Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: A method and apparatus for application anomaly detection and remediation is described. The method may include receiving a plurality of event tracking messages generated by configurable applications after a feature treatment is deployed to configurable applications running on a first set of end user systems, and associating the feature treatment with values of a metric from the event messages that are attributable to the feature treatment being executed by the one or more configurable applications. The method may also include determining an impact of the feature treatment on the degradation of the metric when compared to a control value of the metric determined from a second plurality of end user systems that are not exposed to the feature treatment. Then method may further include, in response to detecting the statistically significant degradation of the metric, performing one or more actions to remediate the undesired impact of the feature treatment on the execution of the application.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: Systems for dynamic watermarking within a cloud-based collaboration environment. Storage facilities are managed by servers to provide cloud-based storage services to two or more client entities as a service across a network. The servers maintain versions of editable documents or photo images or video clips using a version indication. Dynamic watermarking applies a first watermark image to a first version of the editable document and grants edit access to a first client entity of the two or more client entities. While the first client entity has edit access to the first version of the editable document the system generates a second version of the editable document and applies a second watermark image to the second version of the editable document before delivering the second version to a second client entity. A security watermark is based on a user credential, and a leak source can be determined based the user credential.

Abstract: Systems for dynamic watermarking within a cloud-based collaboration environment. Storage facilities are managed by servers to provide cloud-based storage services to two or more client entities as a service across a network. The servers maintain versions of editable documents or photo images or video clips using a version indication. Dynamic watermarking applies a first watermark image to a first version of the editable document and grants edit access to a first client entity of the two or more client entities. While the first client entity has edit access to the first version of the editable document the system generates a second version of the editable document and applies a second watermark image to the second version of the editable document before delivering the second version to a second client entity. A security watermark is based on a user credential, and a leak source can be determined based the user credential.

Abstract: Systems for dynamic watermarking within a cloud-based collaboration environment. Storage facilities are managed by servers to provide cloud-based storage services to two or more client entities as a service across a network. The servers maintain versions of editable documents or photo images or video clips using a version indication. Dynamic watermarking applies a first watermark image to a first version of the editable document and grants edit access to a first client entity of the two or more client entities. While the first client entity has edit access to the first version of the editable document the system generates a second version of the editable document and applies a second watermark image to the second version of the editable document before delivering the second version to a second client entity. A security watermark is based on a user credential, and a leak source can be determined based the user credential.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: Systems for dynamic watermarking within a cloud-based collaboration environment. Storage facilities are managed by servers to provide cloud-based storage services to two or more client entities as a service across a network. The servers maintain versions of editable documents or photo images or video clips using a version indication. Dynamic watermarking applies a first watermark image to a first version of the editable document and grants edit access to a first client entity of the two or more client entities. While the first client entity has edit access to the first version of the editable document the system generates a second version of the editable document and applies a second watermark image to the second version of the editable document before delivering the second version to a second client entity. A security watermark is based on a user credential, and a leak source can be determined based the user credential.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified rules to events in an action log framework (ALF) stream is disclosed. More specifically, collaboration environments can include features or mechanisms that allow end-users or collaborators to perform a variety of actions such as, for example, file uploads and previews, or collaboration operations such as, for example, task assignment and comments. These actions can be captured or logged by an action log framework and, as described herein, automatically translated into one or more jobs by applying customizable user-specified rules.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified metadata rules to metadata events. More specifically, the scalable architecture described herein uses metadata to drive automations and/or polices in a cloud-based environment. In one embodiment, the architecture integrates a metadata service with an event-based automation engine to automatically trigger polices and/or automations based on metadata and/or changes in metadata changes. The metadata service can include customizable and/or pre-build metadata templates which can be used to automatically apply a metadata framework (e.g., particular fields) to files based on, for example, the upload or placement of a particular file in a particular folder. The architecture also provides for advanced metadata searching and data classification.

Abstract: Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified rules to events in an action log framework (ALF) stream is disclosed. More specifically, collaboration environments can include features or mechanisms that allow end-users or collaborators to perform a variety of actions such as, for example, file uploads and previews, or collaboration operations such as, for example, task assignment and comments. These actions can be captured or logged by an action log framework and, as described herein, automatically translated into one or more jobs by applying customizable user-specified rules.