Contextual execution of automated workflows

• US 10,650,427 B2
• Filed: 09/15/2016
• Issued: 05/12/2020
• Est. Priority Date: 09/28/2007
• Status: Active Grant

• Current Assignee: Great-Circle Technologies, Inc.
• Original Assignee: Great-Circle Technologies, Inc.
• Inventors: Bursey, Brent
• Primary Examiner(s): Pond, Robert M
• Application Number:
  US15/265,966
• Publication Number:
  US 20170069009A1
• Time in Patent Office: 1,335 Days
• Field of Search: 705 26, 705 27
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/5854 using shape and object relationship
• CPC Class Codes: G06Q 10/0633 Workflow analysis
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services
• CPC Class Codes: G06Q 30/0281 Customer communication at a business location, e.g. providing product or service information, consulting
• CPC Class Codes: G06Q 30/0603 Catalogue ordering
• CPC Class Codes: G06Q 30/0621 Item configuration or customization
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 5/80 Geometric correction
• CPC Class Codes: H04L 41/0273 using web services for network management, e.g. simple object access protocol [SOAP]
• CPC Class Codes: H04L 63/0245 Filtering by information in the payload
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04W 4/70 Services for machine-to-machine communication [M2M] or machine type communication [MTC]

Abstract

An enterprise geospatial intelligence service oriented architecture (EGI-SOA) provides a consumer with one or more tailored products in response to either a dynamic request or a standing request by the consumer.

Claims

1. A method comprising the following steps:

(a) autonomously tailoring one or more tailored geospatial intelligence products by executing an ExecML job in a computing cloud in response to a consumer request for the one or more tailored geospatial intelligence products;

(b) providing the one or more tailored geospatial intelligence products to the consumer,

wherein a respective CSD server runs on each respective processor in the computing cloud, and wherein each CSD server generates a CSD file representing a contextual state for each respective processor at a given time;

(c) aggregating the CSD files for all of the processors of the computing cloud to form an aggregated list of CSD files;

(d) determining an optimal configuration of the computing resources within the computing cloud that are needed to execute the ExecML job; and

(e) executing the ExecML job using the optimal configuration, wherein step (d) is based on the aggregated list of CSD files.

2. The method of claim 1, wherein step (a) comprises the following steps:

(f) filtering the computing cloud to match one or more events in the computing cloud to a complex event pattern;

(g) extracting contexts for each event matched in step (f); and

(h) translating a workflow with the contexts into an executable process.

3. The method of claim 2, wherein step (a) further comprises the following steps:

(i) generating a first descriptive intermediate file based on a workflow to be used in forming the one or more tailored geospatial intelligence products;

(j) generating a second descriptive intermediate file based on the services associated with the workflow that will be used to form the one or more tailored geospatial intelligence products;

(k) generating a third descriptive intermediate file on how the services interact with each other; and

(l) generating a fourth descriptive intermediate file based on the support data for the workflow.

4. The method of claim 3, wherein step (a) further comprises the following step:

(m) forming an ExecML file based on the first, second, third and fourth descriptive files.

5. The method of claim 4, wherein step (a) further comprises the following step:

(n) executing the ExecML job in the computing cloud to form the one or more tailored geospatial intelligence products.

6. The method of claim 5, wherein step (a) further comprises the following step:

(o) determining which of one or more processors in the computing cloud will be used to form the one or more tailored geospatial intelligence products.

7. A method comprising the following steps:

(a) autonomously tailoring one or more tailored geospatial intelligence products by executing an ExecML job in a computing cloud in response to a consumer request for the one or more tailored geospatial intelligence products;

(b) providing the one or more tailored geospatial intelligence products to the consumer,

wherein a respective CSD server runs on each respective processor in the computing cloud, and wherein each CSD server generates a CSD file representing a contextual state for each respective processor at a given time;

(c) aggregating the CSD files for all of the processors of the computing cloud to form an aggregated list of CSD files;

(d) determining that an optimal configuration of the computing resources within the computing cloud for executing the ExecML job does not exist;

(e) dynamically re-provisioning some of the computing cloud resources to form a re-provisioned configuration of the computing resources within the computing cloud; and

(f) executing the ExecML job using the re-provisioned configuration, wherein steps (d) and (e) are based on the aggregated list of CSD files.

8. The method of claim 7, wherein step (a) comprises the following steps:

(g) filtering the computing cloud to match one or more events in the computing cloud to a complex event pattern;

(h) extracting contexts for each event matched in step (h); and

(i) translating a workflow with the contexts into an executable process.

