Abstract: A processing system including at least one processor may obtain a first ontology of a first type of data pipeline component, map the first ontology to a second ontology for a second type of data pipeline component that is stored in a catalog of data pipeline component types, provide a second data schema for the second type of data pipeline component as a template for a first data schema for the first type of data pipeline component, and add the first type of data pipeline component to the catalog of data pipeline component types, where the adding comprises storing the first ontology and the first data schema for the first type of data pipeline component in the catalog of data pipeline component types.

Abstract: A processing system including at least one processor may obtain a first ontology of a first type of data pipeline component, map the first ontology to a second ontology for a second type of data pipeline component that is stored in a catalog of data pipeline component types, provide a second data schema for the second type of data pipeline component as a template for a first data schema for the first type of data pipeline component, and add the first type of data pipeline component to the catalog of data pipeline component types, where the adding comprises storing the first ontology and the first data schema for the first type of data pipeline component in the catalog of data pipeline component types.

Abstract: A processing system including at least one processor may obtain a first ontology of a first type of data pipeline component, map the first ontology to a second ontology for a second type of data pipeline component that is stored in a catalog of data pipeline component types, provide a second data schema for the second type of data pipeline component as a template for a first data schema for the first type of data pipeline component, and add the first type of data pipeline component to the catalog of data pipeline component types, where the adding comprises storing the first ontology and the first data schema for the first type of data pipeline component in the catalog of data pipeline component types.

Abstract: A method includes creating one or more first policy shims to be applied to a ML/AI module, applying the one or more first policy shims to an input or an output of the ML/AI module and executing the ML/AI module on a data set in response to the applying step. The one or more first policy shims includes an input policy shim and an output policy shim and the applying step includes applying the input policy shim to the data set prior to the executing step and applying the output policy shim to an output of the executing step.

Abstract: A method includes defining a plurality of variables to modify in a control loop; collecting first data using a first variable of the plurality of variables while executing the control loop, generating a first result based on the collecting first data step, substituting a second variable of the plurality of variables for the first variable, collecting second data using the second variable while executing the control loop, generating a second result based on the collecting second data step, comparing the first result and the second result; and taking an action based on the comparing step.

Abstract: A processing system including at least one processor may obtain a first request for delivery of a first data set to a first destination, map the first request to a first information model, obtain a second request for delivery of a second data set to a second destination, map the second request to a second information model, and identify that a portion of data is part of both data sets. The processing system may next determine a plan for configuring data pipeline components for delivering the first data set to the first destination and the second data set to the second destination, the plan comprising: a combination of the first information model and the second information model, and at least one modification to the combination. The processing system may then configure the data pipeline components in accordance with the plan.

Abstract: A method includes defining a plurality of variables to modify in a control loop; collecting first data using a first variable of the plurality of variables while executing the control loop, generating a first result based on the collecting first data step, substituting a second variable of the plurality of variables for the first variable, collecting second data using the second variable while executing the control loop, generating a second result based on the collecting second data step, comparing the first result and the second result; and taking an action based on the comparing step.

Abstract: A processing system including at least one processor may obtain a first request for delivery of a first data set to a first destination, map the first request to a first information model, obtain a second request for delivery of a second data set to a second destination, map the second request to a second information model, and identify that a portion of data is part of both data sets. The processing system may next determine a plan for configuring data pipeline components for delivering the first data set to the first destination and the second data set to the second destination, the plan comprising: a combination of the first information model and the second information model, and at least one modification to the combination. The processing system may then configure the data pipeline components in accordance with the plan.

Abstract: Concepts and technologies disclosed herein are directed to automated control loop searching (“ACLS”). According to one aspect disclosed herein, an ACLS system can create a search model that provides high-level information regarding what the ACLS system should search for when a search pattern is detected within data that is output from execution of a control loop. The ACLS system can activate a control loop system that executes the control loop to yield the data as output. The ACLS system can detect the search pattern within data, and in response, the ACLS system can execute, based upon the search model, a search of the data. The ACLS system can collect search results of the search and select additional data from the search results.

Abstract: Fast-aiding radio access network intelligent controllers (RICs) can assist other RICs that are experiencing performance issues. The system can take into account the availability of other RICs that can aid the RIC needing assistance. Therefore, multiple RICs cooperatively working together can generate synergies, and allow differentiated RICs to operate in both predefined and/or dynamically create RIC groups. Once a RIC that needs assistance is identified, other RICs can offload functionalities that are less important than the functionality of the RIC that is experiencing the performance issues. After the assisting RIC has offloaded its functionalities, it can then devote resources to the RIC that needs the assistance.

Abstract: In one embodiment, a method includes monitoring, by a control loop including a processor and a memory, a first environment. The control loop includes one or more predetermined control loop parameters. The method also includes receiving, by the control loop and in response to monitoring the first environment, first data from the first environment and receiving, by the control loop, information from an adaptation control loop. The method also includes determining, by the control loop, to automatically adjust at least one of the one or more predetermined control loop parameters based at least in part on the information received from the adaptation control loop and automatically adjusting, by the control loop, the one or more predetermined control loop parameters. The method further includes determining, by the control loop, to initiate an action based on the first data collected from the first environment and the one or more adjusted control loop parameters.

