Microsoft Technology Licensing, LLC

Federico Gomez Suarez

Daron Spektor

Ryan Edward Gregg

Patrick Moulhaud

Graham Ramsay

John Mower

James David Morey

Youngha Joo

Phillip Wade Mendoza

Steven Paul Miale

Monte Windsor

David G Porter

Farhan Khan

Techniques and technologies for dynamic schema determination and enforcement are described. In at least some embodiments, a system comprises: a processing component; and a schema determination and enforcement component configured to receive a first data unit; analyze the first data unit to determine an inferred data schema; receive a second data unit; analyze the second data unit to determine whether the second incoming data unit complies with the inferred data schema; if the second data unit complies with the inferred data schema, write the second data unit to storage; and if the second data unit does not comply with the inferred data schema, at least one of: modify the inferred data schema based on the second data unit; or provide a notification of a non-compliance of the second data unit.

Dynamic schema inference and enforcement

An automated process to determine whether an image has been modified includes receiving an image (e.g., via a web portal), requesting an image validation service to analyze the image to determine whether the image and/or a subject depicted in the image, has been modified from its original form and, based on the analysis of the image validation service, outputting a likelihood that the image has been modified. The image validation service may analyze the image using one or more operations to determine a likelihood that the image has been modified, and provide an indication of the likelihood that the image has been modified to the web portal. The indication of the likelihood that the image has been modified may be presented on a display via the web portal, and various actions may be suggested or taken based on the likelihood that the image has been modified.

Automation of image validation

Activities of users of a service often involve one or more resources, such as uploading or downloading files in a file system of an FTP server. The activities of the users may be tracked and recorded in an activity log in order to identify frequently performed activities involving particular resources, and for such frequently performed activities, one or more activity actions may be performed. For example, malicious users may upload or utilize an equivalent set of assets stored in several accounts. The frequency of these undesirable activities may be identified, and an activity action may be automatically applied to the users (e.g., banning accounts), resources (e.g., deleting assets), and/or activities (e.g., blocking access to the resources). Conversely, desirable activities involving particular resources may be similarly detected, and the activity action applied to such desirable activities may involve reporting the desirable activity to an administrator of the service.

Applying activity actions to frequent activities

One or more techniques and/or systems are provided for selective server-side tracing of user requests that match tracing criteria. For example, internal server processing data and/or a server-side perspective of web traffic data associated with a server processing a user request may be generated server-side as tracing data. The tracing data may be utilized in troubleshooting issues that may arise between a client machine and the server (e.g., error in uploading a photo to a website hosted by the server). Because a web-based resource (e.g., website) may be hosted by a large number of servers configured to process numerous user requests over time, an overwhelming amount of tracing data may be produced in the absence of weeding out requests for which tracing data is not needed. Thus, utilizing tracing criteria to selectively trace particular user requests promotes efficient troubleshooting.

Server-side tracing of requests

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Redmond, WA, United States of America

Federico Gomez Suarez