Abstract: Methods and systems for managing the automated retrieval, aggregation, and selection of virtual machines, virtual disks, and electronic files using an integrated data management and storage system are described. In some cases, the integrated data management and storage system may acquire a query with search criteria to identify a set of objects accessible via the integrated data management and storage system, determine the set of objects that satisfies the search criteria, determine a first subset of the set of objects less than all of the set of objects to render, render and display a listing of the first subset, determine a selection of a first plurality of the first subset, and initiate concurrent actions on the first plurality using the integrated data management and storage system. The size of the first subset may vary over time and be determined based on prior user feedback.

Abstract: Data security for data sequences is disclosed, including: receiving, over a network, a first sketch corresponding to a first file, wherein the first sketch was determined from hash values generated from a data sequence associated with the first file; determining that the first sketch and a second sketch corresponding to a second file at least partially overlap; and determining whether a data security policy is violated based at least in part on the determination that the first sketch and the second sketch at least partially overlap.

Abstract: Methods and systems for managing the automated retrieval, aggregation, and selection of virtual machines, virtual disks, and electronic files using an integrated data management and storage system are described. In some cases, the integrated data management and storage system may acquire a query with search criteria to identify a set of objects accessible via the integrated data management and storage system, determine the set of objects that satisfies the search criteria, determine a first subset of the set of objects less than all of the set of objects to render, render and display a listing of the first subset, determine a selection of a first plurality of the first subset, and initiate concurrent actions on the first plurality using the integrated data management and storage system. The size of the first subset may vary over time and be determined based on prior user feedback.

Abstract: Determination of related data sets is disclosed, including: analyze a first plurality of columns belonging to a first data set by determining first attributes for each column in the first data set; analyze a second plurality of columns belonging to a second data set by determining second attributes for each column in the second data set; determine a set of common columns belonging to the first data set and the second data set by comparing at least a portion of the first attributes for each column in the first data set to at least a portion of the second attributes for each column in the second data set; and cluster a plurality of data sets including the first data set and the second data set based at least in part on the set of common columns.

Abstract: Data set discovery is disclosed, including: identifying first file metadata elements for a first file associated with a node in a hierarchy of data; identifying second file metadata elements for a second file associated with the node; identifying common file metadata elements among the first file metadata elements and the second file metadata elements; and determining that the common file metadata elements represent a data set comprising at least the first file and the second file.

Abstract: Data set discovery is disclosed, including: identifying first file metadata elements for a first file associated with a node in a hierarchy of data; identifying second file metadata elements for a second file associated with the node; identifying common file metadata elements among the first file metadata elements and the second file metadata elements; and determining that the common file metadata elements represent a data set comprising at least the first file and the second file.

Abstract: Data set discovery is disclosed, including: identifying first file metadata elements for a first file associated with a node in a hierarchy of data; identifying second file metadata elements for a second file associated with the node; identifying common file metadata elements among the first file metadata elements and the second file metadata elements; and determining that the common file metadata elements represent a data set comprising at least the first file and the second file.

Abstract: In some examples, a computer-implemented method of providing a calendar view interface in a client device includes receiving a request for a calendar view interface from the client device, determining a period of time to be presented in the calendar view interface, generating a calendar view interface for the period of time including an indicator for each day of the time period, retrieving snapshot information for the period of time, and generating a snapshot indicator for each day within the time period based on the snapshot information.

Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

Abstract: A system that manages backups of filesystems. The system analyzes and presents changes in a file system. The system generates user interfaces that present changes to files in the filesystem. The user interfaces allow users to traverse the filesystem to view changes in the filesystem. The changes include file operations that took place during a time interval. As such, users can ascertain a filesystem behavior such as which files were added, modified, or deleted during a time period; how many files under a folder were added, modified, or deleted; or which user has issued most operations in a time interval.

Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

Abstract: A system that manages backups of filesystems. The system analyzes and presents changes in a file system. The system generates user interfaces that present changes to files in the filesystem. The user interfaces allow users to traverse the filesystem to view changes in the filesystem. The changes include file operations that took place during a time interval. As such, users can ascertain a filesystem behavior such as which files were added, modified, or deleted during a time period; how many files under a folder were added, modified, or deleted; or which user has issued most operations in a time interval.

Abstract: In some examples, a computer-implemented method of providing a calendar view interface in a client device includes receiving a request for a calendar view interface from the client device, determining a period of time to be presented in the calendar view interface, generating a calendar view interface for the period of time including an indicator for each day of the time period, retrieving snapshot information for the period of time, and generating a snapshot indicator for each day within the time period based on the snapshot information.

Abstract: In one aspect, a computer system automatically identifies style issues in a source code base. A reference set for a known style issue includes source code examples that exhibit the style issue. The source code examples in the reference set are compared to the source code base, for example using string convolution. Based on the comparison, locations in the source code base that are likely to exhibit the style issue are identified. Various steps in the processing may be implemented using machine learning models, clustering or other automated data science techniques.

Abstract: Disclosed is an approach to incorporate geographical access control features for a cloud-based storage platform. This allows, for example, enterprise administrators to define geographical areas (geofences) with arbitrary precision within which content access can be denied for items of data.

Abstract: Methods and systems for managing the automated retrieval, aggregation, and selection of virtual machines, virtual disks, and electronic files using an integrated data management and storage system are described. In some cases, the integrated data management and storage system may acquire a query with search criteria to identify a set of objects accessible via the integrated data management and storage system, determine the set of objects that satisfies the search criteria, determine a first subset of the set of objects less than all of the set of objects to render, render and display a listing of the first subset, determine a selection of a first plurality of the first subset, and initiate concurrent actions on the first plurality using the integrated data management and storage system. The size of the first subset may vary over time and be determined based on prior user feedback.

Abstract: Disclosed is an approach to incorporate geographical access control features for a cloud-based storage platform. This allows, for example, enterprise administrators to define geographical areas (geofences) with arbitrary precision within which content access can be denied for items of data.

Abstract: Disclosed is an approach to incorporate geographical access control features for a cloud-based storage platform. This allows, for example, enterprise administrators to define geographical areas (geofences) with arbitrary precision within which content access can be applied to items of data.

Abstract: Disclosed is an approach to incorporate geographical access control features for a cloud-based storage platform. This allows, for example, enterprise administrators to define geographical areas (geofences) with arbitrary precision within which content access can be applied to items of data.