Abstract: An approach is provided for autonomous and optimal cloning, reinstating, and archiving of a containerized application. Application metadata is obtained from a catalog. Cloning rules specifying cloning an application are obtained. Application components are selected for cloning and the cloning is determined to be compliant with the cloning rules. The application components are cloned and data for the clone is saved to a clone repository. The catalog is updated with specifications of the cloned application components. Reinstating rules specifying reinstating a clone of the application are obtained. Reinstating the clone is determined to be compliant with the reinstating rules. The catalog is updated with specifications of the reinstated clone. In one embodiment, the catalog is updated with inferred associations among applications and identified rules associated with the application, where the inferred associations and identified rules are generated by a trained machine learning-based classifier.

Abstract: An approach is provided for autonomous and optimal cloning, reinstating, and archiving of a containerized application. Application metadata is obtained from a catalog. Cloning rules specifying cloning an application are obtained. Application components are selected for cloning and the cloning is determined to be compliant with the cloning rules. The application components are cloned and data for the clone is saved to a clone repository. The catalog is updated with specifications of the cloned application components. Reinstating rules specifying reinstating a clone of the application are obtained. Reinstating the clone is determined to be compliant with the reinstating rules. The catalog is updated with specifications of the reinstated clone. In one embodiment, the catalog is updated with inferred associations among applications and identified rules associated with the application, where the inferred associations and identified rules are generated by a trained machine learning-based classifier.

Abstract: Knowledge representation in multi-layered database includes systems and methods for storing and retrieving data in the multi-layered database. In the multi-layered database, an action graph database includes participant-entity nodes corresponding to real world entities and action nodes corresponding to action capabilities of the real world entities. Each of the participant-entity nodes and the action nodes is associated with properties, relationships, and relationship properties. Underlying the action graph layer is a standard graph layer that stores nodes, node properties associated with the nodes, edges, and edge properties associated with the edges, wherein the nodes correspond to the participant-entity nodes and the action nodes. Further, underlying the standard graph layer is a backend database layer that stores corresponding data and metadata.

Abstract: Knowledge representation in multi-layered database includes systems and methods for storing and retrieving data in the multi-layered database. In the multi-layered database, an action graph database includes participant-entity nodes corresponding to real world entities and action nodes corresponding to action capabilities of the real world entities. Each of the participant-entity nodes and the action nodes is associated with properties, relationships, and relationship properties. Underlying the action graph layer is a standard graph layer that stores nodes, node properties associated with the nodes, edges, and edge properties associated with the edges, wherein the nodes correspond to the participant-entity nodes and the action nodes. Further, underlying the standard graph layer is a backend database layer that stores corresponding data and metadata.