Vmware, Inc.

Box Limited

Sesh Jalagam

Komal Mangtani

Vishwas Nagaraja

David Winterfeldt

Victor De Vansa Vikramaratne

Jialiang Wang

David Vengerov

Jenica Nash Blechschmidt

Steven Luis Cipolla

Robert Kyle Waldrop

Natalia Vinnik

Anne Elizabeth Hiatt Pearl

Sam Michael Devlin

Matthew DeLand

Alok Ojha

Benjamin Draffin

A deployment system enables a developer to generate a deployment plan according to a logical, multi-tier application blueprint defined by application architects. The deployment plan includes tasks to be executed for deploying application components on virtual computing resource provided in a cloud infrastructure. The deployment plan includes time dependencies that determine an execution order of the tasks according to dependencies between application components specified in the application blueprint. The deployment plan enables system administrators to view the application blueprint as an ordered workflow view that facilitates collaboration between system administrators and application architects.

System to generate a deployment plan for a cloud infrastructure according to logical, multi-tier application blueprint

Methods, systems and computer program products for recommendation systems. Embodiments commence by gathering a set of pathnames that refer to content objects of a collaboration system. A tokenizer converts at least some of the pathnames into vectors. The vectors comprise hierarchical path components such as folder names or file names, which vectors are labeled with an indication as to whether or not the folder or file referred to in a particular vector had been clicked on by one or more users. Some portion of the labeled vectors are used to train a predictive model. Another portion of the vectors are used to validate the predictive model. When the model exhibits sufficient precision and recall, the predictive model is then used to predict the probability that a particular user would have an interest in a particular folder or file. The folder name or file name is presented as a collaboration recommendation.

Determining collaboration recommendations from file path information

Systems and methods for presenting relevant collaboration activity to a collaboration system user. A method embodiment commences upon identifying user events that correspond to interactions between a plurality of users and collaboration objects. The interactions that had been performed and the collaboration objects are both associated with permissions attributes. The interactions are recorded as event records that include aspects of the permissions attributes. When a user opens a user interface, a set of event records is selected based at least in part on the permissions attributes with respect to the user. The selected set of event records are then used to generate a set of user-specific feed entries corresponding to particular individual ones of the set of event records. Characteristics of the individual event records and/or aggregations of event records are used to prepare user-specific feed entries that are presented in a user interface of a user device.

Presenting collaboration activities

Methods, systems and computer program products for computing system security. Techniques for classifying a potentially unauthorized user as an authorized user involve comparisons of two or more access request times that occur at different computing devices in different geographical locations. Based on those comparisons and the distance between the geographical locations of the different computing devices, a determination is made as to whether or not travel (e.g., via overland travel, via air travel, etc.) between those different geographical locations can be reasonably accomplished within a given time period. If it is determined that the required time for travel between the different geographical locations is greater than the time between the access request times&#x2014;thus suggesting a spoofing attack or other malfeasance&#x2014;then the potentially unauthorized (i.e., only potentially malfeasant) access can still be deemed as an authorizable access request by analyzing browser configurations and activity patterns of the potentially unauthorized user.

Avoiding user session misclassification using configuration and activity fingerprints

Systems and methods for filtering collaboration activity to present in an activity feed. A cloud-based collaboration system is configured to identify collaboration interactions that users have taken over collaboration objects. Some of the collaboration objects or interactions are subject to access permissions as well as policy-based access rules. When a subject user interacts with a user interface, system components select a first set of user interaction events that in some way pertain to the subject user. A first filtering pass applies a first filter to determine object access permissions. After reducing the first set to a smaller second set, then a second filter corresponding to policy-based access rules is applied to the second set to form a still smaller third set of user interaction events. Characteristics of user interaction events in the third set are used to generate user-specific activity feed entries that are presented in a user interface.

Filtering collaboration activity

Systems and methods for on-demand generation of customizable collaboration network graphs. A method embodiment operates in a collaboration system that comprises content objects that are operated on by a plurality of users. Interactions with the content objects are detected and streamed into a customizable graph platform. Customization is accomplished by configuring the graph platform to gather particular events and corresponding data that pertain to the entity interaction events and/or to the users that raised the events. The graph platform organizes the data into data structures that codify a collaboration network graph, where the nodes of the collaboration network graph refer to customizable ones of the content objects and where the edges of the collaboration network graph refer to customizable parameters or values that characterize relationships between connected nodes of the collaboration network graph. Multiple behavior overlays can be applied to cause the customizable graph platform to process particular events differently.

