Computer network controlled data orchestration system and method for data aggregation, normalization, for presentation, analysis and action/decision making

1. A computer automated system comprising:

a processing unit;

a memory element coupled to the processing unit;

an embedded data collection stack;

wherein the computer automated system is configured to, in real-time:

automatically aggregate device behavior data over a network via the embedded data collection stack from a plurality of device classes, wherein the plurality of device classes comprise a single or plurality of proxy devices, legacy protocols, devices, applications, machines, and sensors across locations;

abstract the plurality of device classes to generate an abstract device model;

automatically normalize the aggregated device behavior data from the plurality of device classes;

wherein automatic normalization comprises normalization at a collection point or at an edge from an associated or recognized proxy, and normalization at an associated server from an unassociated or unrecognized proxy;

automatically canonicalize the normalized and aggregated device behavior data, wherein canonicalization comprises adding meta-data and derived data to aggregated device behavior data from each of the plurality of device classes;

automatically analyze the canonicalize device behavior data based on a correlated event or events, and a correlated condition or conditions, across the plurality of device classes;

and

based on the analyzed device behavior data, automatically combine the normalized and aggregated device behavior data, and display the combined normalized and aggregated device behavior data in a display compatible format.

2. The computer automated system of claim 1 wherein the abstracting of the plurality of device classes comprises abstracting a plurality of sensors, and connected devices comprising medical CT scanners, medical Mills, printers, and UPS systems.

3. The computer automated system of claim 1 wherein, in the normalizing of said device behavior data from the plurality of device classes, the computer automated system is further caused to:

via a plurality of extensible connectors, extract a log file data, or a proprietary or standard protocol comprising SNMP data and HTTP data from each of a plurality of connected devices.

4. The computer automated system of claim 3 wherein the system is further configured to:

map device behavior data model parameters to variables in device models via a previously created plurality of templates; and

wherein the plurality of extensible connectors are further caused to extract the device behavior data model parameters mapped to the variables in the device models.

5. The computer automated system of claim 4 wherein the computer automated system is configured to:

store each device behavior data model parameter as a name value pair in a no-schema database.

6. The computer automated system of claim 1 wherein:

said adding meta-data and derived data to the aggregated device behavior data from each of the plurality of device classes in canonicalization of the normalized and aggregated device behavior data comprises adding the meta-data and the derived data via a single or plurality of workflows.

7. The computer automated system of claim 1 wherein the computer automated system is configured to:

display the device behavior data in a plurality of different forms wherein the plurality of different forms comprises at least one of a graph, a chart, and a table.

8. The computer automated system of claim 7 wherein the graph, chart and table are configured to show the device behavior data relevant to each device class.

9. The computer automated system of claim 1 wherein the analysis based on the correlated event or events, and the correlated condition or conditions, across the plurality of device classes, comprises analysis via a single or plurality of Device Internet of Things (IOT) stacks and gateways; and wherein based on the analysis, the computer automated system is configured to implement a single or plurality of decisions, in real-time, in a return path or closed loop, on a plurality of machines, sensors, devices and applications.

10. The computer automated system of claim 1 further comprising a mobile device.

11. In a computer automated system comprising a processing unit coupled to a memory element, an embedded data collection stack, and having instructions encoded thereon, a method comprising, in real-time:

automatically aggregating device behavior data over a network via the embedded data collection stack from a plurality of device classes, wherein the plurality of device classes comprise a single or plurality of proxy devices, legacy protocols, devices, applications, machines, sensors and things across locations;

abstracting the plurality of device classes to generate an abstract device model;

automatically normalizing the aggregated device behavior data from the plurality of device classes;

wherein automatic normalization comprises normalization at a collection point or at an edge from an associated or recognized proxy, and normalization at an associated server from an unassociated or unrecognized proxy;

automatically canonicalizing the normalized and aggregated device behavior data, wherein canonicalization comprises adding meta-data and derived data to aggregated device behavior data from each of the plurality of device classes;

automatically analyzing the canonicalized device behavior data based on a correlated event or events, a correlated condition or conditions, and a correlated trend or trends across the plurality of device classes;

and

based on the analyzed device behavior data, automatically combining the normalized and aggregated device behavior data, and displaying the combined normalized and aggregated device behavior data in a display compatible format.

