embodiments may employ one or more machine learning techniques to historical compression data to update the rules-based analysis. One or more compression techniques may be selected out of a plurality of compression techniques to be applied to the data. Data compression candidates may then be generated according to the selected compression techniques. In some embodiments, a compression service restriction may be enforced. One of the data compression candidates may be selected and sent in a response.

A method, computer program product, and computing system for receiving a candidate data portion; calculating a distance-preserving hash for the candidate data portion; and performing an entropy analysis on the distance-preserving hash to generate a hash entropy for the candidate data portion.

The present disclosure describes methods and systems that provide for content based dynamic data compression. During a training stage a plurality of training sets are compressed using multiple data compression techniques, and each training data set is associated with a most effective compression technique. Statistical features can be extracted from each of the training data sets with consideration of the most effective compression techniques and are saved as a compression parameters file. During a working stage, individual data blocks within a telemetry data set are compressed using a data compression technique selected for the data block in light of the statistical feature presented in the compression parameters file, and the compressed data blocks are written to a compressed data set, along with encoding tags that identify the compression technique used on each data block. During a consumption stage, the compressed data blocks are decompressed using techniques identified by the encoding tags.

An operating method of a memory system may include: searching for, in a memory, target map data corresponding to the read request; loading the target map data from a memory device when the target map data are not searched; compressing the loaded target map data using a predetermined compression ratio depending on an available capacity of the memory; caching the compressed target map data in the memory; parsing the compressed target map data; reading target user data corresponding to the read request from the memory device based on the parsed target map data; and outputting the read target user data.

A technique for selecting context models (CMs) for a CM ensemble (CME) in a context mixing compressor includes measuring compression ratios (CRs) of the compressor on a dataset for each CM included in a base set of CMs. A first CM that has a maximum CR for the dataset is added to the CME. In response to a desired number of the CMs not being in the CME, subsequent CRs for the compressor are measured on the dataset for each of the CMs in the base set of CMs that are not in the CME in conjunction with one or more CMs in the CME. In response to a desired number of the CMs not being in the CME, subsequent CMs that in conjunction with the one or more CMs in the CME result in a maximum subsequent CR for the dataset are added to the CME.

One aspect of the disclosure provides a device, comprising: an allocation module, for determining one or more metrics of each of a plurality of data streams; a compression module, for compressing each of the plurality of data streams and generating a plurality of compressed data streams, the compression module applying a compression ratio that varies as a function of the metrics determined by the allocation module; and a buffer memory, for storing the plurality of compressed data streams.

The present disclosure involves a hard-ware-supported 3D-stacked NVM data compression method and system, involving setting a first identifier to mark a compression state of written-back data, the method at least comprising steps of: dividing the written-back data into a plurality of sub-blocks and acquiring a plurality of first output results through OR operations among the sub-blocks, respectively, or acquiring a plurality of second output results through exclusive OR operations among the sub-blocks, and determining a compression strategy for the written-back data based on the first output results or the second output results; and setting a second identifier to mark a storing means of the written-back data so that the second identifier is in pair with the first identifier, and configuring a storage strategy for the written-back data that includes at least rotating the second identifier.

The present disclosure describes methods and systems that provide for content based dynamic data compression. During a training stage a plurality of training sets are compressed using multiple data compression techniques, and each training data set is associated with a most effective compression technique. Statistical features can be extracted from each of the training data sets with consideration of the most effective compression techniques and are saved as a compression parameters file. During a working stage, individual data blocks within a telemetry data set are compressed using a data compression technique selected for the data block in light of the statistical feature presented in the compression parameters file, and the compressed data blocks are written to a compressed data set, along with encoding tags that identify the compression technique used on each data block. During a consumption stage, the compressed data blocks are decompressed using techniques identified by the encoding tags.