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 interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution. According to one aspect, a system can receive a workflow construction specification derived from a machine learning-enabled event tree (“MLET”). The MLET can be generated for use by a customer service agent to resolve a customer problem. The workflow construction specification can include a plurality of objects, each of which represents a navigation path through the MLET. The system can traverse the workflow construction specification and can create a set of workflow creation commands based upon at least one policy. The system can generate a workflow visualization interpretation file based upon the set of workflow creation commands. The workflow visualization interpretation file can identify how the MLET derived a root cause of the customer problem.

Abstract: An example method includes receiving a plurality of data request specifications from a plurality of user endpoint devices of requesting users, aggregating a subset of the plurality of data request specifications which share a similarity into a super request, retrieving a plurality of datasets, wherein the plurality of datasets is needed to fulfill the super request, aggregating the plurality of datasets into a single data superset, and delivering the single data superset to the plurality of user endpoint devices of the requesting users via a data pipeline, where the delivering includes segregating the single data superset so that each user endpoint device of the plurality of user endpoint devices of the requesting users receives a portion of the single data superset corresponding to one data request specification of the plurality of data request specifications that was submitted by the each user endpoint device.

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 interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution. According to one aspect, a system can receive a workflow construction specification derived from a machine learning-enabled event tree (“MLET”). The MLET can be generated for use by a customer service agent to resolve a customer problem. The workflow construction specification can include a plurality of objects, each of which represents a navigation path through the MLET. The system can traverse the workflow construction specification and can create a set of workflow creation commands based upon at least one policy. The system can generate a workflow visualization interpretation file based upon the set of workflow creation commands. The workflow visualization interpretation file can identify how the MLET derived a root cause of the customer problem.

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: Concepts and technologies disclosed herein are directed to managing enterprise software licenses for virtual network functions (“VNFs”). According to one aspect disclosed herein, a system can acquire a software license for a software asset to be instantiated and used by a cloud computing environment associated with an enterprise. The system can prepare, with an enterprise anchor point (“EAP”) module managed by a vendor of the software asset, a certificate validation process that uses an enterprise security certificate to ensure the software license is valid for an execution instance of the software asset. The system can instantiate the execution instance of the software asset in the cloud computing environment. The system can validate, by the EAP module, the enterprise security certificate to ensure the software asset is instantiated and used in accordance with the software license.

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 method includes receiving, by a computing device of a media service provider, a search request associated with a first product. The search request is received from a media device associated with a subscriber of a service provided by the media service provider. The method includes, in response to the search request, determining, by the computing device, a responsible party associated with the first product based on a purchase date of the first product and a return period for the first product. The method also includes sending, from the computing device to the media device, contact information of the responsible party. The contact information is to be displayed by the media device to a display device with an option to contact the responsible party.

Abstract: Concepts and technologies disclosed herein are directed to interpretation workflows for machine learning-enabled event tree-based diagnostic and customer problem resolution. According to one aspect, a system can receive a workflow construction specification derived from a machine learning-enabled event tree (“MLET”). The MLET can be generated for use by a customer service agent to resolve a customer problem. The workflow construction specification can include a plurality of objects, each of which represents a navigation path through the MLET. The system can traverse the workflow construction specification and can create a set of workflow creation commands based upon at least one policy. The system can generate a workflow visualization interpretation file based upon the set of workflow creation commands. The workflow visualization interpretation file can identify how the MLET derived a root cause of the customer problem.

Abstract: A processing system including at least one processor may perform a method including receiving a data request, executing a request fulfillment module to determine at least one information model and at least one executable flow associated with the data request, determining that at least one combining module is to be applied to the data request based on the at least one information model and the at least one executable flow, applying the at least one combining module to the data request, and generating a data pipeline to transmit data to a target that initiated the data request, wherein the data pipeline is generated in accordance with the at least one combining module that is applied.

Abstract: A processing system may obtain a request to deploy a service comprising a first virtual network function (VNF) to a multi-cloud environment comprising cloud infrastructures, the request including metadata associated with the service, decompose the service into a plurality of modules comprising: a plurality of VNFs and/or a plurality of components of the first VNF, select at least a first deployment model for the service from among a plurality of deployment models, based upon the metadata, each deployment model comprising rules for assigning the plurality of modules to cloud infrastructures, and deploy a first module to a first cloud infrastructure and a second module to a second cloud infrastructure.

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: An example method includes receiving a plurality of data request specifications from a plurality of user endpoint devices of requesting users, aggregating a subset of the plurality of data request specifications which share a similarity into a super request, retrieving a plurality of datasets, wherein the plurality of datasets is needed to fulfill the super request, aggregating the plurality of datasets into a single data superset, and delivering the single data superset to the plurality of user endpoint devices of the requesting users via a data pipeline, where the delivering includes segregating the single data superset so that each user endpoint device of the plurality of user endpoint devices of the requesting users receives a portion of the single data superset corresponding to one data request specification of the plurality of data request specifications that was submitted by the each user endpoint device.

Abstract: A method includes receiving a first request from a first user endpoint device of a first user, wherein the first request specifies a data need of the first user, identifying a first dataset from among a plurality of datasets, wherein the first dataset is determined to match the data need, determining a resource need of a second user who controls the first dataset, wherein the resource need is specified in a second request from a second user endpoint device of the second user, determining that the first user can provide a first resource that satisfies the resource need, wherein the first resource is offered by the first user, establishing an agreement upon approval from the first user and from a second user to orchestrate an exchange of the first dataset for the first resource, and delivering the first dataset to the first user endpoint device via a data pipeline.

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: A processing system may obtain a request to deploy a service comprising a first virtual network function (VNF) to a multi-cloud environment comprising cloud infrastructures, the request including metadata associated with the service, decompose the service into a plurality of modules comprising: a plurality of VNFs and/or a plurality of components of the first VNF, select at least a first deployment model for the service from among a plurality of deployment models, based upon the metadata, each deployment model comprising rules for assigning the plurality of modules to cloud infrastructures, and deploy a first module to a first cloud infrastructure and a second module to a second cloud infrastructure.

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.