Abstract: Controlling access to nodes in a relational graph at query time by using an approximate membership query (AMQ) filter and ordered queries based on historic grants or denials of access according to security context enables a more efficient querying of the relational graph while preserving access controls. Security contexts that grant or deny access to a node are stored in an associated AMQ filter and are queried according to the subject's security context in an order based on the frequency at which the security contexts have previously granted or denied access to nodes in the relational graph.

Abstract: Controlling access to nodes in a relational graph at query time by using an approximate membership query (AMQ) filter and ordered queries based on historic grants or denials of access according to security context enables a more efficient querying of the relational graph while preserving access controls. Security contexts that grant or deny access to a node are stored in an associated AMQ filter and are queried according to the subject's security context in an order based on the frequency at which the security contexts have previously granted or denied access to nodes in the relational graph.

Abstract: Controlling access to nodes in a relational graph at query time by using an approximate membership query (AMQ) filter and ordered queries based on historic grants or denials of access according to security context enables a more efficient querying of the relational graph while preserving access controls. Security contexts that grant or deny access to a node are stored in an associated AMQ filter and are queried according to the subject's security context in an order based on the frequency at which the security contexts have previously granted or denied access to nodes in the relational graph.

Abstract: A system and method for surfacing content relevant is described. The system computes a rank score for each item of a plurality of items that are accessible from a user of an enterprise application. The system identifies a set of relevant items from the plurality of items based on the rank score for each item of the plurality of items. The system surfaces the set of relevant items within a context of a communication application of the user without receiving, from the user, a query for any of the items in the set of relevant items.

Abstract: Reductions in latencies and improvements in computational efficiency when analyzing data stored in a relational graph by integrating analytical capabilities into graph queries. Instead of a user having to run a graph query and then perform analytics on the resulting subgraph via separate requests, the user is enabled to run analytics at the time the graph query is run via a single request to the database maintaining the relationship graph, which improves the computationally efficiency of analyzing relational graphs and thereby improves the functionality of the computing devices hosting the relational graphs and running the queries and analytics.

Abstract: Collected usage information relating to interaction with content is integrated with operation of a system, such as a content management system. The usage information is collected in response to occurrences of usage events. A user may create, configure and define the usage events according to their needs. The user may also configure/define different values that are associated with the different events. For example, a user may specify: when to count a view (e.g. unique user view or each view); what period of time to collect counts; aggregation of counts (e.g. tenant, site, collection); a weighting for the event; a trim value; when to provide the parameter to a search index of the system such that the value is considered by the search engine of the system; whether or not a parameter is configurable by other users, and the like.

Abstract: A system and method for surfacing content relevant is described. The system computes a rank score for each item of a plurality of items that are accessible from a user of an enterprise application. The system identifies a set of relevant items from the plurality of items based on the rank score for each item of the plurality of items. The system surfaces the set of relevant items within a context of a communication application of the user without receiving, from the user, a query for any of the items in the set of relevant items.

Abstract: Controlling access to nodes in a relational graph at query time by using an approximate membership query (AMQ) filter and ordered queries based on historic grants or denials of access according to security context enables a more efficient querying of the relational graph while preserving access controls. Security contexts that grant or deny access to a node are stored in an associated AMQ filter and are queried according to the subject's security context in an order based on the frequency at which the security contexts have previously granted or denied access to nodes in the relational graph.

Abstract: Controlling access to nodes in a relational graph at query time by using an approximate membership query (AMQ) filter and ordered queries based on historic grants or denials of access according to security context enables a more efficient querying of the relational graph while preserving access controls. Security contexts that grant or deny access to a node are stored in an associated AMQ filter and are queried according to the subject's security context in an order based on the frequency at which the security contexts have previously granted or denied access to nodes in the relational graph.

Abstract: Approximate Membership Query (AMQ) Filters are used in conjunction with graph queries to a relational graph to provide change monitoring that span views associated with the queries. Each node from the relational graph spanned by a graph query and the index structure for the view are added as members to an AMQ filter. When a change is made to the relational graph, the changed nodes are queried against the AMQ filter. When a changed node is noted as a candidate member of the AMQ filter, the graph query may be rerun to update the view associated with the query. Otherwise, the graph query is not rerun, thus saving computing resources and improving the systems hosting and querying the relational graph.

Abstract: Reductions in latencies and improvements in computational efficiency when analyzing data stored in a relational graph by integrating analytical capabilities into graph queries. Instead of a user having to run a graph query and then perform analytics on the resulting subgraph via separate requests, the user is enabled to run analytics at the time the graph query is run via a single request to the database maintaining the relationship graph, which improves the computationally efficiency of analyzing relational graphs and thereby improves the functionality of the computing devices hosting the relational graphs and running the queries and analytics.

