Example methods and systems are directed to a multitenant application server using a union file system. Each tenant has one or more users and a tenant layer in the union file system. Each user has a user layer in the union file system. The union file system provides a logical file system to each user based on the user layer, the tenant layer, and a base layer comprising a set of application layers. A first user shares an application template file with other users of the same tenant by moving the file from the first user's user layer to the tenant layer. After the file is moved, all users of the tenant have access to the application defined by the application template file. The moving of the file is achieved by modifying metadata for the file.

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.

A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.

A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.

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.

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.

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.

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.