Abstract: A computer-implemented method includes receiving a set of basis fingerprints corresponding to image chunks within a basis set of image regions wherein each image region within the basis set of image regions comprises one or more image chunks, and generating a fingerprint for each image chunk of a plurality of selected image chunks within an unprocessed region of a machine image to produce a plurality of sampled fingerprints. The method also includes determining a similarity metric for the unprocessed region from the sampled fingerprints and the basis fingerprints, comparing the similarity metric for the unprocessed region with a selected threshold, and including the unprocessed region within the basis set of image regions in response to determining that the similarity metric is less than the selected threshold. A corresponding computer program product and computer system are also disclosed herein.

Abstract: A computer-implemented method includes receiving a set of basis fingerprints corresponding to image chunks within a basis set of image regions wherein each image region within the basis set of image regions comprises one or more image chunks, and generating a fingerprint for each image chunk of a plurality of selected image chunks within an unprocessed region of a machine image to produce a plurality of sampled fingerprints. The method also includes determining a similarity metric for the unprocessed region from the sampled fingerprints and the basis fingerprints, comparing the similarity metric for the unprocessed region with a selected threshold, and including the unprocessed region within the basis set of image regions in response to determining that the similarity metric is less than the selected threshold. A corresponding computer program product and computer system are also disclosed herein.

Abstract: A system and method for recovering a database and restoring an index following a failure of the database is disclosed. The method receives a change to a record in the database. The change is stored in a persistent data store, the persistent data store is divided into a plurality of segments. The volatile index is updated in volatile memory with a pointer to the record in the persistent data store. A shadow index is generated in the persistent data store, where the shadow index is a persistent copy of the volatile index and is not updated at the same time as the volatile index. The shadow thread is executed on the plurality of records where the shadow thread scans each record in the persistent storage device to populate and update the shadow index, wherein the shadow thread operates as a background operation on the persistent data store.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate, the heuristic computed by, for each one of the data classes, calculating an expected number of characters to be in a data class, calculating an expected number of characters that will not belong to a predefined set of the data classes, and calculating an actual number of the characters for each of the data classes and the non-classifiable data.

Abstract: A method for storage systems improvement includes collecting information that indicates one or more failure correlations for disks in a storage system. The disks are then separated into a plurality of virtual failure domains based on the indicated one or more failure correlations. The method then determines that all data objects of a set of redundant data objects are included in a first virtual failure domain. Responsive to determining that all data objects of the set of redundant data objects are included in the first virtual failure domain, the method then migrates at least one data object of the set of redundant data objects from a first disk in the first virtual failure domain to a second disk in a second virtual failure domain.

Abstract: A computer-implemented method includes receiving a set of basis fingerprints corresponding to image chunks within a basis set of image regions wherein each image region within the basis set of image regions comprises one or more image chunks, and generating a fingerprint for each image chunk of a plurality of selected image chunks within an unprocessed region of a machine image to produce a plurality of sampled fingerprints. The method also includes determining a similarity metric for the unprocessed region from the sampled fingerprints and the basis fingerprints, comparing the similarity metric for the unprocessed region with a selected threshold, and including the unprocessed region within the basis set of image regions in response to determining that the similarity metric is less than the selected threshold. A corresponding computer program product and computer system are also disclosed herein.

Abstract: A computer-implemented method includes receiving a set of basis fingerprints corresponding to image chunks within a basis set of image regions wherein each image region within the basis set of image regions comprises one or more image chunks, and generating a fingerprint for each image chunk of a plurality of selected image chunks within an unprocessed region of a machine image to produce a plurality of sampled fingerprints. The method also includes determining a similarity metric for the unprocessed region from the sampled fingerprints and the basis fingerprints, comparing the similarity metric for the unprocessed region with a selected threshold, and including the unprocessed region within the basis set of image regions in response to determining that the similarity metric is less than the selected threshold. A corresponding computer program product and computer system are also disclosed herein.

