Abstract: Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository.

Abstract: Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository.

Abstract: Systems and methods for content management wherein a client can submit requests to a first API which the forwards the requests to either an IMDB or a gateway to a distributed cluster-computing framework. Requests to the IMDB are serviced and responses from the IMDB are returned to the client. Requests that are forwarded to the gateway are first modified for the distributed cluster-computing framework, and are then parsed by the gateway and used to instantiate processors that generate corresponding requests to the distributed cluster-computing framework. Responsive data from the distributed cluster-computing framework is used to generate responses to the client requests that are forwarded to the first API which modifies them to appear as if they were generated by the IMDB. These modified responses are returned by the first API to the client.

Abstract: Systems and methods for content management wherein a client can submit requests to a first API which the forwards the requests to either an IMDB or a gateway to a distributed cluster-computing framework. Requests to the IMDB are serviced and responses from the IMDB are returned to the client. Requests that are forwarded to the gateway are first modified for the distributed cluster-computing framework, and are then parsed by the gateway and used to instantiate processors that generate corresponding requests to the distributed cluster-computing framework. Responsive data from the distributed cluster-computing framework is used to generate responses to the client requests that are forwarded to the first API which modifies them to appear as if they were generated by the IMDB. These modified responses are returned by the first API to the client.

Abstract: Systems and methods for content management wherein a client can submit requests to a first API which the forwards the requests to either an IMDB or a gateway to a distributed cluster-computing framework. Requests to the IMDB are serviced and responses from the IMDB are returned to the client. Requests that are forwarded to the gateway are first modified for the distributed cluster-computing framework, and are then parsed by the gateway and used to instantiate processors that generate corresponding requests to the distributed cluster-computing framework. Responsive data from the distributed cluster-computing framework is used to generate responses to the client requests that are forwarded to the first API which modifies them to appear as if they were generated by the IMDB. These modified responses are returned by the first API to the client.

Abstract: Systems and methods for content management wherein a client can submit requests to a first API which the forwards the requests to either an IMDB or a gateway to a distributed cluster-computing framework. Requests to the IMDB are serviced and responses from the IMDB are returned to the client. Requests that are forwarded to the gateway are first modified for the distributed cluster-computing framework, and are then parsed by the gateway and used to instantiate processors that generate corresponding requests to the distributed cluster-computing framework. Responsive data from the distributed cluster-computing framework is used to generate responses to the client requests that are forwarded to the first API which modifies them to appear as if they were generated by the IMDB. These modified responses are returned by the first API to the client.

Abstract: Systems and methods for content management wherein a client can submit requests to a first API which the forwards the requests to either an IMDB or a gateway to a distributed cluster-computing framework. Requests to the IMDB are serviced and responses from the IMDB are returned to the client. Requests that are forwarded to the gateway are first modified for the distributed cluster-computing framework, and are then parsed by the gateway and used to instantiate processors that generate corresponding requests to the distributed cluster-computing framework. Responsive data from the distributed cluster-computing framework is used to generate responses to the client requests that are forwarded to the first API which modifies them to appear as if they were generated by the IMDB. These modified responses are returned by the first API to the client.

Abstract: Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository.