Abstract: A scalable integrated information system in a network environment, the system comprising: an agent instantiated as a virtual machine or virtual network function, the agent configured to communicate with the network environment, the network environment comprising a meta data inventory; a data store comprising a central metadata repository, the central metadata repository configured to communicate with the network environment and selectively retrieve the meta data inventory, wherein the central metadata repository stores an integrated context representation comprising at least one of a real-time temporal context, a historical context, and a meta context associated with the meta data inventory; a reasoning module instantiated as a virtual machine or virtual network function and including an input configured to receive a reasoning concept; a machine learning module, instantiated as a virtual machine or virtual network function and configured to communicate with the central metadata repository to selectively retri

Abstract: Model creation and management using a model management service can include obtaining model management data that comprises modeling data that defines a project to model, the project including a network flow, a network system, a device, or a process; detecting, in the model management data, a selection of a template to apply to the project to generate the model; and creating, based on the model management data and the template, the model. The model can include a directory, a data structure, and a diagram that describes the project. The model can be converted into a device-agnostic format version of the model and loaded to a data storage resource as model data that can include the device-agnostic format version of the model.

Abstract: A scalable integrated information system in a network environment, the system comprising: an agent instantiated as a virtual machine or virtual network function, the agent configured to communicate with the network environment, the network environment comprising a meta data inventory; a data store comprising a central metadata repository, the central metadata repository configured to communicate with the network environment and selectively retrieve the meta data inventory, wherein the central metadata repository stores an integrated context representation comprising at least one of a real-time temporal context, a historical context, and a meta context associated with the meta data inventory; a reasoning module instantiated as a virtual machine or virtual network function and including an input configured to receive a reasoning concept; a machine learning module, instantiated as a virtual machine or virtual network function and configured to communicate with the central metadata repository to selectively retri

Abstract: A scalable integrated information system in a network environment, the system comprising: an agent instantiated as a virtual machine or virtual network function, the agent configured to communicate with the network environment, the network environment comprising a meta data inventory; a data store comprising a central metadata repository, the central metadata repository configured to communicate with the network environment and selectively retrieve the meta data inventory, wherein the central metadata repository stores an integrated context representation comprising at least one of a real-time temporal context, a historical context, and a meta context associated with the meta data inventory; a reasoning module instantiated as a virtual machine or virtual network function and including an input configured to receive a reasoning concept; a machine learning module, instantiated as a virtual machine or virtual network function and configured to communicate with the central metadata repository to selectively retri

Abstract: A scalable integrated information system in a network environment, the system comprising: an agent instantiated as a virtual machine or virtual network function, the agent configured to communicate with the network environment, the network environment comprising a meta data inventory; a data store comprising a central metadata repository, the central metadata repository configured to communicate with the network environment and selectively retrieve the meta data inventory, wherein the central metadata repository stores an integrated context representation comprising at least one of a real-time temporal context, a historical context, and a meta context associated with the meta data inventory; a reasoning module instantiated as a virtual machine or virtual network function and including an input configured to receive a reasoning concept; a machine learning module, instantiated as a virtual machine or virtual network function and configured to communicate with the central metadata repository to selectively retri

Abstract: Methods, non-transitory computer readable media and devices are disclosed for caching data associated with a second state in a customer service interaction call flow when a first state in the customer service interaction call flow is reached. For example, a method includes a processor for initiating a customer service transaction associated with a customer service interaction flow in response to a communication from a user, determining that a first state in the customer service interaction flow is reached during the customer service transaction, calculating that a likelihood of needing data associated with a second state in the customer service interaction flow during the customer service transaction exceeds a threshold, retrieving, from a data store, data associated with the second state, and storing data associated with the second state in a cache.

Abstract: Methods, non-transitory computer readable media and devices are disclosed for caching data associated with a second state in a customer service interaction call flow when a first state in the customer service interaction call flow is reached. For example, a method includes a processor for initiating a customer service transaction associated with a customer service interaction flow in response to a communication from a user, determining that a first state in the customer service interaction flow is reached during the customer service transaction, calculating that a likelihood of needing data associated with a second state in the customer service interaction flow during the customer service transaction exceeds a threshold, retrieving, from a data store, data associated with the second state, and storing data associated with the second state in a cache.