BACKGROUND

Embodiments of the invention relate to generating highlight queries (e.g., for analysts) and executing those highlight queries. The highlight queries may be generated per a specific user or a group.

A graph has nodes and edges. A graph may also be referred to as a chart. The nodes represent entities (e.g., people, groups, accounts, objects, etc.) and edges represent connections (e.g., ownership, relative, employee, etc.) between nodes. The connections may be described as links or relationships between entities.

Analysts explore graphs with entities and connections. When looking at any entity, they often want to explore the local network/graph around that entity. Highlight queries may be described as a tool that helps with this exploration.

A highlight query takes a starting entity and runs a query over the graphs with that entity as a seed for the query. The highlight query is a graphical query that may be represented as a sub-graph with conditional statements on the elements (where elements include both nodes and edges) within the sub-graph.

SUMMARY

In accordance with certain embodiments, a computer-implemented method is provided for generating highlight queries. The computer-implemented method comprises operations. Data is analyzed using one or more schemas to identify patterns and frequencies of the patterns in the data, wherein each of the patterns comprises one or more node types and one or more edge types. The patterns and the frequencies of the patterns are stored in a global patterns structure. A graph is analyzed to identify one or more of the patterns in the global patterns structure that are present in the graph and in which frequencies. The identified one or more of the patterns in the global patterns structure that are present in the graph are added to a per-user patterns structure with the frequencies of those patterns. The patterns in the per-user patterns structure are scored. A subset of the patterns in the per-user patterns structure that have scores above a threshold are selected as highlight queries. The selected subset of the patterns are executed as highlight queries to generate results, and the results are returned.

In accordance with other embodiments, a computer program product is provided for generating highlight queries. The computer program product comprises a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to perform operations. Data is analyzed using one or more schemas to identify patterns and frequencies of the patterns in the data, wherein each of the patterns comprises one or more node types and one or more edge types. The patterns and the frequencies of the patterns are stored in a global patterns structure. A graph is analyzed to identify one or more of the patterns in the global patterns structure that are present in the graph and in which frequencies. The identified one or more of the patterns in the global patterns structure that are present in the graph are added to a per-user patterns structure with the frequencies of those patterns. The patterns in the per-user patterns structure are scored. A subset of the patterns in the per-user patterns structure that have scores above a threshold are selected as highlight queries. The selected subset of the patterns are executed as highlight queries to generate results, and the results are returned.

In accordance with yet other embodiments, a computer system is provided for generating highlight queries. The computer system comprises one or more processors, one or more computer-readable memories and one or more computer-readable, tangible storage devices; and program instructions, stored on at least one of the one or more computer-readable, tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to perform operations. Data is analyzed using one or more schemas to identify patterns and frequencies of the patterns in the data, wherein each of the patterns comprises one or more node types and one or more edge types. The patterns and the frequencies of the patterns are stored in a global patterns structure. A graph is analyzed to identify one or more of the patterns in the global patterns structure that are present in the graph and in which frequencies. The identified one or more of the patterns in the global patterns structure that are present in the graph are added to a per-user patterns structure with the frequencies of those patterns. The patterns in the per-user patterns structure are scored. A subset of the patterns in the per-user patterns structure that have scores above a threshold are selected as highlight queries. The selected subset of the patterns are executed as highlight queries to generate results, and the results are returned.

