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: A setting method of a protective component includes a resin supply step of supplying a thermoplastic resin to a flat support surface of a support table, and a protective component forming step of shaping the thermoplastic resin into a sheet shape through pressing and spreading the thermoplastic resin along the support surface while heating and softening the thermoplastic resin to form a protective component of the thermoplastic resin in the sheet shape on the support surface. The setting method includes also a protective component bonding step of bringing a front surface that is one surface of the workpiece into tight contact with one surface of the protective component in the sheet shape and heating the protective component in tight contact to bond the protective component to the workpiece, and a post-bonding cooling step of cooling the protective component heated in the protective component bonding step.

Abstract: A data compression processing system includes: a selection unit configured to select a compression processing setting to be applied to processing target data from among a plurality of compression processing settings; and a compression processing unit configured to compress the processing target data according to the compression processing setting selected by the selection unit. The selection unit is configured to: receive a condition required for compression processing of the processing target data; apply a plurality of compression processing settings applicable to the processing target data to evaluation data to perform compression processing; select, from the plurality of compression processing settings, a setting that satisfies the condition when applied to the processing target data based on a result of the compression processing for the evaluation data; and manage the selected compression processing setting in association with the condition.

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: A file storage include: a processor that receives a write request for a file from an application, writes data of the file to a storage unit, then compresses the data of the written file, and writes the compressed data to the storage unit. The processor determines a compression algorithm to be used for the compression according to an amount of data, which is written during a predetermined time, of one or more written files.

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 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: The computation load of computation using a neural network can be lowered. A computation apparatus has a prediction device, an encoder, and a decoder, and encodes and decodes data by using a probability density distribution. Of a learning process and a compression process, at least the compression process can be executed. By performing learning by using a neural network created by the learning process, a probability distribution table that causes a parameter and a symbol value probability distribution to correspond to each other can be used. In the compression process, the prediction device calculates the parameter from input data, and the encoder compresses the input data by using the symbol value probability distribution on the basis of the calculated parameter and the probability distribution table.

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: It is assumed that at least one of the plurality of nonvolatile semiconductor memory devices is a nonvolatile semiconductor memory device (hereinafter, referred to as a first memory device) in a low power consumption state in which error check processing and refresh processing cannot be performed. A storage apparatus releases a low power consumption state of a first memory device at a timing according to a lapsed time after the first memory device is in the low power consumption state and an estimated ambient temperature of the first memory device at the lapsed time. When the low power consumption state is released, the first memory device executes the error check processing and the refresh processing.

Abstract: t is assumed that at least one of the plurality of nonvolatile semiconductor memory devices is a nonvolatile semiconductor memory device (hereinafter, referred to as a first memory device) in a low power consumption state in which error check processing and refresh processing cannot be performed. A storage apparatus releases a low power consumption state of a first memory device at a timing according to a lapsed time after the first memory device is in the low power consumption state and an estimated ambient temperature of the first memory device at the lapsed time. When the low power consumption state is released, the first memory device executes the error check processing and the refresh processing.

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 setting method of a protective component includes a resin supply step of supplying a thermoplastic resin to a flat support surface of a support table, and a protective component forming step of shaping the thermoplastic resin into a sheet shape through pressing and spreading the thermoplastic resin along the support surface while heating and softening the thermoplastic resin to form a protective component of the thermoplastic resin in the sheet shape on the support surface. The setting method includes also a protective component bonding step of bringing a front surface that is one surface of the workpiece into tight contact with one surface of the protective component in the sheet shape and heating the protective component in tight contact to bond the protective component to the workpiece, and a post-bonding cooling step of cooling the protective component heated in the protective component bonding step.