Google Inc.

Victor Bennett

Gorkem Ozcelebi

Kourosh Gharachorloo

Gokul Rajaram

Niels Provos

Shrish Agrawal

Jayesh Sharma

Sasank Mudunuri

A system and method for verifying content on a network site is provided. A document such as a website is identified. The document is accessed over a network from a content provider system connected to the network to obtain information about the document. Through an automated process, using the information about the document to determine whether the document conforms to one or more predetermined rules associated with the content provider system governing the usage of content in the document.

Method and system for using a network analysis system to verify content on a website

A system determines human recognizable labels for portions of an electronic media stream, gathers data associated with the electronic media stream from a number of media players, and determines at least one section of the electronic media stream with a particular media feature. The system selects a representative clip for the electronic media stream based on information regarding the labeled portions, the gathered data, and the at least one section.

Automatic selection of representative media clips

A traffic prediction component may automatically generate predicted traffic profiles for web sites based on tags that characterize the sites. An initial set of tags can be selected for a web site based on a set of predefined rules. An initial traffic profile may be selected based on the initial set of tags. The predicted profile of user traffic is then generated based on the initial set of tags and on the initial traffic profile.

Traffic prediction for web sites

A system trains a first model to identify portions of electronic media streams based on first attributes of the electronic media streams and/or trains a second model to identify labels for identified portions of the electronic media streams based on at least one of second attributes of the electronic media streams, feature information associated with the electronic media streams, or information regarding other portions within the electronic media streams. The system inputs an electronic media stream into the first model, identifies, by the first model, portions of the electronic media stream, inputs the electronic media stream and information regarding the identified portions into the second model, and/or determines, by the second model, human recognizable labels for the identified portions.

Deconstructing electronic media stream into human recognizable portions

A system stores an electronic document that has markers inserted within the electronic document. The system visually renders the electronic document to a user and uses the inserted markers to determine a speed at which a reader reads the electronic document.

Detection and utilization of document reading speed

A system provides account statistics to a user of a network service. The system provides access, to a user, to the network service. The system determines one of an environmental attribute or context associated with the user's access of the network service. The system provides, contemporaneously with the access of the network service, a document to the user, where the document includes account statistics related to the one of the determined environmental attribute or content.

Context-aware processes for allowing users of network services to access account information

A system includes multiple logging units, an aggregating unit, and an evaluation unit. The logging unit logs traffic attributes associated with network traffic received at multiple network nodes and generates traffic signatures using the received attributes. The aggregating unit aggregates the traffic signatures generated at the multiple logging units. The evaluating unit determines relationships among the network traffic or between the plurality of network nodes using the aggregated traffic signatures.

Systems and methods for relating network traffic using traffic-based signatures

Errors occurring in computing clusters and other computing systems can impact system performance. Each error has an error type and each error type has a base cost estimating importance of correcting the error. Each error type also has a confidence indicating the level of agreement between those who fix the errors and those who assigned the base cost. An error type's actual cost is produced using the base cost and confidence. An error cascade map contains estimates that one error will cause another. An error type that causes other error types has a cascade cost. Upon detecting an error type, a repair order can be generated, depending on the cost involved. Repairs are then performed. Feedback mechanisms and correlations can be used to update the confidences and the error cascade map.

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Berkeley, CA, United States of America

Victor Bennett