Hitachi, Ltd.

Takahiro Naruko

Hiroaki Akutsu

Akifumi Suzuki

Katsuto Sato

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.

Data compression system and data compression method

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.

Data compression and decompression system and method thereof

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.

Storage system and storage cost optimization method

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.

Storage system including encoder

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.

Storage system and storage control method including dimension setting information representing attribute for each of data dimensions of multidimensional dataset

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.

Data processing system and data compression method

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. In a case that it is determined that the first identification information, and the second identification information do not match, the second computer requests a third computer that is capable of decompression of the compressed data to decompress the compressed data, the third computer decompresses the compressed data, and transmits decompressed data to the second computer, and the second computer completes the decompression of the compressed data by receiving the decompressed data from the third computer.

Data compression/decompression system, and data compression/decompression method

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.

Storage system and storage control method

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.

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Tokyo, Japan

Takahiro Naruko