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 storage system that performs irreversible compression on time-series data using a compressor/decompressor based on machine learning calculates a statistical amount value of each of one or more kinds of statistical amounts based on one or more parameters in relation to original data (time-series data input to a compressor/decompressor) and calculates a statistical amount value of each of the one or more kinds of statistical amounts based on the one or more kinds of parameters in relation to decompressed data (time-series data output from the compressor/decompressor) corresponding to the original data. The machine learning of the compressor/decompressor is performed based on the statistical amount value calculated for each of the one or more kinds of statistical amounts in relation to the original data and the statistical amount value calculated for each of the one or more kinds of statistical amounts in relation to the decompressed data.

A method for transmitting data collected by at least one sensor to a monitoring device. The method includes, upon acquisition of a new piece of data by the at least one sensor, acts of calculating a deviation indicator indicating a deviation between the value of the new piece of data and a value predicted for this piece of data by a prediction model representative of previously acquired data, and transmitting the new piece of data to the monitoring device when the deviation indicator is higher than a threshold. Also provided are a monitoring method on a monitoring device, a terminal implementing the transmission method and a server implementing the monitoring method.

Computer-implemented methods, systems, and devices to perform lossless compression of floating point format time-series data are disclosed. A first data value may be obtained in floating point format representative of an initial time-series parameter. For example, an output checkpoint of a computer simulation of a real-world event such as weather prediction or nuclear reaction simulation. A first predicted value may be determined representing the parameter at a first checkpoint time. A second data value may be obtained from the simulation. A prediction error may be calculated. Another predicted value may be generated for a next point in time and may be adjusted by the previously determined prediction error (e.g., to increase accuracy of the subsequent prediction). When a third data value is obtained, the adjusted prediction value may be used to generate a difference (e.g., XOR) for storing in a compressed data store to represent the third data value.

Determining an expected compression rate for a prospective process in a federated system includes obtaining compression rate data for existing processes in the federated system, compiling the compression rate data into a plurality of entries in a process name table according to process identifier, client, and industry, determining a specific entry in the process name table for an existing process that most closely matches the prospective process, and determining an expected compression rate of the prospective process based on the compression rate data for the specific entry. Compression rate data may be provided by a driver at host systems that sends compression rate information to a central repository. The central repository may be provided by a host system at a data center of the federated system. The compression rate data may use a sliding average that weighs the data more heavily to favor more recent data.

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.

An information processing device and method for enabling partial control of the resolution of a data group that can be turned into a tree structure. Data of an Octree pattern is encoded, so that a bit stream containing depth control information indicating that a leaf node is to be formed at a different level from the lowest level based on information specifying the depth of the Octree pattern is generated. Also, a bit stream is decoded, so that an Octree pattern including a leaf node at a different level from the lowest level is constructed, on the basis of depth control information indicating that the leaf node is to be formed at a different level from the lowest level based on information specifying the depth of the Octree pattern. The present disclosure can be applied to an information processing device, an image processing device, an electronic apparatus, an information processing method, a program, or the like, for example.

Various embodiments include methods and devices for implementing compression of high dynamic ratio fields. Various embodiments may include receiving a compression block having data units, receiving a mapping for the compression block, wherein the mapping is configured to map bits of each data unit to two or more data fields to generate a first set of data fields and a second set of data fields, compressing the first set of data fields together to generate a compressed first set of data fields, and compressing the second set of data fields together to generate a compressed second set of data fields.

Computer-implemented methods, systems, and devices to perform lossless compression of floating point format time-series data are disclosed. A first data value may be obtained in floating point format representative of an initial time-series parameter. For example, an output checkpoint of a computer simulation of a real-world event such as weather prediction or nuclear reaction simulation. A first predicted value may be determined representing the parameter at a first checkpoint time. A second data value may be obtained from the simulation. A prediction error may be calculated. Another predicted value may be generated for a next point in time and may be adjusted by the previously determined prediction error (e.g., to increase accuracy of the subsequent prediction). When a third data value is obtained, the adjusted prediction value may be used to generate a difference (e.g., XOR) for storing in a compressed data store to represent the third data value.

Significance flags in advanced video compression systems are coded using contexts adaptive to the last N significance flags coded taken in a scanning order. One embodiment uses the last N significance flags in scanning order as a predictor to determine which of a plurality of sets of significance flag contexts to use for coding subsequent significance flags. A second embodiment uses the last N significance flags in scanning order as a predictor in order to modulate the probability value associated with significance flag contexts that are used to code significance flags for future coding.