Abstract: To efficiently generate compressed data having ensured compatibility with a transmission destination. A computer system includes: a model configured to output inference data indicating importance of each region of multi-dimensional data; a compression level information generation unit configured to generate compression level information including a parameter for determining a data amount for each region of the multi-dimensional data based on the inference data; and a compressor configured to generate the compressed data in a data format having ensured compatibility with a transmission destination of compressed data by lossy compression using the compression level information.

Abstract: In a computer that compresses data, a compressor that divides the compression target data into a plurality of pieces of partial data, and performs compression processing on each piece of the partial data in parallel, the compressor including a probability calculation unit that includes a neural network and calculates an appearance probability for each predetermined data unit of the partial data, and an entropy coding unit that outputs a coded bit string that is an entropy-coded bit string based on the data unit and the appearance probability for each data unit.

Abstract: An object of the invention is to optimize a storage cost for data. There is provided a storage system including a storage device, a memory, and a processor configured to control input and output of data to and from the storage device. The processor monitors a storage amount that is at least one of a write amount (a total amount of data received as a write target) and a physical use amount (a total amount of data physically stored in the storage device), and a read amount (a total amount of data that is read), and calculates a fee as a storage cost that is a cost related to use of the storage device in a target period, based on a storage amount and a read amount in the target period in accordance with a monitoring result.

Abstract: To reduce a calculation processing load as a whole while realizing a small amount of data loss for at least one of compression and decompression. For each of a plurality of pieces of data, a storage system determines a compression operation scale of the data based on a feature of the data, executes a lossy compression operation according to the determined compression operation scale to covert the data into encoded data, and stores the encoded data or compressed data thereof into a storage device.

Abstract: To generate an optimum compressor irrespective of the number of dimensions and a format of a multidimensional dataset. A storage system refers to dimension setting information, which is information representing an attribute for each of data dimensions of the multidimensional dataset, and generates a compressor based on the dimension setting information.

Abstract: Provided is a data processing system which includes a processor and a storage device, and inputs/outputs data using a learned compander that compresses and expands data, wherein the data processing system comprises an estimation unit which uses learning data and estimates a region of interest to a data model, and a learning unit which causes the compander to learn according to an evaluation function in which each region was weighted based on the region of interest, and a result of the compander compressing and expanding the learning data.

Abstract: The present disclosure provides an information compression system that is capable of achieving higher compression efficiency. A data acquisition section acquires data. A generation section (segmentation section and integration section) determines each object depicted by the data and a sense of each object, and according to results of the determination, generates compression target data by converting values of elements in the data to identification information indicating each object and the sense of each object. A data storage section generates compressed data by compressing the compression target data. This makes it possible to convert highly random element values to slightly random identification information and compress the resulting converted information while reducing the amount of information. Consequently, the compression ratio can be increased.

Abstract: A data compression system compresses original data by a first lossy compression method to generate first compressed data, decompresses the first compressed data to generate first decompressed data, extracts residual information between the original data and the first decompressed data, compresses the residual information by a second lossy compression method different from the first lossy compression method to generate second compressed data, and stores the first compressed data and the second compressed data in the one or more storage devices.

Abstract: To provide a data compression/decompression system that can appropriately decompress compressed data. A first computer generates first identification information that can identify a first execution environment; and stores the first identification information on a storage device in association with compressed data of data. A second computer generates second identification information that can identify a second execution environment; and determines whether or not the first identification information, and the second identification information match.

Abstract: 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.

Abstract: Both of a high compression ratio and a high processing speed are achievable. In a data compression and decompression system that includes a parallel processing device performing a plurality of processes in parallel, the parallel processing device divides original data into a plurality of data by a predetermined unit. The parallel processing device performs coding processes on the plurality of data in parallel using a predetermined model to create a plurality of coded data. The parallel processing device creates compressed data of the original data from the plurality of coded data.

Abstract: To generate an optimum compressor irrespective of the number of dimensions and a format of a multidimensional dataset. A storage system refers to dimension setting information, which is information representing an attribute for each of data dimensions of the multidimensional dataset, and generates a compressor based on the dimension setting information.

Abstract: Provided is a data processing system comprising a compression/expansion unit configured by including a compressor which compresses data, and an expander which expands the data compressed by the compressor, wherein the compression/expansion unit comprises a first interface unit capable of outputting configuration information of the compressor, and a second interface unit capable of outputting the data compressed by the compressor.

Abstract: Provided is a data processing system which includes a processor and a storage device, and inputs/outputs data using a learned compander that compresses and expands data, wherein the data processing system comprises an estimation unit which uses learning data and estimates a region of interest to a data model, and a learning unit which causes the compander to learn according to an evaluation function in which each region was weighted based on the region of interest, and a result of the compander compressing and expanding the learning data.

Abstract: An object of the invention is to optimize a storage cost for data. There is provided a storage system including a storage device, a memory, and a processor configured to control input and output of data to and from the storage device. The processor monitors a storage amount that is at least one of a write amount (a total amount of data received as a write target) and a physical use amount (a total amount of data physically stored in the storage device), and a read amount (a total amount of data that is read), and calculates a fee as a storage cost that is a cost related to use of the storage device in a target period, based on a storage amount and a read amount in the target period in accordance with a monitoring result.

Abstract: An object of the invention is to optimize a storage cost for data. There is provided a storage system including a storage device, a memory, and a processor configured to control input and output of data to and from the storage device. The processor monitors a storage amount that is at least one of a write amount (a total amount of data received as a write target) and a physical use amount (a total amount of data physically stored in the storage device), and a read amount (a total amount of data that is read), and calculates a fee as a storage cost that is a cost related to use of the storage device in a target period, based on a storage amount and a read amount in the target period in accordance with a monitoring result.

Abstract: To provide a data compression/decompression system that can appropriately decompress compressed data. A first computer generates first identification information that can identify a first execution environment; and stores the first identification information on a storage device in association with compressed data of data. A second computer generates second identification information that can identify a second execution environment; and determines whether or not the first identification information, and the second identification information match.

Abstract: Provided is a storage system that can store various types of and large amounts of sensor data while accurately compressing the sensor data without wasting storage resources. The storage system includes: a storage that records sensor data output from a plurality of sensors; a processor that controls recording of sensor data in the storage; and a memory that records parameters of the plurality of sensors. The processor reads parameters assigned to the sensors that output the sensor data from the memory, normalizes the sensor data based on the parameters, compresses the normalized sensor data, and records the compressed sensor data in the storage.

Abstract: To reduce a calculation processing load as a whole while realizing a small amount of data loss for at least one of compression and decompression. For each of a plurality of pieces of data, a storage system determines a compression operation scale of the data based on a feature of the data, executes a lossy compression operation according to the determined compression operation scale to covert the data into encoded data, and stores the encoded data or compressed data thereof into a storage device.

Abstract: 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.