Described are methods, systems and computer readable media for data source refreshing.

A technique of processing storage cluster performance metrics involves obtaining access to performance metrics from storage appliances of a storage cluster, the performance metrics identifying performance for storage objects managed by the storage appliances. The technique further involves, after access to the performance metrics is obtained, disregarding a duplicate set of performance metrics for a storage object that migrates from a first storage appliance of the storage cluster to a second storage appliance of the storage cluster. The technique further involves, after the duplicate set of performance metrics is disregarded, archiving the performance metrics to an archive. After archiving the performance metrics to the archive, the technique may provide a performance analysis based on the performance metrics from the archive as well as adjust operation of the storage cluster according to the performance analysis.

Intelligent management of stub files in hierarchical storage is provided by: in response to identifying a file to migrate from a file system to offline storage, providing metadata for the file to a machine learning engine; receiving a stub profile for the file from the machine learning engine that indicates an offset from a beginning of the file and a length from the offset for previewing the file; and migrating the portion of the file from the file system to an offline storage based on the stub profile. In some embodiments this further comprises: monitoring file system operations; in response to detecting a read operation of the portion of the file: determining a file type; providing file data to the machine learning engine; and performing a supervised learning operation based on the file type and the file data to update the machine learning engine.

The disclosed technology relates to a system configured to detect a modification to a node in a tree data structure. The node is associated with a content item managed by a content management service as well as a filename. The system may append the filename and a separator to a filename array, determine a location of the filename in the filename array, and store the location of the filename in the node.

According to one embodiment, a computer-implemented method for file handling in a hierarchical storage environment includes performing a file access notification process for determining files related to the first file based on enhanced metadata and a priority list defining a likelihood of possible access, in response to receiving a file access notification corresponding to access of a first file. The related files are placed in a highest level storage tier, and the priority list is updated.

In one aspect, a computer-implemented method useful for migrating hundreds of Terabytes to Petabytes of data to a cloud-computing environment with a data transfer appliance includes the step of providing a data transfer appliance. The data transfer appliance includes an operating system, one or more computing processing units (CPU's), a memory, and a data storage system. The computer-implemented method includes the step of implementing data capture from a data storage system to the data transfer appliance. The computer-implemented method includes the step of storing the dedupe form of the data in the data transfer appliance by; providing a capture utility, wherein the capture utility comprises a data traversal engine and a data read engine.

A user of a storage system can upload files for a media asset, which can include a high quality media file and various related files. As part of the upload process, the storage system can extract metadata that describes the media asset. The user can specify one or more lifecycle policies to be applied for storage of the asset, and a rules engine can ensure the application of the one or more policies. The rules engine can also enable the use of simple media processing workflows. A filename hashing approach can be used to ensure that the segments and files for the asset are stored in a relatively random and even distribution across the partitions of the storage system. As part of the lifecycle for the asset, the high quality media file can be moved to less expensive storage once transcoding of the asset or another such action occurs.

Techniques herein make and use a pluggable database archive file (AF). In an embodiment, a source database server of a source container database (SCD) inserts contents into an AF from a source pluggable database (SPD). The contents include data files from the SPD, a listing of the data files, rollback scripts, and a list of patches applied to the SPD. A target database server (TDS) of a target container database (TCD) creates a target pluggable database (TPD) based on the AF. If a patch on the list of patches does not exist in the TCD, the TDS executes the rollback scripts to adjust the TPD. In an embodiment, the TDS receives a request to access a block of a particular data file. The TDS detects, based on the listing of the data files, a position of the block within the AF. The TDS retrieves the block based on the position.

Inter-operative switching of tools in a robotic system includes a system with a plurality of manipulators and a controller. The controller is configured to detect mounting of a first imaging device to a first manipulator of the plurality of manipulators, the first imaging device having a first reference frame; in response to detecting the mounting of the first imaging device, control a tool relative to the first reference frame using a second manipulator of the plurality of manipulators, the tool being mounted to the second manipulator; detect mounting of a second imaging device to a third manipulator of the plurality of manipulators, the second imaging device having a second reference frame; and in response to detecting the mounting of the second imaging device, control the tool relative to the second reference frame using the second manipulator.

A device receives, from a user device, a request to prune a primary database, and receives primary database information associated with the primary database and secondary database information associated with a secondary database that is different than the primary database. The device processes the primary database information and the secondary database information, with a machine learning model, to generate suggested pruning parameters, and provides the suggested pruning parameters to the user device. The device receives selected pruning parameters from the user device, where the selected pruning parameters are selected from the suggested pruning parameters or are input via the user device. The device removes pruned information from the primary database based on the selected pruning parameters, and provides the pruned information to the secondary database based on the selected pruning parameters.