On-demand collaboration user interfaces

Systems for curating and presenting relevant collaboration activity to newly-added users in a cloud-based content management platform. A method embodiment commences by observing and recording user events that correspond to interactions between any number of users and any number of content objects. When a new person who does not have an event history is added, his/her user profile is updated to indicate he/she is a new user, and a start-up proxy user is identified. The proxy user refers to either a real user that is in some way similar to the newly-added user, or the proxy user is a virtual user as amalgamated from previously recorded user history records. The events in the history records of the proxy user are associated with the newly-added user. When the newly-added user interacts with a user interface of the cloud-based content management platform, feed entries are generated and presented to the newly-added user.

Curating collaboration activity

Systems for forming and maintaining spontaneous networks of collaborators in shared content management systems. A shared content management system supports user interactions with content objects. A service of the content management system monitors occurrences of interactions between users and objects. The users are associated with collaboration groups. To generate recommendations of groups other than the collaboration group or groups in which a particular user is already a member, a method embodiment receives entity relationship scores from the service. An entity relationship score quantifies a relationship between two subject entities that are common to a particular entity interaction event. The method then assigns the subject entities to one or more spontaneously-generated clusters. As clusters are formed and populated, cluster affinity scores are continuously calculated. Periodically, a recommended cluster is selected based on a corresponding cluster affinity score. A recommended cluster is named based on the member entities of the recommended cluster.

Spontaneous networking

Systems for managing content in a cloud-based service platform. A server in a cloud-based environment is interfaced with storage devices that hold one or more stored objects accessible by two or more users. The stored objects comprise folders and files as well as other objects such as workflow objects that are associated with the folders or the files. The workflow objects comprise workflow metadata that describes a workflow as a set of workflow tasks to be carried out in a progression. Processing of a workflow task and/or carrying out a portion of the progression includes modification of shared content objects. The processing or modification events are detected through workflow events, which in turn cause one or more workflow responses to be generated. Workflow responses comprise updates to the workflow metadata to record progression through the workflow and/or workflow responses comprise updates to any one or more of the stored objects.

Managing flow-based interactions with cloud-based shared content

A deployment system enables a developer to define a logical, multi-tier application blueprint that can be used to create and manage (e.g., redeploy, upgrade, backup, patch) multiple applications in a cloud infrastructure. In the application blueprint, the developer models an overall application architecture, or topology, that includes individual and clustered nodes (e.g., VMs), logical templates, cloud providers, deployment environments, software services, application-specific code, properties, and dependencies between top-tier and second-tier components. The application can be deployed according to the application blueprint, which means any needed VMs are provisioned from the cloud infrastructure, and application components and software services are installed.

Single, logical, multi-tier application blueprint used for deployment and management of multiple physical applications in a cloud infrastructure

A deployment system orchestrates execution of deployment plan in coordination with nodes participating in deployment of a multi-tier application in a cloud infrastructure. The deployment system distributes local deployment plans to each node and maintains a centralized state of deployment time dependencies between tasks in different local deployment plans. Prior to execution of each task, deployment agents executing on each node communicates with the centralized deployment system to check whether any deployment time dependencies need to be resolved. Additionally, the deployment system utilizes a node task timer that triggers a heartbeat mechanism for monitoring failure of deployment agents.

Execution of a distributed deployment plan for a multi-tier application in a cloud infrastructure

A user may create a blueprint that specifies an application's architecture, including virtual machines (VM) nodes, software services and application components within the VM nodes. To do so, the user manipulates a graphical user interface (GUI) rendered by a graph layout manager and a Scalable Vector Graphics (SVG) layout engine. The SVG layout engine parses declarative layout configurations and translates the declarative layout into SVG elements that visually represent the blueprint. Additional information may be rendered in independent layers overlaid on the GUI which a user may selectively activate or de-activate to show or hide, respectively, the additional information. The layers may be generated using extension points exposed via the declarative layout.

HTML5 graph overlays for application topology

A user may create a blueprint that specifies an application's architecture, including virtual machines (VM) nodes, software services and application components within the VM nodes. To do so, the user manipulates a graphical user interface (GUI) rendered by a Scalable Vector Graphics (SVG) layout engine. The SVG layout engine parses declarative layout configurations and translates the declarative layout into SVG elements that visually represent the blueprint. The SVG layout engine dynamically calculates absolute positions and sizes of child elements based on the declarative layout.

HTML5 graph layout for application topology

A user may create a blueprint that specifies an application's architecture, including virtual machines (VM) nodes, software services and application components within the VM nodes. To do so, the user manipulates a graphical user interface (GUI) rendered by a graph layout manager and a Scalable Vector Graphics (SVG) layout engine. The SVG layout engine parses declarative layout configurations and translates the declarative layout into SVG elements that visually represent the blueprint. The graph layout manager renders topology connections representing deployment dependencies in the GUI using a street-block-and-roads design pattern.

Growing community of inventors

Union City, CA, United States of America

Sesh Jalagam