Abstract: Concepts and technologies disclosed herein are directed to automated control loop grading and data labeling (“ACLGDL”). An ACLGDL system analyzes results of an execution, by a control loop system, of a control loop. The ACLGDL system can grade the results. The ACLGDL system also can instruct, based at least in part upon the grade of the results of the execution, the control loop system to collect additional data. The ACLGDL system can label the additional data for use by an output system. The ACLGDL system can establish plurality of policies including a grading-analysis policy, a grading-results policy, a labeling-collection policy, a labeling policy, a publishing policy, and a notification policy. The ACLGDL system can publish the data labeled in accordance with the labeling policy based, at least in part, upon the publishing policy. The ACLGDL system can notify the output system based, at least in part, upon the notification policy.

Abstract: Fast-aiding radio access network intelligent controllers (RICs) can assist other RICs that are experiencing performance issues. The system can take into account the availability of other RICs that can aid the RIC needing assistance. Therefore, multiple RICs cooperatively working together can generate synergies, and allow differentiated RICs to operate in both predefined and/or dynamically create RIC groups. Once a RIC that needs assistance is identified, other RICs can offload functionalities that are less important than the functionality of the RIC that is experiencing the performance issues. After the assisting RIC has offloaded its functionalities, it can then devote resources to the RIC that needs the assistance.

Abstract: In one embodiment, a method includes monitoring, by a control loop including a processor and a memory, a first environment. The control loop includes one or more predetermined control loop parameters. The method also includes receiving, by the control loop and in response to monitoring the first environment, first data from the first environment and receiving, by the control loop, information from an adaptation control loop. The method also includes determining, by the control loop, to automatically adjust at least one of the one or more predetermined control loop parameters based at least in part on the information received from the adaptation control loop and automatically adjusting, by the control loop, the one or more predetermined control loop parameters. The method further includes determining, by the control loop, to initiate an action based on the first data collected from the first environment and the one or more adjusted control loop parameters.

Abstract: Concepts and technologies disclosed herein are directed to automated control loop grading and data labeling (“ACLGDL”). An ACLGDL system analyzes results of an execution, by a control loop system, of a control loop. The ACLGDL system can grade the results. The ACLGDL system also can instruct, based at least in part upon the grade of the results of the execution, the control loop system to collect additional data. The ACLGDL system can label the additional data for use by an output system. The ACLGDL system can establish plurality of policies including a grading-analysis policy, a grading-results policy, a labeling-collection policy, a labeling policy, a publishing policy, and a notification policy. The ACLGDL system can publish the data labeled in accordance with the labeling policy based, at least in part, upon the publishing policy. The ACLGDL system can notify the output system based, at least in part, upon the notification policy.

Abstract: A processing system may generate a first dataset according to a first policy set, record first metadata for the first dataset, generate a first enhanced dataset from the first dataset and a second dataset, according to a second policy set to associate the first and second datasets, and record second metadata including information regarding the second policy set that is applied to associate the first and second datasets, generate a second enhanced dataset derived from the first enhanced dataset and a third dataset according to a fifth policy set to associate the first enhanced dataset with at least the third dataset, the first and second datasets from a first domain and the third dataset from a second domain, record fifth metadata including information associated with the fifth policy set to associate the first enhanced dataset with the third dataset, and add the second enhanced dataset to a dataset catalog.

Abstract: A processing system including at least one processor may obtain a first request for delivery of a first data set to a first destination, map the first request to a first information model, obtain a second request for delivery of a second data set to a second destination, map the second request to a second information model, and identify that a portion of data is part of both data sets. The processing system may next determine a plan for configuring data pipeline components for delivering the first data set to the first destination and the second data set to the second destination, the plan comprising: a combination of the first information model and the second information model, and at least one modification to the combination. The processing system may then configure the data pipeline components in accordance with the plan.

Abstract: A processing system including at least one processor may obtain a first ontology of a first type of data pipeline component, map the first ontology to a second ontology for a second type of data pipeline component that is stored in a catalog of data pipeline component types, provide a second data schema for the second type of data pipeline component as a template for a first data schema for the first type of data pipeline component, and add the first type of data pipeline component to the catalog of data pipeline component types, where the adding comprises storing the first ontology and the first data schema for the first type of data pipeline component in the catalog of data pipeline component types.

Abstract: Concepts and technologies disclosed herein are directed to automated control loop searching (“ACLS”). According to one aspect disclosed herein, an ACLS system can create a search model that provides high-level information regarding what the ACLS system should search for when a search pattern is detected within data that is output from execution of a control loop. The ACLS system can activate a control loop system that executes the control loop to yield the data as output. The ACLS system can detect the search pattern within data, and in response, the ACLS system can execute, based upon the search model, a search of the data. The ACLS system can collect search results of the search and select additional data from the search results.

Abstract: Concepts and technologies disclosed herein are directed to automated control loop grading and data labeling (“ACLGDL”). According to one aspect disclosed herein, an ACLGDL system analyzes results of an execution, by a control loop system, of a control loop. The ACLGDL system can grade the results. The ACLGDL system also can instruct, based at least in part upon the grade of the results of the execution, the control loop system to collect additional data. The ACLGDL system can label the additional data for use by an output system. The ACLGDL system can establish plurality of policies including a grading-analysis policy, a grading-results policy, a labeling-collection policy, a labeling policy, a publishing policy, and a notification policy. The ACLGDL system can publish the data labeled in accordance with the labeling policy based, at least in part, upon the publishing policy. The ACLGDL system can notify the output system based, at least in part, upon the notification policy.