A Huffman cache is used to hold Huffman dictionaries that are changeable for dynamically selecting literal frequencies that are similar, wherein the Huffman cache is a data storage cache.

A runtime bit-plane data-format optimizer for a processing element includes a sparsity-detector and a compression-converter. The sparsity-detector selects a bit-plane compression-conversion format during a runtime of the processing element using a performance model that is based on a first sparsity pattern of first bit-plane data stored in a memory exterior to the processing element and a second sparsity pattern of second bit-plane data that is to be stored in a memory within the processing element. The second sparsity pattern is based on a runtime configuration of the processing element. The first bit-plane data is stored using a first bit-plane compression format and the bit-plane second data is to be stored using a second bit-plane compression format. The compression-conversion circuit converts the first bit-plane compression format of the first data to be the second bit-plane compression format of the second data.

This application provides a data processing method and apparatus. The method is applied to a storage system. The storage system includes a storage apparatus and a processing apparatus. The method is performed by the processing apparatus. The method includes: obtaining a tiered storage feature and a data feature of first data, where the tiered storage feature includes at least one of the following features: an importance, an access frequency, and a retention time, and the data feature includes at least one of the following features: a data type, a data dimension, a data size, or a data content feature; determining a first compression algorithm based on the tiered storage feature and the data feature; and compressing the first data based on the first compression algorithm, to obtain compressed data.

A data encoder including a preprocessor configured to divide a data stream into a plurality of sub data blocks; a plurality of meta data generators each configured to generate meta data from one of the plurality of sub data blocks; and a plurality of data compressors each configured to compress one of the plurality of sub data blocks according to the meta data.

Methods, apparatuses, and storage media associated with compaction of data from one or more computing devices are disclosed. In various embodiments, one or more Internet of Things (IoT) devices may transmit information to a computing system. The computing system may group together raw data received from these one or more IoT devices based on a shared attribute. The computing system may select a compaction scheme to represent the knowledge conveyed by a group of the raw data. The computing system may apply this compaction scheme to the group of raw data to generate data that is representative of the group of raw data. Other embodiments may be disclosed or claimed.

According to one embodiment, a device includes a determination unit, compression unit, selecting unit, write updating unit, writing unit. The determination unit determines whether to compress write data based on specific information. The specific information including at least one of the type, number of accesses, access frequency and importance level of the write data. The compression unit compresses the write data when determining to compress the write data. The selecting unit selects a write region for the write data in nonvolatile memory based on the specific information. The write updating unit updates the specific information. The writing unit writes compressed write data into the write region when determining to compress the write data. The writing unit writes uncompressed write data into the write region when not determining to compress the write data.

A data storage device includes at least one data storage medium and a controller that is operably coupled to the at least one data storage medium. The controller receives the bit stream in a data storage device and performs a first level of compression on the received bit stream to obtain a symbol frame including a plurality of symbols. The controller encodes an initial portion of the plurality of symbols contained in the symbol frame by a fixed encoding scheme. The controller also collects statistics for the initial portion of the symbol frame. The controller then selects at least one data compression algorithm based on the collected statistics. The controller then performs compression encoding on a remaining portion of the symbol frame with the selected at least one data compression algorithm.

Embodiments of the present disclosure provide systems, methods, and computer storage media for optimizing computing resources generally associated with cloud-based media services. Instead of decoding digital assets on-premises to stream to a remote client device, an encoded asset can be streamed to the remote client device. A codebook employable for decoding the encoded asset can be embedded into the stream transmitted to the remote client device, so that the remote client device can extract the embedded codebook, and employ the extracted codebook to decode the encoded asset locally. In this way, not only are processing resources associated with on-premises decoding eliminated, but on-premises storage of codebooks can be significantly reduced, while expensive bandwidth is freed up by virtue of transmitting a smaller quantity of data from the cloud to the remote client device.

Methods, devices and systems enhance compression and decompression of data values when they comprise a plurality of semantically meaningful data fields. Compression is sometimes not applied to each data value as a whole, but instead to at least one of the semantically meaningful data fields of each data value, and in isolation from the other ones. Data fields can be organized that share the same semantic meaning together to accelerate compression and decompression as multiple compressors and decompressors can be used in parallel. A system can be used where methods and devices are tailored to perform compression and decompression of the semantically meaningful data fields of floating-point numbers after first partitioning further at least one of said data fields into two or a plurality of sub-fields to increase the degree of value locality and improve compressibility of floating-point values.

Apparatuses, methods and systems for selective data compression are described. Apparatuses for selective data compression comprise one or modules executable to analyze data to generate analysis results, determine and associate appeal factor values with the data or portions thereof, and compress the data with the compression configurations associated with the appeal factor values. The apparatuses may be employed to compress data on-board manned or unmanned aerial vehicles or other devices. Through use of data analysis, including but not limited to artificial intelligence analysis, image analysis, meta-data analysis, in orbit analysis, and so forth, the manned or unmanned aerial vehicles may compress data collected, generated and/or stored in the vehicle based on data content, data contextual information, data collection opportunity, a priori information, change detection, and/or a particular task the vehicle is tasked to perform (application).