Systems, methods, and devices for an enterprise AI and internet-of-things platform

1. A method for simplifying or unifying access or processing of aggregated data, comprising:

with aid of an integration component, obtaining and aggregating data from a plurality of different sources comprising smart devices, sensors, enterprise systems, extraprise systems, or Internet sources, wherein the plurality of different sources utilize different underlying technologies or programming languages, wherein the data is persisted in a plurality of data stores and comprises structured data, time-series data, unstructured data, and relational data;

implementing abstraction and continuous processing of the aggregated data using a model driven architecture, wherein implementing the abstraction of the aggregated data comprises transforming the aggregated data to a standardized format according to a canonical data model using a plurality of configurable transformers corresponding to the plurality of different sources, wherein each configurable transformer of the plurality of configurable transformers is associated with only one corresponding source of the plurality of different sources, wherein the transforming comprises correlating the aggregated data across the plurality of different sources, and wherein the canonical data model comprises standardized data definitions that provide an interface across the different underlying technologies and programming languages and abstract details of the plurality of data stores, to thereby simplify or unify the access or the processing of the aggregated data, wherein the model driven architecture is logically separated into a plurality of layers selected from the group consisting of an entity layer, an application layer, a machine learning inference layer, and a user interface (UI) layer;

merging, by a runtime engine, two or more distinct layers selected from said plurality of layers when the abstracted data is accessed or processed at runtime using programmatic interfaces, rules, or machine learning algorithms to make inferences or draw conclusions to inform end users or machine-to-machine actions; and

providing an application development platform, wherein the application development platform comprises virtual building blocks that are used to write application business logic, wherein the virtual building blocks define and/or graphically represent application logic functions including the programmatic interfaces, rules, or machine learning algorithms, and wherein the virtual building blocks allow for drag and drop development to write the application business logic.

2. The method of claim 1 , wherein the model driven architecture is further configured to provide an interface across processing technology comprising of data transposition, queues, stream processing, batch processing, data encryption, authorization, or authentication.

3. The method of claim 1 , wherein the model driven architecture provides: (1) abstract representations of knowledge and activities governing different application domains; (2) an abstraction layer that is available and common to the end users comprising of programmers, data scientists, or business analysts; and (3) enables models to be aggregated and published subject to access controls.

4. The method of claim 1 , wherein a complexity of a machine learning or Internet-of-Things application is reduced by orders of magnitude when the data is abstracted and processed using the model driven architecture, as compared to processing of the data using a structured programming architecture.

5. The method of claim 1 , wherein the model driven architecture is configured to enforce validation of data or model structure using annotations or keywords, wherein said validation comprises determining whether required fields are present in the data and determining whether the data matches an expected format.

6. The method of claim 1 , wherein (1) the entity layer includes definitions for base data models associated with devices, entities, or customers, (2) the application layer includes definitions for application logic functions, (3) the machine learning inference layer includes one or more machine learning algorithms, and (4) the UI layer defines default view definitions for how specific models of data, models, or results of application logic functions are displayed.

7. The method of claim 1 , wherein a model defined by the model driven architecture includes one or more defined methods or functions with a standards-based interface (API) for said model.

8. The method of claim 7 , comprising at least one of:

(1) explicitly calling the one or more defined methods or functions within business logic; or

(2) automatically triggering the one or more defined methods or functions based on requests or functions made by business logic or time or data completeness-based triggers via the model driven architecture.

9. The method of claim 1 , wherein the abstraction is implemented via an abstraction layer, and the model driven architecture is configured to (1) abstract details above the abstraction layer and (2) abstract details between a plurality of models.

10. The method of claim 9 , wherein the plurality of models comprises model definitions indicating one or more properties, relationships, and functions relative to the plurality of data stores and processing technologies.

11. The method of claim 1 , wherein flexible input and output connectors are provided for batch and stream processing and data storage.

12. The method of claim 1 , wherein each configurable transformer comprises a transformation rule for transforming data from the only one corresponding source to the standardized data format, and wherein said transformation rule is configured to be changed if a format of data said corresponding source changes.