Abstract: Solutions for enforcing both syntactic and semantic correctness of domain-specific data queries include: receiving a data query; constraining an operation of the data query to enforce semantic correctness, wherein enforcing semantic correctness comprises: determining semantic information associated with each of a plurality of data entities; based on at least the semantic information, determining whether combining the two data entities of the plurality of data entities is allowed or is not allowed; based on at least determining that combining the two data entities is allowed, permitting combining the two data entities in the data query; and based on at least determining that combining the two data entities is not allowed, preventing combining the two data entities in the data query; and executing the data query. Some examples further include basing the permission on (at least) an ontology between the semantic information of the two data entities and a set of axioms.

Abstract: Solutions for enforcing both syntactic and semantic correctness of domain-specific data queries include: receiving a data query; constraining an operation of the data query to enforce semantic correctness, wherein enforcing semantic correctness comprises: determining semantic information associated with each of a plurality of data entities; based on at least the semantic information, determining whether combining the two data entities of the plurality of data entities is allowed or is not allowed; based on at least determining that combining the two data entities is allowed, permitting combining the two data entities in the data query; and based on at least determining that combining the two data entities is not allowed, preventing combining the two data entities in the data query; and executing the data query. Some examples further include basing the permission on (at least) an ontology between the semantic information of the two data entities and a set of axioms.

Abstract: Technology described herein assigns a reliability score to web content, such as a web site or portion of a website. In one aspect, an output of the technology is a high reliability score and a low reliability score for a web content. The high reliability score represents conformance to high reliability sites, while the low reliability score represents conformance to low reliability sites. The high reliability score may be generated by first identifying high reliability online content within a compressed web graph. In a first iteration, the high reliability score of the seeds is used to score online content that is linked to the seed sites. At a high level, the more links that originate from high reliability sources, the higher the reliability score for the linked content. The low reliability score is similar, but uses outgoing links to low reliability sites instead of incoming links from high reliability sites.

Abstract: Technology described herein assigns a reliability score to web content, such as a web site or portion of a website. In one aspect, an output of the technology is a high reliability score and a low reliability score for a web content. The high reliability score represents conformance to high reliability sites, while the low reliability score represents conformance to low reliability sites. The high reliability score may be generated by first identifying high reliability online content within a compressed web graph. In a first iteration, the high reliability score of the seeds is used to score online content that is linked to the seed sites. At a high level, the more links that originate from high reliability sources, the higher the reliability score for the linked content. The low reliability score is similar, but uses outgoing links to low reliability sites instead of incoming links from high reliability sites.

Abstract: Solutions for enforcing both syntactic and semantic correctness of domain-specific data queries include: receiving a data query; constraining an operation of the data query to enforce semantic correctness, wherein enforcing semantic correctness comprises: determining semantic information associated with each of a plurality of data entities; based on at least the semantic information, determining whether combining the two data entities of the plurality of data entities is allowed or is not allowed; based on at least determining that combining the two data entities is allowed, permitting combining the two data entities in the data query; and based on at least determining that combining the two data entities is not allowed, preventing combining the two data entities in the data query; and executing the data query. Some examples further include basing the permission on (at least) an ontology between the semantic information of the two data entities and a set of axioms.

Abstract: Technology described herein assigns a reliability score to web content, such as a web site or portion of a website. In one aspect, an output of the technology is a high reliability score and a low reliability score for a web content. The high reliability score represents conformance to high reliability sites, while the low reliability score represents conformance to low reliability sites. The high reliability score may be generated by first identifying high reliability online content within a compressed web graph. In a first iteration, the high reliability score of the seeds is used to score online content that is linked to the seed sites. At a high level, the more links that originate from high reliability sources, the higher the reliability score for the linked content. The low reliability score is similar, but uses outgoing links to low reliability sites instead of incoming links from high reliability sites.

Abstract: In one example, a method includes monitoring user interaction with a modified computing environment for a period of time and detecting a plurality of groups of users, each group of users corresponding to a different initial time of interaction with the modified computing environment. The method also includes generating a metric for each of the groups of users, the metric corresponding to an action in the modified computing environment initiated by the users in each group. Additionally, the method includes generating an impact curve corresponding to the modified computing environment based on aggregated metrics for each of the plurality of groups of users and disabling the modification to the computing environment in response to detecting values along the impact curve are below a threshold value.

Abstract: In one example, a method includes monitoring user interaction with a modified computing environment for a period of time and detecting a plurality of groups of users, each group of users corresponding to a different initial time of interaction with the modified computing environment. The method also includes generating a metric for each of the groups of users, the metric corresponding to an action in the modified computing environment initiated by the users in each group. Additionally, the method includes generating an impact curve corresponding to the modified computing environment based on aggregated metrics for each of the plurality of groups of users and disabling the modification to the computing environment in response to detecting values along the impact curve are below a threshold value.