Abstract: Embodiments relate to tenant-side detection and mitigation of performance degradation resulting from interference generated by a noisy neighbor in a distributed computing environment. A first machine-learning model such as a k-means nearest neighbor classifier is operated by a tenant to detect an anomaly with a computer system emulator resulting from a co-located noisy neighbor. A second machine-learning model such as a multi-class classifier is operated by the tenant to identify a contended resource associated with the anomaly. A corresponding trigger signal is generated and provided to trigger various mitigation responses, including an application/framework-specific mitigation strategy (e.g., triggered approximations in application/framework performance, best-efforts paths, run-time changes, etc.), load-balancing, scaling out, updates to a scheduler to avoid impacted nodes, and the like. In this manner, a tenant can detect, classify, and mitigate performance degradation resulting from a noisy neighbor.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to facilitate identification of security policies for documents. In one embodiment, content features are identified from a set of documents having assigned security policies. The content features and corresponding security policies are analyzed to generate a security policy prediction model. Such a security policy prediction model can then be used to identify a security policy relevant to a document.

Abstract: Embodiments relate to tenant-side detection and mitigation of performance degradation resulting from interference generated by a noisy neighbor in a distributed computing environment. A first machine-learning model such as a k-means nearest neighbor classifier is operated by a tenant to detect an anomaly with a computer system emulator resulting from a co-located noisy neighbor. A second machine-learning model such as a multi-class classifier is operated by the tenant to identify a contended resource associated with the anomaly. A corresponding trigger signal is generated and provided to trigger various mitigation responses, including an application/framework-specific mitigation strategy (e.g., triggered approximations in application/framework performance, best-efforts paths, run-time changes, etc.), load-balancing, scaling out, updates to a scheduler to avoid impacted nodes, and the like. In this manner, a tenant can detect, classify, and mitigate performance degradation resulting from a noisy neighbor.

Abstract: Embodiments relate to tenant-side detection and mitigation of performance degradation resulting from interference generated by a noisy neighbor in a distributed computing environment. A first machine-learning model such as a k-means nearest neighbor classifier is operated by a tenant to detect an anomaly with a computer system emulator resulting from a co-located noisy neighbor. A second machine-learning model such as a multi-class classifier is operated by the tenant to identify a contended resource associated with the anomaly. A corresponding trigger signal is generated and provided to trigger various mitigation responses, including an application/framework-specific mitigation strategy (e.g., triggered approximations in application/framework performance, best-efforts paths, run-time changes, etc.), load-balancing, scaling out, updates to a scheduler to avoid impacted nodes, and the like. In this manner, a tenant can detect, classify, and mitigate performance degradation resulting from a noisy neighbor.

Abstract: Techniques and systems are described in which a document management system is configured to update content of digital documents through use of static and transient tags. A transient tag, for instance, may be associated with portions of the digital document that may be changed and a static tag with portions of the digital document that are not to be changed. An update to the digital document is then triggered by a document management system based on a triggering change made to an initial document portion of the digital document having a transient tag, and is not based on changes made to portions having a static tag or are untagged.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to facilitate identification of security policies for documents. In one embodiment, content features are identified from a set of documents having assigned security policies. The content features and corresponding security policies are analyzed to generate a security policy prediction model. Such a security policy prediction model can then be used to identify a security policy relevant to a document.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to facilitate identification of security policies for documents. In one embodiment, content features are identified from a set of documents having assigned security policies. The content features and corresponding security policies are analyzed to generate a security policy prediction model. Such a security policy prediction model can then be used to identify a security policy relevant to a document.

Abstract: Techniques and systems are described in which a document management system is configured to update content of document portions of digital documents. In one example, an update to the digital document is initially triggered by a document management system by detecting a triggering change applied to an initial portion of the digital document. The document management system, in response to the triggering change, then determines whether trailing changes are to be made to other document portions, such as to other document portions in the same digital document or another digital document. To do so, triggering and trailing change representations are generated and compared to determine similarity of candidate document portions with an initial document portion.

Abstract: Embodiments relate to tenant-side detection and mitigation of performance degradation resulting from interference generated by a noisy neighbor in a distributed computing environment. A first machine-learning model such as a k-means nearest neighbor classifier is operated by a tenant to detect an anomaly with a computer system emulator resulting from a co-located noisy neighbor. A second machine-learning model such as a multi-class classifier is operated by the tenant to identify a contended resource associated with the anomaly. A corresponding trigger signal is generated and provided to trigger various mitigation responses, including an application/framework-specific mitigation strategy (e.g., triggered approximations in application/framework performance, best-efforts paths, run-time changes, etc.), load-balancing, scaling out, updates to a scheduler to avoid impacted nodes, and the like. In this manner, a tenant can detect, classify, and mitigate performance degradation resulting from a noisy neighbor.

