Abstract: Examples of the present disclosure describe systems and methods for preventing illicit data transfer and storage. In aspects, a computing platform may receive a data request from a caller system, device, or service. The computing platform may identify data items/properties associated with the data request and retrieve one or more rules relevant to the caller and/or caller location. The retrieved rule(s) may be used to evaluate the data item(s) such that data items, data item content, and/or data item properties that are prohibited by the retrieved rule(s) from being manipulated (e.g., accessed, transferred, stored) are removed from the identified data item(s). Based on the evaluation of the identified data item(s), one or more relevant status codes may be set. The computing platform may then manipulate the identified data item(s) in accordance with the data request and provide a processing response to the caller.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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: A method comprising: pre-generating insights for each of a plurality of user-content combinations, each user-content combination comprising a different respective combination one of a plurality of first users and one of a plurality of first pieces of content, wherein each insight specifies a relationship type and other content having that relationship with the respective first content; subsequently receiving a query seeking insights on a target one of the first users and first pieces of content; based on the query, identifying the respective set of insights for that user-content combination; subsequently pruning away one or more insights which specify no related pieces of content to which the target user is permitted access; and outputting at least one of the remaining subset of insights to the target user.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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: 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: 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.