A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive metadata and results including at least one data transmission constraint, perform a search of a k-d tree based on the at least one data transmission constraint, and select at least one data compression process from the k-d tree based on the search. The k-d tree includes a plurality of nodes corresponding to data compression solutions.

A method of sending blocks of data from a client to be stored at a storage server, wherein for each block compression and encryption is performed at the client, and deduplication is performed at the server. Security is thus enhanced as the block is compressed and encrypted when it is sent over an unsecured network and when it is stored in potentially a third-party backup system. Provisions are made to enable addition of new compression algorithms and for retirement of old compression algorithms, while ensuring that a client would not receive a block which was compressed using an unsupported, e.g., retired, compression algorithm. In some examples a compression algorithm ID is tied to an encryption key version to enable refresh of blocks compressed with old algorithm.

A sensor data are compressed on field devices using a representation is provided. The field device immediately decompresses the compressed data in order to detect a deviation. If there is a deviation, then a cloud storage receives the sensor data as raw uncompressed data. A cloud component receives a trigger signal from the field device, indicating that the representation used by the field device for compression does not sufficiently describe the sensor data. The cloud component then learns a new representation by retrieving and analyzing all data stored in the cloud storage. The method and field device provide robust, compression-based data acquisition. They improve quality and precision of the data captured by the field devices. As the representation in the field device can be updated, it becomes possible to accommodate changes in the device setup. The cloud infrastructure provides automatic learning of the representation in the cloud.

A data compression system in a storage system compresses data with a first compression method to generate compressed data, determines whether a compression rate of the compressed data is better than a predetermined reference, outputs data obtained by compressing the data by the compression method having a better compression rate than that of the other compression method of the first compression method and a second compression method when it is determined that the compression rate is better than the reference, and outputs data obtained by compressing the data by the compression method having a worse compression rate than that of the other compression method of the first compression method and the second compression method when it is determined that the compression rate is equal to or worse than the reference.

A method, computer program product, and computer system for identifying, by a computing device, storage containers that contain cold data. At least a portion of the storage containers may be processed to determine whether a first compression technique will result in a higher level of compression above a threshold level of compression than a second compression technique. The storage containers may be processed using the first compression technique based upon, at least in part, determining that the first compression technique will result in the higher level of compression above the threshold level of compression than the second compression technique.

The present disclosure involves systems, software, and computer implemented methods for improved design and implementation of data access metrics for automated physical database design. An example method includes identifying a database workload for which index advisor access counters are to be tracked. Each SQL statement in the database workload is executed. For each SQL statement, attribute sets are determined for which a selection predicate filters a result for an SQL statement. An output cardinality of each selection predicate is determined. A logarithmic counter for an attribute set corresponding to the selection predicate is determined based on the output cardinality of the selection predicate. The determined logarithmic counter is incremented. Respective values for logarithmic counters of the determined attributes are provided to an index advisor. The index advisor determines attribute sets for which to propose an index based on the logarithmic counters of the respective attribute sets.

A first storage system compresses data relating to read and write by a primary site and stores the data in a first physical volume. A second storage system compresses data relating to read and write by a secondary site and stores the data in a second physical volume. When performing replication for transferring the data stored in the first physical volume of the first storage system to the second storage system and storing the data in the second physical volume, the first storage system and the second storage system determine, based on a compression scheme executable by the first storage system and a compression scheme executable by the second storage system, a compression scheme to be applied to transfer target data and transfer the transfer target data compressed by the determined compression scheme.

The present disclosure involves systems, software, and computer implemented methods for improved design and implementation of data access metrics for automated physical database design. An example method includes identifying a database workload for which index advisor access counters are to be tracked. Each SQL statement in the database workload is executed. For each SQL statement, attribute sets are determined for which a selection predicate filters a result for an SQL statement. An output cardinality of each selection predicate is determined. A logarithmic counter for an attribute set corresponding to the selection predicate is determined based on the output cardinality of the selection predicate. The determined logarithmic counter is incremented. Respective values for logarithmic counters of the determined attributes are provided to an index advisor. The index advisor determines attribute sets for which to propose an index based on the logarithmic counters of the respective attribute sets.

A Data Compression Manager (DCM) that can help a data provider (DP) and a data consumer (DC) to establish a data transfer using an advantageous data compression method. In one embodiment, when a DC wants to get some data from a DC, the DP will contact the DCM and the DCM will then choose data compression information based on (i.e., based at least on), for example, current conditions within the network (e.g., a maximum available network bandwidth), information about the data to be compressed (e.g., the type of the data), and/or the DC's data error tolerance. Then the DCM will indicate to the DP the chosen data compression information. The DP will then i) inform the DC of the compression information that is needed by the DC to decompress the data and ii) provide the requested data to the DC in compressed form.

To facilitate storage of data from plural data sources of an industrial automation control system, power distribution system or power generation system, a decision making device executes a machine learning algorithm to determine a compression technique in dependence on the data source from which data originates.