Abstract: Techniques and systems are described in which a document management system is configured to update content of document portions of digital documents. In one example, an update to the digital document is initially triggered by a document management system by detecting a triggering change applied to an initial portion of the digital document. The document management system, in response to the triggering change, then determines whether trailing changes are to be made to other document portions, such as to other document portions in the same digital document or another digital document. To do so, triggering and trailing change representations are generated and compared to determine similarity of candidate document portions with an initial document portion.

Abstract: Techniques and systems are described in which a document management system is configured to update content of digital documents through use of static and transient tags. A transient tag, for instance, may be associated with portions of the digital document that may be changed and a static tag with portions of the digital document that are not to be changed. An update to the digital document is then triggered by a document management system based on a triggering change made to an initial document portion of the digital document having a transient tag, and is not based on changes made to portions having a static tag or are untagged.

Abstract: Rules for triggering geo-notifications, such as push notifications to a mobile computing device based on its location, are automatically generated based on a user's location, dwell time, and browsing activity. In some embodiments, a geo-fence communication system determines an average dwell time for a group of users in a user segment. The average dwell time indicates an average time for each user to respond to a geo-notification for a product, within a geo-fence. The geo-fence communication system generates a rule for the user segment/product/geo-fence tuple based on the average dwell time. In some cases, semantic similarities between geo-fence locations are determined, and a rule for a geo-fence is generated based on average dwell times for other geo-fences in similar locations.

Abstract: A method for propagating changes from master content to variant content includes receiving, from a user interface, a modified master text composition that incorporates changes to a master text composition. Each of the master text composition, the modified master text composition, and a variant text composition assigned to the master text composition are divided into text units. A correspondence model is constructed that includes mappings between the text units of the master text composition, the text units of the modified master text composition, and the text units of the variant text composition. Proposed changes to the variant text composition are determined that correspond to the changes to the master text composition based on the mappings. At least one of the proposed changes is integrated into the variant text composition.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to facilitate identification of security policies for documents. In one embodiment, content features are identified from a set of documents having assigned security policies. The content features and corresponding security policies are analyzed to generate a security policy prediction model. Such a security policy prediction model can then be used to identify a security policy relevant to a document.

Abstract: Techniques and systems are described to control output of a notification by a marketing system based on a prediction of location, activity, and/or time. In one example, selection of a notification from a plurality of notifications by the notification system is based on a series of activities performed by a user over time at respective locations with respect to an item of digital content. Based on this series of activities, a prediction is made by the notification system as to a likely location, activity, and even time at which a future activity is likely to be performed by the user. This prediction is then used by the notification system as a basis to control which notification is to be output by a computing device of the user.

Abstract: A method for propagating changes from master content to variant content includes receiving, from a user interface, a modified master text composition that incorporates changes to a master text composition. Each of the master text composition, the modified master text composition, and a variant text composition assigned to the master text composition are divided into text units. A correspondence model is constructed that includes mappings between the text units of the master text composition, the text units of the modified master text composition, and the text units of the variant text composition. Proposed changes to the variant text composition are determined that correspond to the changes to the master text composition based on the mappings. At least one of the proposed changes is integrated into the variant text composition.

Abstract: The present disclosure is directed toward systems and method for generating geo-fences having boundaries around geographic regions of interest based on historical activity data of a user application on a plurality of client devices. For example, systems and methods described herein involve tracking activity data to identify instances of an application activity associated with an action to promote and identify locations where users more frequently perform the application activity. In addition, systems and methods described herein involve tracking activity data over time to determine geographic regions of interest at different time periods where users more frequently perform application activities. Further, systems and methods described herein involve generating geo-fences around one or more geographic regions of interest.

Abstract: Techniques to personalize a sequence of marketing actions and/or marketing channels used to contact individuals are described herein. Marketing data may be analyzed to select a sequence of marketing actions to employ for targeted marketing to an individual user. The analysis involves a comparison of sequence data obtained from collected marketing data that describes sequencing for the marketing offers provided to consumers to one or more potential sequences for the individual user. The potential sequences may be ranked based on similarities in characteristics of consumers associated with sequences that achieved a designated objective and the individual user's marketing sequence. Characteristics used for the ranking may further include demographic details and behavioral information regarding the consumers and individual user. One or more top ranking sequences are identified and employed to determine one or more marketing actions to perform next to provide targeted marketing offers to the individual user.

Abstract: Techniques for multi-channel marketing campaigns are described herein. The techniques enable marketers to determine sequences of chronologically ordered communication channels by which to perform a multi-channel marketing campaign. In some cases, the techniques determine a sequence likely to have a positive result based on historic marketing sequence data and a desired category of the marketer's campaign. The techniques may also determine some number of trial sequences for a trial marketing campaign and then determine a best sequence for a full-scale marketing campaign based on the success of the trial sequences during the trial marketing campaign.