Abstract: Collected usage information relating to interaction with content is integrated with operation of a system, such as a content management system. The usage information is collected in response to occurrences of usage events. A user may create, configure and define the usage events according to their needs. The user may also configure/define different values that are associated with the different events. For example, a user may specify: when to count a view (e.g. unique user view or each view); what period of time to collect counts; aggregation of counts (e.g. tenant, site, collection); a weighting for the event; a trim value; when to provide the parameter to a search index of the system such that the value is considered by the search engine of the system; whether or not a parameter is configurable by other users, and the like.

Abstract: Reductions in latencies and improvements in computational efficiency when analyzing data stored in a relational graph by integrating analytical capabilities into graph queries. Instead of a user having to run a graph query and then perform analytics on the resulting subgraph via separate requests, the user is enabled to run analytics at the time the graph query is run via a single request to the database maintaining the relationship graph, which improves the computationally efficiency of analyzing relational graphs and thereby improves the functionality of the computing devices hosting the relational graphs and running the queries and analytics.

Abstract: Systems, methods, and computer readable devices embodying instructions are provided herein for reducing latencies and/or improving computational efficiency when traversing data stored in a relational graph by caching subgraphs and enabling the utilization thereof. More specifically, after a user performs a graph query, the resulting subgraphs of the graph query are cached in a reusable form. Subsequent graph queries are able to identify cached subgraphs based on the graph query. Further, the subsequent graph query is operable to integrate the cached subgraphs as part of the result of subsequent graph query, which may include a portion or the entire result of the subsequent graph query being composed from cached subgraphs, thereby improving the computational efficiency and performance of querying relational graphs, reducing the query execution cost required to traverse the relational graphs, and improving the functionality of the computing devices hosting the relational graphs and running the queries.

Abstract: Collected usage information relating to interaction with content is integrated with operation of a system, such as a content management system. The usage information is collected in response to occurrences of usage events. A user may create, configure and define the usage events according to their needs. The user may also configure/define different values that are associated with the different events. For example, a user may specify: when to count a view (e.g. unique user view or each view); what period of time to collect counts; aggregation of counts (e.g. tenant, site, collection); a weighting for the event; a trim value; when to provide the parameter to a search index of the system such that the value is considered by the search engine of the system; whether or not a parameter is configurable by other users, and the like.

Abstract: Traversing data stored in a relational graph by utilization of probabilistic characteristics associated with the graph nodes is disclosed. When a user submits a request with a graph query, an initial node associated with the graph query is identified. Further, the edge type associated the node is extracted from the graph query. When traversing the graph by following relevant edges from the initial node to new nodes, each new node is queried with the extracted edge type. If the query for the node is negative, then the edges for the particular node are not enumerated. However, if the query for the node is positive, then the edges for the particular node are enumerated for expanding the subgraph. This process continues until the subgraph is expanded to include all relevant nodes. Thus, the computational efficiency is improved by reducing the number of edges that must be traversed when performing graph queries.

Abstract: Controlling access to nodes in a relational graph at query time by using an approximate membership query (AMQ) filter and ordered queries based on historic grants or denials of access according to security context enables a more efficient querying of the relational graph while preserving access controls. Security contexts that grant or deny access to a node are stored in an associated AMQ filter and are queried according to the subject's security context in an order based on the frequency at which the security contexts have previously granted or denied access to nodes in the relational graph.

Abstract: Controlling access to nodes in a relational graph at query time by using an approximate membership query (AMQ) filter and ordered queries based on historic grants or denials of access according to security context enables a more efficient querying of the relational graph while preserving access controls. Security contexts that grant or deny access to a node are stored in an associated AMQ filter and are queried according to the subject's security context in an order based on the frequency at which the security contexts have previously granted or denied access to nodes in the relational graph.

Abstract: Search results returned by ad hoc queries integrate search results with usage analytics calculated from collected usage information for a system, such as a content management system. In response to receiving a query, the query is submitted to the system to obtain search results. Information from the usage analytics is integrated with the obtained search results. A search index of the system may include all/portion of the usage analytics such that the obtained results from the system may already be integrated with the usage analytics. The system is configurable such that users of the system may configure the usage events that are used to collect the usage information. The users may also configure parameters relating to the usage events and when/how the collected information is stored. The usage analytics may also be used to filter, collapse and boost different items in the search results.

Abstract: Controlling access to nodes in a relational graph at query time by using an approximate membership query (AMQ) filter and ordered queries based on historic grants or denials of access according to security context enables a more efficient querying of the relational graph while preserving access controls. Security contexts that grant or deny access to a node are stored in an associated AMQ filter and are queried according to the subject's security context in an order based on the frequency at which the security contexts have previously granted or denied access to nodes in the relational graph.