Suggestion of views based on correlation of data

1. A computer-implemented method comprising:

identifying, using one or more processors and for each particular asset of a plurality of assets, at least one characteristic asset signal of the particular asset and at least one characteristic of non-asset-specific signals associated with the particular asset, wherein the characteristic asset signals and non-asset-specific signals each belong to a dimension based on data type;

automatically correlating, using the one or more processors, the at least one characteristic asset signal of the particular asset and the at least one characteristic of the non-asset-specific signals associated with the particular asset to create a set of correlated records comprising a correlated set of the at least one characteristic asset signal of the particular asset and the at least one characteristic of the non-asset-specific signals;

analyzing, using the one or more processors, the set of correlated records to identify a set of anomaly-detection rules, each anomaly-detection rule identifying potential anomalies for assets matching a combination of the at least one characteristic asset signal of the particular asset and the at least one characteristic of non-asset-specific signals;

identifying, in at least a subset of the assets, an indication of assets associated with a potential anomaly identified using the set of anomaly-detection rules;

determining a context of the potential anomaly, the context including a machine-generated insight determined from: i) the at least one characteristic asset signal of the particular asset, and ii) related business and external data associated with the particular asset;

automatically identifying, by one or more processors, a suggested view based on the potential anomaly, the context, and the anomaly-detection rules, wherein identifying the suggested view comprises:

determining, for a plurality of potential views, the dimensions to be presented, wherein the dimensions belong to the signals from the set of correlated records;

determining, for each specific dimension of the dimensions, an amount of influence the specific dimension has on the presentation of the potential anomaly based on the set of correlated records, and a statistical correlation of the at least one characteristic of the particular asset and the potential anomaly; and

identifying a suggested view from the plurality of potential views by selecting a potential view which presents the dimensions that satisfy a degree of statistical correlation with the potential anomaly; and

providing the suggested view for presentation in a user interface, wherein the suggested view provides drill-down capabilities to display additional information for each presented dimension.

2. The computer-implemented method of claim 1 , wherein the analyzing the set of correlated records is based on previously determined anomalies and machine learning to identify relevant or determinative sets of characteristics.

3. The computer-implemented method of claim 1 , wherein identifying the suggested view includes identifying a presentation of a view type.

4. The computer-implemented method of claim 1 , wherein identifying the suggested view includes identifying particular sets of assets and particular characteristics.

5. The computer-implemented method of claim 1 , wherein the suggested view is a product of plural transformations.

6. The computer-implemented method of claim 1 , wherein the suggested view includes color coding of anomalies and different types of data.

7. The computer-implemented method of claim 1 , wherein the suggested view provides hover capabilities.

8. The computer-implemented method of claim 1 , wherein the suggested view is 1, 2-, 3-, or 4-dimensional.

9. The computer-implemented method of claim 1 , wherein the suggested view includes multiple charts, presented side-by-side.

10. The computer-implemented method of claim 1 , wherein the suggested view includes providing filtering functions by time, region, and type of asset.

11. The computer-implemented method of claim 1 , wherein the suggested view provides clustering capabilities based on view or zoom level.

12. The computer-implemented method of claim 1 , wherein the suggested view provides stackable layers.

13. The computer-implemented method of claim 1 , wherein an asset is an Internet of things (IoT) device.

14. A system comprising:

memory storing information about assets; and

a server performing, using one or more processors, operations comprising:

identifying, using the one or more processors and for each particular asset of a plurality of assets, at least one characteristic asset signal of the particular asset and at least one characteristic of non-asset-specific signals associated with the particular asset, wherein the characteristic asset signals and non-asset-specific signals each belong to a dimension based on data type;

automatically correlating, using the one or more processors, the at least one characteristic asset signal of the particular asset and the at least one characteristic of the non-asset-specific signals associated with the particular asset to create a set of correlated records comprising a correlated set of the at least one characteristic asset signal of the particular asset and the at least one characteristic of the non-asset-specific signals;

analyzing, using the one or more processors, the set of correlated records to identify a set of anomaly-detection rules, each anomaly-detection rule identifying potential anomalies for assets matching a combination of the at least one characteristic asset signal of the particular asset and the at least one characteristic of non-asset-specific signals;

identifying, in at least a subset of the assets, an indication of assets associated with a potential anomaly identified using the set of anomaly-detection rules;

determining a context of the potential anomaly, the context including a machine-generated insight determined from: i) the at least one characteristic asset signal of the particular asset, and ii) related business and external data associated with the particular asset;

automatically identifying, by the one or more processors, a suggested view based on the potential anomaly, the context, and the anomaly-detection rules, wherein identifying the suggested view comprises:

determining, for a plurality of potential views, the dimensions to be presented, wherein the dimensions are associated with the set of correlated records;

determining, for each specific dimension, an amount of influence the specific dimension has on the presentation of the potential anomaly based on the set of correlated records, and a statistical correlation of the at least one characteristic of the particular asset and the potential anomaly; and

identifying a suggested view from the plurality of potential views by selecting a potential view which presents the dimensions that satisfy a degree of statistical correlation with the potential anomaly; and

analyzing, using the one or more processors, the set of correlated records to identify a set of anomaly-detection rules, each anomaly-detection rule identifying potential anomalies for assets matching a combination of the at least one characteristic asset signal of the particular asset and the at least one characteristic of non-asset-specific signals;

identifying, in at least a subset of the assets, an indication of assets associated with a potential anomaly identified using the set of anomaly-detection rules;

determining a context of the potential anomaly, the context including a machine-generated insight determined from: i) the at least one characteristic asset signal of the particular asset, and ii) related business and external data associated with the particular asset;

automatically identifying, by one or more processors, a suggested view based on the potential anomaly, the context, and the anomaly-detection rules, wherein identifying the suggested view comprises:

determining, for a plurality of potential views, the dimensions to be presented, wherein the dimensions belong to the signals from the set of correlated records;

determining, for each specific dimension of the dimensions, an amount of influence the specific dimension has on the presentation of the potential anomaly based on the set of correlated records, and a statistical correlation of the at least one characteristic of the particular asset and the potential anomaly; and

providing the suggested view for presentation in a user interface, wherein the suggested view provides drill-down capabilities to display additional information for each presented dimension.

15. The system of claim 14 , wherein the analyzing the set of correlated records is based on previously determined anomalies and machine learning to identify relevant or determinative sets of characteristics.

16. A non-transitory computer-readable media encoded with a computer program, the computer program comprising instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

identifying, using one or more processors and for each particular asset of a plurality of assets, at least one characteristic asset signal of the particular asset and at least one characteristic of non-asset-specific signals associated with the particular asset wherein the characteristic asset signals and non-asset-specific signals each belong to a dimension based on data type;

automatically correlating, using the one or more processors, the at least one characteristic asset signal of the particular asset and the at least one characteristic of the non-asset-specific signals associated with the particular asset to create a set of correlated records comprising a correlated set of the at least one characteristic asset signal of the particular asset and the at least one characteristic of the non-asset-specific signals;

identifying a suggested view from the plurality of potential views by selecting a potential view which presents the dimensions that satisfy a degree of statistical correlation with the potential anomaly; and

providing the suggested view for presentation in a user interface, wherein the suggested view provides drill-down capabilities to display additional information for each presented dimension.

17. The non-transitory computer-readable media of claim 16 , wherein the analyzing the set of correlated records is based on previously determined anomalies and machine learning to identify relevant or determinative sets of characteristics.