9. The method of claim 8, wherein step (a) further comprises the following steps:

(j) generating a first descriptive intermediate file based on a workflow to be used in forming the one or more tailored geospatial intelligence products;

(k) generating a second descriptive intermediate file based on the services associated with the workflow that will be used to form the one or more tailored geospatial intelligence products;

(l) generating a third descriptive intermediate file on how the services interact with each other; and

(m) generating a fourth descriptive intermediate file based on the support data for the workflow.

10. The method of claim 9, wherein step (a) further comprises the following step:

(n) forming an ExecML file based on the first, second, third and fourth descriptive files.

11. The method of claim 10, wherein step (a) further comprises the following step:

(o) executing the ExecML job in the computing cloud to form the one or more tailored geospatial intelligence products.

12. The method of claim 11, wherein step (a) further comprises the following step:

(p) determining which of one or more processors in the computing cloud will be used to form the one or more tailored geospatial intelligence products.

13. A method comprising the following steps:

(a) autonomously tailoring one or more tailored geospatial intelligence products by executing an ExecML job in a computing cloud in response to a consumer request for the one or more tailored geospatial intelligence products;

(b) providing the one or more tailored geospatial intelligence products to the consumer,

wherein a respective CSD server runs on each respective processor in the computing cloud, and wherein each CSD server generates a CSD file representing a contextual state for each respective processor at a given time;

(c) aggregating the CSD files for all of the processors of the computing cloud to form an aggregated list of CSD files;

(d) determining that an optimal configuration of the computing resources within the computing cloud for executing the ExecML job does not exist;

(e) determining that a less optimal configuration of the computing resources within the computing cloud that can execute the ExecML; and

(f) executing the ExecML job using the less optimal configuration, wherein steps (d) and (e) are based on the aggregated list of CSD files.

14. The method of claim 13, wherein step (a) comprises the following steps:

(g) filtering the computing cloud to match one or more events in the computing cloud to a complex event pattern;

(h) extracting contexts for each event matched in step (h); and

(i) translating a workflow with the contexts into an executable process.

15. The method of claim 14, wherein step (a) further comprises the following steps:

(j) generating a first descriptive intermediate file based on a workflow to be used in forming the one or more tailored geospatial intelligence products;

(k) generating a second descriptive intermediate file based on the services associated with the workflow that will be used to form the one or more tailored geospatial intelligence products;

(l) generating a third descriptive intermediate file on how the services interact with each other; and

(m) generating a fourth descriptive intermediate file based on the support data for the workflow.

16. The method of claim 15, wherein step (a) further comprises the following step:

(n) forming an ExecML file based on the first, second, third and fourth descriptive files.

17. The method of claim 16, wherein step (a) further comprises the following step:

(o) executing the ExecML job in the computing cloud to form the one or more tailored geospatial intelligence products.

18. The method of claim 17, wherein step (a) further comprises the following step:

(p) determining which of one or more processors in the computing cloud will be used to form the one or more tailored geospatial intelligence products.

19. A method comprising the following steps:

(a) autonomously tailoring one or more tailored geospatial intelligence products by executing an ExecML job in a computing cloud in response to a consumer request for the one or more tailored geospatial intelligence products;

(b) providing the one or more tailored geospatial intelligence products to the consumer,

wherein a respective CSD server runs on each respective processor in the computing cloud, and wherein each CSD server generates a CSD file representing a contextual state for each respective processor at a given time;

(c) aggregating the CSD files for all of the processors of the computing cloud to form an aggregated list of CSD files;

(d) determining that an optimal configuration of the computing resources within the computing cloud for executing the ExecML job does not exist;

(e) determining that a less optimal configuration of the computing resources within the computing cloud that can execute the ExecML cannot fulfill all of the conditions of a service level agreement; and

(f) executing the ExecML job in the computing cloud to form the one or more tailored geospatial intelligence products.

20. The method of claim 19, wherein step (a) comprises the following steps:

(g) filtering the computing cloud to match one or more events in the computing cloud to a complex event pattern;

(h) extracting contexts for each event matched in step (f); and

(i) translating a workflow with the contexts into an executable process.

21. The method of claim 20, wherein step (a) further comprises the following steps:

(j) generating a first descriptive intermediate file based on a workflow to be used in forming the one or more tailored geospatial intelligence products;

(k) generating a second descriptive intermediate file based on the services associated with the workflow that will be used to form the one or more tailored geospatial intelligence products;

(l) generating a third descriptive intermediate file on how the services interact with each other; and

(m) generating a fourth descriptive intermediate file based on the support data for the workflow.

