Systems and methods for full motion video search are provided. In one aspect, a method includes receiving one or more search terms. The search terms include one or more of a characterization of the amount of man-made features in a video image and a characterization of the amount of natural features in the video image. The method further includes searching a full motion video database based on the one or more search terms.

A system and method for backing up workloads for multiple tenants of a cloud computing system are disclosed. A method of backing up workloads for multiple tenants of a computing system includes triggering an archival process according to an archival policy set by a tenant, and executing the archival process by reading backup data of the tenant stored in a backup storage device of the computer system and transmitting the backup data to an archival store designated in the archival policy, and then deleting or invalidating the backup data stored in the backup storage device.

In one approach, filesets to be backed up are divided into partitions and snapshots are pulled for each partition. In one architecture, a data management and storage (DMS) cluster includes a plurality of peer DMS nodes and a distributed data store implemented across the peer DMS nodes. One of the peer DMS nodes receives fileset metadata for the fileset and defines a plurality of partitions for the fileset based on the fileset metadata. The peer DMS nodes operate autonomously to execute jobs to pull snapshots for each of the partitions and to store the snapshots of the partitions in the distributed data store.

A data transcoding device includes a memory device for storing clear data containing private information and a processor configured as a data transcoder. The processor is configured to create packets of the clear data, prepare the packets for transcoding the clear data into an indecipherable multimedia data file appearing as noise, by determining properties of the indecipherable multimedia file based on parameters of the clear data. The processor is configured to generate the indecipherable multimedia file by transcoding the clear data based on the determined properties.

The electronic device includes a display, a memory, and a processor operatively coupled to the display and the memory. The processor is configured to identify a residual level of a storage space of the memory, in response to the residual level of the storage space of the memory being less than a predefined threshold value, control the display to display a file converting scheme list including a plurality of file converting schemes, select one file converting scheme among the plurality of file converting schemes, based on a received input, and perform file converting, based on the selected file converting scheme.

An integrated digital-analog archiving system can automatically initiate a migration process to move electronic documents to a media library. For each electronic document, the system may retrieve the electronic document from a digital data storage medium, extract metadata from the electronic document, determine size, orientation, and format of the electronic document, generate indicators for indicating the start and end of the electronic document to be stored on an analog data storage medium, generate an analog document identifier for identifying the electronic document on the analog data storage medium, generate a scaled image of the electronic document based on the size, orientation, and format of the electronic document, generate a text string based at least in part on the extracted metadata, and render the indicators, the analog document identifier, the scaled image of the electronic document, and the text string on the analog data storage medium.

Methods for file archiving using machine learning are disclosed herein. An exemplary method comprises archiving a first file of a plurality of files from a storage server to a tiered storage system, training a machine learning module based on file access operations for the plurality of files, determining one or more rules for predicting access to the archived files using the machine learning module, determining a prediction of access of the archived file based on the one or more rules and retrieving the archived file from the tiered storage system into a file cache in the storage server based on the prediction of access.

A method of summarizing data files includes implementing, at a server, a storage event for a data file, analyzing the data file and creating a summary of the data file, and storing the summary linked to the data file.

Systems and methods are provided for analyzing data in one or more datasets, where the one or more datasets are embodied as local, embedded databases in a client-side application, such as a web browser or web browser tab. A client-side data analysis application or artifact may be used to interact, i.e., query, the local, embedded databases, and retrieve results to analyze data. Because the one or more datasets are localized, there is no need to access a remote database/datastore in order to analyze the data. Moreover, the client-side data analysis application or artifact can be executed as individual instances in the client-side application. The state of a local, embedded database may be stored as another file that can be used as a local, embedded database for another instance of the client-side data analysis application or artifact.

In some embodiments, a computer system may buffer, in a local storage buffer, data to be stored at the cloud and upload the data to the cloud storage from the local storage buffer. Upon receipt of an acknowledgement, from the cloud storage, that the data was successfully uploaded, the computer system may delete the data from the local storage buffer. The computer system may provide a ghost file representation of the uploaded data through the computer system, through which the data uploaded to the cloud storage may be accessed. In some embodiments, the computer system may access multiple cloud storage accounts, each corresponding to a file folder on the computer system, allowing the user to easily access different cloud storage accounts. The computer system may recover data that was deleted from either local persistent storage or the cloud storage.