Abstract: Artificial intelligence and data centric applications may be programmed, operated and managed systematically by using a common representation model of data, machine learning and other computing components and application logic and flows, and providing a configuration system with layered architecture involving computing nodes in a hybrid and multi-cloud environment involving computing notes of various architectures. Components can be easily added or changed to adapt to changing business requirements, and new layers of parallelization may be added to existing components. Thus embodiments of the method and system may play a role in artificial intelligence centric products akin to product assembly lines in manufacturing such as the auto industry, and with greater degree of granularity.

Abstract: An entity (a device, a user of a device or set of devices, a user of one or more applications on a device, a group of users of the device or set of devices, or the like) is identified across multiple device, usage, and application domains. The entity is assigned a unique entity identity that is generated from a set of feature data that model the entity. The feature data typically includes deterministic data, device and system-specific feature data, and usage feature data. The identity is generated by applying to the feature data one or more rules that identify which of the feature data to use to generate the entity identity. The rules include at least one deterministic rule, and at least one probabilistic rule. Periodically, an identity is merged into one or more entity identities that are found, by applying a rule, to represent a same entity.