Multi-dimensional clustering and correlation with interactive user interface design

1. A method comprising:

receiving, by an application or cloud service executing on one or more computing devices, a request to cluster a set of records that identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns;

responsive to receiving the request to cluster the set of records, generating, by the application or cloud service, a plurality of clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records;

generating, for each respective cluster of the plurality of clusters, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster;

presenting, by the application or cloud service through a user interface, at least one cluster in the plurality of clusters and the analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster;

mapping the analytic result to one or more recommended actions to perform; and

executing, by the application or cloud service, at least one recommended action.

2. The method of claim 1 , further comprising determining that a subset of one or more clusters of the plurality of clusters are outliers based at least in part on a distribution of records within the plurality of records and distances between centroids of different clusters in the plurality of clusters.

3. The method of claim 1 , further comprising determining a range of values in the first set of one or more dimensions that are outliers; and presenting the range of values through the user interface.

4. The method of claim 1 , wherein the request identifies a first dimension and a second dimension; where presenting the at least one cluster comprises presenting visualizations representing different clusters on a chart with a first axis representing different values of the first dimension and a second axis representing different values of the second dimension.

5. The method of claim 4 , wherein the visualizations representing different clusters include at least a first visualization that is displayed in a first color based on a first value or range of values of a third dimension for records in a first cluster and a second visualization that is displayed in a second color based on a second value or range of values for the third dimension for records in a second cluster.

6. The method of claim 4 , wherein the visualizations have different sizes determined as a function of how many records are in each cluster; wherein a first visualization for a first cluster has a greater size than a second visualization for a second cluster that have fewer records than the first cluster.

7. The method of claim 1 , wherein the at least one recommended action includes at least one of adjusting a system configuration or tuning a database query to remediate a performance issue associated with an outlier cluster.

8. The method of claim 1 , wherein presenting the at least one cluster comprises displaying, for a particular cluster, a first set of information extracted from at least a first log record generated by a system in a first domain; responsive to receiving input from a user interacting with a visualization representing the particular cluster, presenting a second set of information extracted from at least a second log record generated by a system in a second domain.

9. The method of claim 1 , wherein presenting the at least one cluster comprises presenting at least one of a scatter plot or a heat map that highlights outlier clusters indicative of performance degradation.

10. A non-transitory computer-readable medium storing instructions which, when executed by one or more hardware processors, cause:

receiving, by an application or cloud service executing on one or more computing devices, a request to cluster a set of records that identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns;

responsive to receiving the request to cluster the set of records, generating, by the application or cloud service, a plurality of clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records;

generating, for each respective cluster of the plurality of clusters, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster;

presenting, by the application or cloud service through a user interface, at least one cluster in the plurality of clusters and the analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster;

mapping the analytic result to one or more recommended actions to perform; and

executing, by the application or cloud service, at least one recommended action.

11. The non-transitory computer-readable medium of claim 10 , wherein the instructions further cause: determining that a subset of one or more clusters of the plurality of clusters are outliers based at least in part on a distribution of records within the plurality of records and distances between centroids of different clusters in the plurality of clusters.

12. The non-transitory computer-readable medium of claim 10 , wherein the instructions further cause: determining a range of values in the first set of one or more dimensions that are outliers; and presenting the range of values through the user interface.

13. The non-transitory computer-readable medium of claim 10 , wherein the request identifies a first dimension and a second dimension; where presenting the at least one cluster comprises presenting visualizations representing different clusters on a chart with a first axis representing different values of the first dimension and a second axis representing different values of the second dimension.

14. The non-transitory computer-readable medium of claim 13 , wherein the visualizations representing different clusters include at least a first visualization that is displayed in a first color based on a first value or range of values of a third dimension for records in a first cluster and a second visualization that is displayed in a second color based on a second value or range of values for the third dimension for records in a second cluster.

15. The non-transitory computer-readable medium of claim 13 , wherein the visualizations have different sizes determined as a function of how many records are in each cluster; wherein a first visualization for a first cluster has a greater size than a second visualization for a second cluster that have fewer records than the first cluster.

16. The non-transitory computer-readable medium of claim 10 , wherein the at least one recommended action includes at least one of adjusting a system configuration or tuning a database query to remediate a performance issue associated with an outlier cluster.

17. The non-transitory computer-readable medium of claim 10 , wherein presenting the at least one cluster comprises displaying, for a particular cluster, a first set of information extracted from at least a first log record generated by a system in a first domain; responsive to receiving input from a user interacting with a visualization representing the particular cluster, presenting a second set of information extracted from at least a second log record generated by a system in a second domain.

18. A system comprising:

one or more hardware processors;

one or more non-transitory computer-readable media storing instructions which, when executed by the one or more hardware processors, cause:

receiving, by an application or cloud service executing on one or more computing devices, a request to cluster a set of records that identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns;

responsive to receiving the request to cluster the set of records, generating, by the application or cloud service, a plurality of clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records;

generating, for each respective cluster of the plurality of clusters, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster;

presenting, by the application or cloud service through a user interface, at least one cluster in the plurality of clusters and the analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster;

mapping the analytic result to one or more recommended actions to perform; and

executing, by the application or cloud service, at least one recommended action.

19. The system of claim 18 , wherein the instructions further cause: determining that a subset of one or more clusters of the plurality of clusters are outliers based at least in part on a distribution of records within the plurality of records and distances between centroids of different clusters in the plurality of clusters.

20. The system of claim 18 , wherein the instructions further cause: determining a range of values in the first set of one or more dimensions that are outliers; and presenting the range of values through the user interface.