22. The method of claim 21, wherein step (a) further comprises the following step:

(n) forming an ExecML file based on the first, second, third and fourth descriptive files.

23. The method of claim 19, wherein step (a) further comprises the following step:

(g) determining which of one or more processors in the computing cloud will be used to form the one or more tailored geospatial intelligence products.

24. A machine readable medium having stored thereon sequences of instructions, which when executed by one or more processors, cause one or more electronic devices to perform a set of operations comprising the following steps:

(a) autonomously tailoring one or more tailored geospatial intelligence products by executing an ExecML job in a computing cloud in response to a consumer request for the one or more tailored geospatial intelligence products; and

(b) providing the one or more tailored geospatial intelligence products to the consumer,

wherein a respective CSD server runs on each respective processor in the computing cloud, and wherein each CSD server generates a CSD file representing a contextual state for each respective processor at a given time;

(c) aggregating the CSD files for all of the processors of the computing cloud to form an aggregated list of CSD files;

(d) determining an optimal configuration of the computing resources within the computing cloud that are needed to execute the ExecML job; and

(e) executing the ExecML job using the optimal configuration, wherein step (d) is based on the aggregated list of CSD files.

25. The machine readable medium of claim 24, wherein step (a) comprises the following steps:

(f) filtering the computing cloud to match one or more events in the computing cloud to a complex event pattern;

(g) extracting contexts for each event matched in step (f); and

(h) translating a workflow with the contexts into an executable process.

26. The machine readable medium of claim 25, wherein step (a) further comprises the following steps:

(i) generating a first descriptive intermediate file based on a workflow to be used in forming the one or more tailored geospatial intelligence products;

(j) generating a second descriptive intermediate file based on the services associated with the workflow that will be used to form the one or more tailored geospatial intelligence products;

(k) generating a third descriptive intermediate file on how the services interact with each other; and

(l) generating a fourth descriptive intermediate file based on the support data for the workflow.

27. The machine readable medium of claim 26, wherein step (a) further comprises the following step:

(m) forming an ExecML file based on the first, second, third and fourth descriptive files.

28. The machine readable medium of claim 27, wherein step (a) further comprises the following step:

(n) executing the ExecML job in the computing cloud to form the one or more tailored geospatial intelligence products.

29. The machine readable medium of claim 28, wherein step (a) further comprises the following step:

(o) determining which of one or more processors in the computing cloud will be used to form the one or more tailored geospatial intelligence products.

30. A computer system comprising:

(a) a Contextual Service Description (CSD) server, the CSD server autonomously tailoring one or more tailored geospatial intelligence products by executing an ExecML job in a computing cloud in response to a consumer request for the one or more tailored geospatial intelligence products; and

(b) providing the one or more tailored geospatial intelligence products to the consumer,

wherein a respective CSD server runs on each respective processor in the computing cloud, and wherein each CSD server generates a CSD file representing a contextual state for each respective processor at a given time;

(c) aggregating the CSD files for all of the processors of the computing cloud to form an aggregated list of CSD files;

(d) determining an optimal configuration of the computing resources within the computing cloud that are needed to execute the ExecML job; and

(e) executing the ExecML job using the optimal configuration, wherein step (d) is based on the aggregated list of CSD files.

31. The computer system implementing a method of claim 30, wherein step (a) comprises the following steps:

(f) filtering the computing cloud to match one or more events in the computing cloud to a complex event pattern;

(g) extracting contexts for each event matched in step (f); and

(h) translating a workflow with the contexts into an executable process.

32. The computer system implementing a method of claim 31, wherein step (a) further comprises the following steps:

(i) generating a first descriptive intermediate file based on a workflow to be used in forming the one or more tailored geospatial intelligence products;

(j) generating a second descriptive intermediate file based on the services associated with the workflow that will be used to form the one or more tailored geospatial intelligence products;

(k) generating a third descriptive intermediate file on how the services interact with each other; and

(l) generating a fourth descriptive intermediate file based on the support data for the workflow.

33. The computer system implementing a method of claim 32, wherein step (a) further comprises the following step:

(m) forming an ExecML file based on the first, second, third and fourth descriptive files.

34. The computer system implementing a method of claim 33, wherein step (a) further comprises the following step:

(n) executing the ExecML job in the computing cloud to form the one or more tailored geospatial intelligence products.

35. The computer system implementing a method of claim 34, wherein step (a) further comprises the following step:

(o) determining which of one or more processors in the computing cloud will be used to form the one or more tailored geospatial intelligence products.