Microsoft Technology Licensing, LLC

Steffen Viken Valvåg

Jan-Ove Almli Karlberg

Tor Kreutzer

Amund Kronen Johansen

Åge Andre Kvalnes

Dag Steinnes Eidesen

Peter Dahle Heen

Daniele Vettorel

Jon Meling

Bjørnstein Lilleby

Dag Brattli

Åge Kvalnes

Bernt Ivar Olsen-kristiansen

A computerized method filters search result content using user interaction data. Search result content, including search result entries, is presented. User interaction data is received that is indicative of a user's interactions with the presented search result content and a result filter rule is determined using the user interaction data. Filtered search result content is generated using the determined result filter rule and the generated filtered search result content, including a portion of the search result entries, is presented. In some examples, the user is prompted to accept the filtering of the search result content prior to the generation of the filtered search result content. Thus, the search result content is dynamically filtered based on the user's interactions with that content while the user reviews the content.

Dynamically filtering search results using contextual user interaction data

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

Methods and systems are disclosed for optimizing record placement in a graph by minimizing fragmentation when writing data. Issues with fragmented data within a graph database are addressed on the record level by placing data that is frequently accessed together contiguously within memory. For example, a dynamic rule set may be developed based on dynamically analyzing access patterns of the graph database, policies, system characteristics and/or other heuristics. Based on statistics regarding normal query patterns, the systems and methods may identify an optimal position for certain types of edges that are often traversed with respect to particular types of nodes.

Optimized record placement in graph database

Methods and systems are disclosed for optimizing record placement in defragmenting a graph database. Issues with fragmented data within a graph database are addressed on the record level by placing data that is frequently accessed together contiguously within memory. For example, a dynamic rule set may be developed based on dynamically analyzing access patterns of the graph database, policies, system characteristics and/or other heuristics. Based on statistics regarding normal query patterns, the systems and methods may identify an optimal position for certain types of edges that are often traversed with respect to particular types of nodes.

Optimized record placement in defragmenting graph database

Examples of the present disclosure describe systems and methods for query execution across multiple graphs. In an example, a graph or isolated collection may be split into multiple subparts, such that each subpart may store information of the isolated collection. Cross-collection reference resources may be used to reference resources that are stored by other isolated collection subparts. A breadth-first search of an isolated collection subpart may be performed in order to identify matches or potential matches in an isolated collection subpart. In an example, a potential match may comprise a cross-collection reference resource, which may reference a resource in another isolated collection subpart. Once query execution has completed in the isolated collection subpart, query execution may be paused and transferred to another isolated collection subpart that comprises a resource referenced by a cross-collection resource reference. Accordingly, query execution may resume in the subsequent isolated collection subpart.

Query execution across multiple graphs

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

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

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.

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Tromsø, Norway

Steffen Viken Valvåg