Abstract: A method for storage systems improvement includes collecting information that indicates one or more failure correlations for disks in a storage system. The disks are then separated into a plurality of virtual failure domains based on the indicated one or more failure correlations. The method then determines that all data objects of a set of redundant data objects are included in a first virtual failure domain. Responsive to determining that all data objects of the set of redundant data objects are included in the first virtual failure domain, the method then migrates at least one data object of the set of redundant data objects from a first disk in the first virtual failure domain to a second disk in a second virtual failure domain.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate, the heuristic computed by, for each one of the data classes, calculating an expected number of characters to be in a data class, calculating an expected number of characters that will not belong to a predefined set of the data classes, and calculating an actual number of the characters for each of the data classes and the non-classifiable data.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a distributed computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate, the heuristic computed by, for each one of the data classes, calculating an expected number of characters to be in a data class, calculating an expected number of characters that will not belong to a predefined set of the data classes, and calculating an actual number of the characters for each of the data classes and the non-classifiable data.

Abstract: Machines, systems and methods for increasing data resiliency in a computing system, the method comprising distinguishing between first data and second data stored in one or more data storage mediums, wherein the first data is more vulnerable than the second data for the purpose of recovering lost data; and recovering the first data before recovering the second data. Increasing redundancy protection for the first data to increase chances for data recovery by way of data reconstruction; and decreasing redundancy protection for the first data, after the first data has been backed up at least once.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate for determining if the data candidate may benefit from the classification-based compression, wherein a ratio is summed between the actual number of the characters and the expected number of the characters, and then dividing the ratio by a number of the data classes that are not empty, wherein the non-classifiable data are included in the number of the data classes during the dividing, and the number of the data classes, that are not empty, have characters that belong to the class that were observed in the input; and the classification-based compression is performed on the data candidates if the ratio exceeds a threshold.

Abstract: Batch processing machines, systems and methods are provided. The method comprises copying target data from a target location to a first data storage medium, wherein the target data stored in the first data storage medium is utilized by a batch process; executing the batch process; checkpointing state information for the batch process at one or more time intervals while the batch process is executing; determining whether a data record in the target data stored in the target location was updated while the batch process was executing; and reinstating the batch process from a point in time prior to an earliest point in the batch process when a representation of an updated data record in the target data in the target location was accessed by the batch process.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a distributed computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate, the heuristic computed by, for each one of the data classes, calculating an expected number of characters to be in a data class, calculating an expected number of characters that will not belong to a predefined set of the data classes, and calculating an actual number of the characters for each of the data classes and the non-classifiable data.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate for determining if the data candidate may benefit from the classification-based compression, wherein a ratio is summed between the actual number of the characters and the expected number of the characters, and then dividing the ratio by a number of the data classes that are not empty, wherein the non-classifiable data are included in the number of the data classes during the dividing, and the number of the data classes, that are not empty, have characters that belong to the class that were observed in the input; and the classification-based compression is performed on the data candidates if the ratio exceeds a threshold.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate for determining if the data candidate may benefit from the classification-based compression. A decision is provided for approving the classification-based compression on the data candidates according to the heuristic.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate for determining if the data candidate may benefit from the classification-based compression. A decision is provided for approving the classification-based compression on the data candidates according to the heuristic.

Abstract: Batch processing machines, systems and methods are provided. The method comprises copying target data from a target location to a first data storage medium, wherein the target data stored in the first data storage medium is utilized by a batch process; executing the batch process; checkpointing state information for the batch process at one or more time intervals while the batch process is executing; determining whether a data record in the target data stored in the target location was updated while the batch process was executing; and reinstating the batch process from a point in time prior to an earliest point in the batch process when a representation of an updated data record in the target data in the target location was accessed by the batch process.

Abstract: Machines, systems and methods for increasing data resiliency in a computing system, the method comprising distinguishing between first data and second data stored in one or more data storage mediums, wherein the first data is more vulnerable than the second data for the purpose of recovering lost data; and recovering the first data before recovering the second data. Increasing redundancy protection for the first data to increase chances for data recovery by way of data reconstruction; and decreasing redundancy protection for the first data, after the first data has been backed up at least once.

Abstract: Identification of data candidates for data processing is performed in real time by a processor device in a computing environment. Data candidates are sampled for performing a classification-based compression upon the data candidates. A heuristic is computed on a randomly selected data sample from the data candidate for determining if the data candidate may benefit from the classification-based compression. A decision is provided for approving the classification-based compression on the data candidates according to the heuristic.