Microsoft Technology Licensing, LLC

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Amund Kronen Johansen

Jan-Ove Almli Karlberg

Tor Kreutzer

Steffen Viken Valvåg

Peter Dahle Heen

Åge Andre Kvalnes

Jon Meling

Krister Mikalsen

Adrian Fanaru

Darrin N Hatakeda

John D Fan

Age Kvalnes

Steffen Viken Valvag

Kristian Elsebø

Gunnar Ljosdahl Rasmussen

Vegar Skjaerven Wang

Rune Devik

Magnus Mortensen

Daniele Vettorel

Ashok Kuppusamy

Scott M Stiles

Michael Francis Palermiti, Ii

Susan Marie Grimshaw

Amy Harilal Rambhia

Dag Brattli

Åge Kvalnes

Examples of the present disclosure describe systems and methods for ontology-based graph query optimization. In an example, ontology data relating to a graph or isolated collection may be collected. The ontology data may comprise uniqueness and topology information and may be used to reformulate a query in order to yield a query that is more performant than the original query when retrieving target information from a graph. In an example, reformulating a query may comprise reordering one or more parameters of the query relating to resources, relationships, and/or properties based on uniqueness information. In another example, the query may be reformulated by modifying the resource type to which the query is anchored based on the topology information. The reformulated query may then be executed to identify target information in the isolated collection, thereby identifying the same target information as the original query, but in a manner that is more performant.

Ontology-based graph query optimization

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.

Access controlled graph query spanning

In non-limiting examples of the present disclosure, systems, methods and devices for recommending content are presented. An electronic communication between a plurality of members of a common entity may be received. A link to web content in the electronic communication may be identified. A number of instances that the link has been electronically shared with members of the common entity may be determined. A determination may be made as to whether a content propagation threshold has been exceeded based on electronic sharing of the link amongst members of the common entity. The link may be provided for propagation upon determining that the content propagation threshold has been exceeded.

Crowdsourced content sharing

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.

Preemptively surfacing relevant content within email

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.

Query-time analytics on graph queries spanning subgraphs

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.

Extensibility model for usage analytics used with a system

In non-limiting examples of the present disclosure, systems, methods and devices for recommending a shared connection are presented. A set of shared connections between a first application user and a second application user may be identified. A determination may be made that a communication value between the first and second application users is below a recommendation surfacing threshold. A communication value between each application user of the set of shared connections and the first user may be calculated. A communication value between each application user of the set of shared connections and the second user may be calculated. One or both of the calculated communication values may be utilized to rank the shared connections based on importance to the first user, importance to the second user, and/or importance to the first user and the second user. One or more top ranked candidate user profiles may be promoted on a graphical user interface.

Recommendation of shared connections for display

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.

Change monitoring spanning graph queries

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