System and method for generating analytical insights utilizing a semantic knowledge graph

1. A method for providing visual data for user interfaces based on a knowledge graph, comprising:

identifying at least one second node with respect to a first node based on a plurality of connections between nodes of a knowledge graph, wherein the knowledge graph includes the first node and the at least one second node, wherein the first node represents a dimension of interest;

selecting at least one third node from among the at least one second node, each of the at least one third node having a respective value, wherein selecting the at least one third node further comprises determining a correlation between the first node and each of the at least one second node, wherein each of the at least one third node is one of the at least one second node for which the determined correlation is above a threshold;

determining a new value for a dimension of each of the at least one third node based on a target value, wherein the new value for each third node is determined such that a correlation of the third node to the first node is maintained while achieving the target value; and

generating visual data for an action item user interface based on the determined new value for each of the at least one third node; and wherein

determining the new value for each third node further comprises:

selecting a first data point from a time series including a plurality of data points, the first data point corresponding to the third node;

selecting a second data point from the time series, wherein the second data point is selected from a point in time which is different from a point in time of the first data point; and

determining the new value based on the knowledge graph, the third node, and the second data point.

2. The method of claim 1 , further comprising:

determining the target value, wherein determining the target value further comprises performing a regression analysis to produce a regression function, wherein the target value is a value of the regression function.

3. The method of claim 1 , wherein the knowledge graph further includes a plurality of dimension nodes, wherein identifying the at least one second node further comprises:

generating a rank value for each of the plurality of dimension nodes; and

identifying the second node from among the plurality of dimension nodes such that the second node has a rank value above a threshold.

4. The method of claim 3 , wherein the rank value for each of the plurality of dimension nodes is generated based on at least one of: a number of edges between the dimension node and a corresponding user node, and a weight assigned to an edge between the dimension node and a corresponding user node.

5. The method of claim 3 , wherein determining the correlation between the first node and one of the at least one second node further comprises:

performing a regression analysis between the first node and the second node in order to determine an expected value, wherein the correlation is determined based on the expected value.

6. The method of claim 5 , wherein the correlation between the first node and one of the at least one second node is determined based further on a covariance and a standard deviation calculated for the first node and the second node.

7. The method of claim 1 , wherein determining the new value for each third node further comprises:

identifying at least one impact node in the knowledge graph, wherein each impact node is determined to likely affect the first data point and the second data point, wherein the new value is determined based further on the identified at least one impact node.

8. The method of claim 7 , wherein identifying the at least one impact node further comprises:

generating a confidence score for each of a plurality of nodes of the knowledge graph, wherein each of the at least one impact node has a confidence score above a threshold.

9. A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:

identifying at least one second node with respect to a first node based on a plurality of connections between nodes of a knowledge graph, wherein the knowledge graph includes the first node and the at least one second node, wherein the first node represents a dimension of interest;

selecting at least one third node from among the at least one second node, each of the at least one third node having a respective value, wherein selecting the at least one third node further comprises determining a correlation between the first node and each of the at least one second node, wherein each of the at least one third node is one of the at least one second node for which the determined correlation is above a threshold;

determining a new value for a dimension of each of the at least one third node based on a target value, wherein the new value for each third node is determined such that a correlation of the third node to the first node is maintained while achieving the target value; and

generating visual data for an action item user interface based on the determined new value for each of the at least one third node; and wherein

determining the new value for each third node further comprises:

selecting a first data point from a time series including a plurality of data points, the first data point corresponding to the third node;

selecting a second data point from the time series, wherein the second data point is selected from a point in time which is different from a point in time of the first data point; and

determining the new value based on the knowledge graph, the third node, and the second data point.

10. A system for providing visual data for user interfaces based on a knowledge graph, comprising:

a processing circuitry; and

a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:

identify at least one second node with respect to a first node based on a plurality of connections between nodes of a knowledge graph, wherein the knowledge graph includes the first node and the at least one second node, wherein the first node represents a dimension of interest;

select at least one third node from among the at least one second node, each of the at least one third node having a respective value, wherein selecting the at least one third node further comprises determining a correlation between the first node and each of the at least one second node, wherein each of the at least one third node is one of the at least one second node for which the determined correlation is above a threshold;

determine a new value for a dimension of each of the at least one third node based on a target value, wherein the new value for each third node is determined such that a correlation of the third node to the first node is maintained while achieving the target value;

generate visual data for an action item user interface based on the determined new value for each of the at least one third node;

select a first data point from a time series including a plurality of data points, the first data point corresponding to the third node;

select a second data point from the time series, wherein the second data point is selected from a point in time which is different from a point in time of the first data point; and

determine the new value based on the knowledge graph, the third node, and the second data point.

11. The system of claim 10 , wherein the system is further configured to:

determine the target value, wherein determining the target value further comprises performing a regression analysis to produce a regression function, wherein the target value is a value of the regression function.

12. The system of claim 10 , wherein the knowledge graph further includes a plurality of dimension nodes, wherein the system is further configured to:

generate a rank value for each of the plurality of dimension nodes; and

identify the second node from among the plurality of dimension nodes such that the second node has a rank value above a threshold.

13. The system of claim 12 , wherein the rank value for each of the plurality of dimension nodes is generated based on at least one of: a number of edges between the dimension node and a corresponding user node, and a weight assigned to an edge between the dimension node and a corresponding user node.

14. The system of claim 12 , wherein the system is further configured to:

perform a regression analysis between the first node and the second node in order to determine an expected value, wherein the correlation is determined based on the expected value.

15. The system of claim 14 , wherein the correlation between the first node and one of the at least one second node is determined based further on a covariance and a standard deviation calculated for the first node and the second node.

16. The system of claim 10 , wherein the system is further configured to:

identify at least one impact node in the knowledge graph, wherein each impact node is determined to likely affect the first data point and the second data point, wherein the new value is determined based further on the identified at least one impact node.

17. The system of claim 16 , wherein the system is further configured to:

generate a confidence score for each of a plurality of nodes of the knowledge graph, wherein each of the at least one impact node has a confidence score above a threshold.