Bundling of automated work flow
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.
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:
(f) filtering an event cloud to match one or more events in the event cloud to an event pattern;
(g) extracting contexts for each event matched in step (f);
(h) translating a workflow with the contexts into an executable process.
3. 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.
4. The method of claim 3 , wherein step (a) comprises:
(g) filtering an event cloud to match one or more events in the event cloud to an event pattern;
(h) extracting contexts for each event matched in step (g);
(i) translating a workflow with the contexts into an executable process.
5. 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.
6. The method of claim 5 , wherein step (a) comprises:
(g) filtering an event cloud to match one or more events in the event cloud to an event pattern;
(h) extracting contexts for each event matched in step (g);
(i) translating a workflow with the contexts into an executable process.
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) 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 using the less optimal 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:
(g) filtering an event cloud to match one or more events in the event cloud to an event pattern;
(h) extracting contexts for each event matched in step (f);
(i) translating a workflow with the contexts into an executable process.
9. 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;
(b) providing the one or more tailored 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.
10. The machine readable medium of claim 9 , wherein step (a) comprises:
(f) filtering an event cloud to match one or more events in the event cloud to an event pattern;
(g) extracting contexts for each event matched in step (f);
(h) translating a workflow with the contexts into an executable process.
11. A computer system implementing 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 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.
12. The computer system implementing a method of claim 11 , wherein step (a) comprises:
(f) filtering an event cloud to match one or more events in the event cloud to an event pattern;
(g) extracting contexts for each event matched in step (f);
(h) translating a workflow with the contexts into an executable process.