12. The method of claim 11 wherein the abstracting of the plurality of classes of the plurality of device classes comprises abstracting a plurality of sensors, and connected devices comprising medical CT scanners, medical Mills, printers, and UPS systems.

13. The method of claim 11 wherein the normalizing of aggregated device behavior data further comprises:

extracting a log file data, or a proprietary or standard protocol comprising at least one of an SNMP data and HTTP data from each of a plurality of connected devices via a plurality of extensible connectors.

14. The method of claim 13 further comprising:

mapping device behaivor data model parameters to variables in device class models via a plurality of pre-created templates; and

extracting the device behavior data model parameters mapped to the variables in the device class models via the plurality of extensible connectors.

15. The method of claim 14 further comprising:

storing each device behaivor data model parameter as a name value pair in a no-schema database.

16. The method of claim 11 wherein:

said adding meta-data and derived data to the aggregated device behavior data from each of the plurality of device classes in canonicalization of the normalized and aggregated device behavior data comprises adding the meta-data and the derived data via a single or plurality of workflows.

17. The method of claim 11 further comprising:

displaying device behavior data in a plurality of forms wherein the said plurality of forms comprises at least one of a graph, a chart, and a table.

18. The method of claim 17 wherein the said graph, chart and table are configured to show behavior data relevant to each device class.

19. The method of claim 11 wherein the analyzing of device behavior data based on the correlated event or events, and the correlated condition or conditions, across the plurality of device classes comprises:

analyzing via a single or plurality of Device Internet of Things (IOT) stacks and gateways; and

based on the analyzing via the single or plurality of Device Internet of Things (IOT) stacks and gateways, implementing a single or plurality of actions, in real-time, in a return path or closed loop, on a single or plurality of machines, sensors, devices or applications.

20. In a computer automated system comprising a processing unit coupled to a memory element and having instructions encoded thereon, a method comprising, automatically in real-time:

via an embedded data collection stack comprised in the computer automated system, aggregating device behavior data over a network from a plurality of device classes, wherein the plurality of device classes comprise a single or plurality of proxy devices, legacy protocols, devices, applications, machines, sensors and things across locations;

normalizing the aggregated device behavior data from the plurality of device classes, wherein the normalizing comprises:

generating an abstract device model for the plurality of device classes;

extracting device model parameters via the generated abstract device model;

polling the extracted device model parameters for each device class type from the plurality of device classes;

wherein the normalizing of the aggregated device behavior data from the plurality of device classes further comprises normalization at a collection point or at an edge from an associated or recognized proxy, and normalization at an associated server from an unassociated or unrecognized proxy;

canonicalizing the normalized and aggregated device behavior data which comprises adding meta-data and derived data to the extracted device model parameters;

analyzing the canonicalized device behavior data based on a correlated event or events, and a correlated condition or conditions, across the plurality of device classes; and

based on the analyzing:

implementing a single or plurality of actions, in real-time, in a return path or closed loop, on a single or plurality of machines, sensors, devices or applications;

and

combining the normalized and aggregated device behavior data, and displaying the combined normalized and aggregated device behavior data in a display compatible format.

21. A mobile wireless communication device comprising:

a processing unit;

a memory element coupled to the processing unit;

an embedded data collection stack;

encoded instructions that configure the mobile device to, automatically in real-time:

aggregate device behavior data over a network via the embedded data collection stack from a plurality of device classes, wherein the plurality of device classes comprise a single or plurality of proxy devices, legacy protocols, devices, applications, machines, sensors and things across locations;

abstract the plurality of device classes to generate an abstract device model;

normalize the aggregated device behavior data from the plurality of device classes;

wherein automatic normalization comprises normalization at a collection point or at an edge from an associated or recognized proxy, and normalization at an associated server from an unassociated or unrecognized proxy;

canonicalize the normalized and aggregated device behavior data, wherein canonicalization comprises adding meta-data and derived data to aggregated device behavior data from each of the plurality of device classes;

analyze the canonicalized device behavior data based on a correlated event or events, a correlated condition or conditions, and a correlated trend or trends across the plurality of device classes;

and

based on the analyzed device behavior data, combine the normalized and aggregated device behavior data, and display the combined normalized and aggregated device behavior data in a display compatible format.

22. The mobile wireless communication device of claim 21 wherein the device is further configured to:

based on the analyzed device behavior data, trigger a single or plurality of actions, in real-time, in a return path or closed loop, on the single or plurality of machines, sensors, devices or applications.