A computing system and a compressing method for neural network parameters are provided. In the method, multiple neural network parameters are obtained. The neural network parameters are used for a neural network algorithm. Every at least two neural network parameters are grouped into an encoding combination. The number of neural network parameters in each encoding combination is the same. The encoding combinations are compressed with the same compression target bit number. Each encoding combination is compressed independently. The compression target bit number is not larger than a bit number of each encoding combination. Thereby, the storage space can be saved and excessive power consumption for accessing the parameters can be prevented.

Cognitive compression with varying structural granularities in a NoSQL database by establishing a data training set for compressing and decompressing data stored within the NoSQL database. The data training set includes received user policy goals, compression parameters, and metered feedback associated with data usage and workload characteristics. A compression parameter model is dynamically implemented in real-time for the data selected according to the established data training set to compress and decompress the data at a given structural granularity.

An apparatus includes a network interface and a processor. The network interface is configured to communicate over a communication network. The processor is configured to receive (i) data, including a medical parameter acquired as a function of time, and (ii) a selection of one or more time intervals of interest within the time period. The processor is further configured to compress a first portion of the data, which is within the selected time intervals, at a first resolution, and compress a second portion of the data, which is outside the selected time intervals, at a second resolution, which is coarser than the first resolution. The processor is additionally configured to transmit the compressed first and second portions of the data, via the network interface, over the communication network.

Methods and systems for improving coding decoding efficiency of video by providing a syntax modeler, a buffer, and a decoder. The syntax modeler may associate a first sequence of symbols with syntax elements. The buffer may store tables, each represented by a symbol in the first sequence, and each used to associate a respective symbol in a second sequence of symbols with encoded data. The decoder decodes the data into a bitstream using the second sequence retrieved from a table.

A radar data processing method is provided. The method includes dividing to-be-compressed radar data into groups, determining encoding parameters of each group according to at least one radar data in each group, and encoding each radar data in each group according to the encoding parameters of each group, to obtain encoded radar 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.

The disclosure provides a data backup system. The data backup system comprises an electronic device and a server. The electronic device is configured to store original data. The server predicts a data size of predicted compressing data and a first predicted compressing time corresponding to the predicted compressing data, which are generated by compressing the original data with a plurality of compressing algorithm respectively. The server fetches a computing resource data of the electronic device and predicts respectively a plurality of second predicted compressing time for which the electronic device compresses the original data according to the computing resource data and the plurality of first predicted compressing time. The server computes a plurality of reference data and generates a recommending command according to a default compressing algorithm of the plurality of the compressing algorithm which corresponds to the minimal reference data.

A computer system includes a hardware controller and an internal millicode storage area. The controller includes an accelerator that decompresses a data stream requested by an application. The internal millicode storage area can store a compression dictionary library including a plurality of different pre-defined compression dictionaries. A host system includes a dictionary manager that determines a compression dictionary from the plurality of different pre-defined compression dictionaries included in the dictionary library to decompress the data stream. The accelerator can access the internal millicode storage area to obtain the determined compression dictionary, and to decompress the data stream according to the determined compression dictionary.

An apparatus includes a network interface and a processor. The network interface is configured to communicate over a communication network. The processor is configured to receive (i) data, including a medical parameter acquired as a function of time, and (ii) a selection of one or more time intervals of interest within the time period. The processor is further configured to compress a first portion of the data, which is within the selected time intervals, at a first resolution, and compress a second portion of the data, which is outside the selected time intervals, at a second resolution, which is coarser than the first resolution. The processor is additionally configured to transmit the compressed first and second portions of the data, via the network interface, over the communication network.

Methods and systems for improving coding decoding efficiency of video by providing a syntax modeler, a buffer, and a decoder. The syntax modeler may associate a first sequence of symbols with syntax elements. The buffer may store tables, each represented by a symbol in the first sequence, and each used to associate a respective symbol in a second sequence of symbols with encoded data. The decoder decodes the data into a bitstream using the second sequence retrieved from a table.