Abstract: Systems and methods are disclosed for using AI and machine learning to generate a dataset zone by receiving a plurality of datasets and storing them in a data catalog. A first, second, third, and fourth zone are generated having various levels of policies and permissions. Based on the known policies, the system trains a machine learning program to generate a trained predictive model and deploys the trained predictive model to predict a data privacy policy for a first dataset from the plurality of datasets. The predictive model further predicts a suitable zone for the first dataset based on the predicted data privacy policy and stores the first dataset in the suitable zone. The system then displays a representation of the first dataset in the suitable zone on a graphical user interface.

Abstract: Systems and methods are disclosed for moving data based on data governance policies, wherein a plurality of datasets from a plurality of sources are received and stored into a data catalog. Predefined zones are generated, each having predefined policies. At least one common characteristic is determined for a first dataset and a second dataset. The system receives a request from an authorized user to move the first and second datasets into a particular zone and moves the datasets accordingly. The system then displays, via a graphical user interface, a representation depicting the first and second datasets. The predefined zones include a transient zone, a raw zone, a trusted zone, and a refined zone. The plurality of datasets move through the zones through a data pipeline that performs a data quality check to ensure that the data is moved through the zones according to the predefined polices.

Abstract: Systems and methods are disclosed for generating dataset zones, wherein a plurality of datasets from a plurality of sources are received and stored into a data catalog. The method further includes: (1) generating a first zone comprising a transient zone and configured for storing the sourced data from the plurality of datasets; (2) generating a second zone comprising a raw zone and configured for storing raw data generated from the sourced data after it has been ingested and organized; (3) generating a third zone comprising a trusted zone and configured for storing standardized data generated from the raw data after it has been ingested and organized according to one or more data governance policies; and (4) generating a fourth zone comprising a refined zone and configured for storing business-specific data generated from the standardized data.

Abstract: A system and method for discovering lightpath information in an optical network is disclosed. A selected node is requested to provide lightpath information. The node, in turn, requests the other node of the optical network to provide channel attribute data. The node processes the channel attribute data of a plurality of nodes to determine the lightpath information.

Abstract: A system and method of managing subrate paths in an optical network. An example embodiment is a network administration application or component. The application receives a description of a desired service for an optical network through a graphical interface. The application uses the information in the service request to identify and provision paths for the service through the nodes. The application may include varying degrees of interactivity and automation for selecting timeslot configurations and may support line and tributary protection schemes. The application may also support multiple provisioning strategies. Example system configurations, application components, graphical interfaces, and methods are provided.