Ensemble

From Steeple

Ensemble is a meta-data caching engine optimised to handle podcasting RSS data.

The above diagram shows a vision of how Ensemble can enable greater distribution of content, in a targetted, optimised manner, able to support advanced searching and browsing an providing a means to embed the content within thirdparty frameworks.

Ensemble consists of three core components: Ingest, Engine, Export. The Ingest component handles data input, feed normalisation, agregation and filtering. This robust interface is responsible for populating the Metadata Engine with consistent, standardised and agnostic podcasting metadata. The Metadata Engine handles query processing and data storage. The Export component provides a read only ReST interface to the Metadata Engine, returning data in a variety of formats and allowing a high degree of flexibility in terms of filtering, seaching and collation of data.

Ensemble is not directly responsible for generating metadata, but for agregating metadata from as many seeds as necessary. These are shown at the top of the diagram and typically represent an institution's complete podcasting output. As Ensemble can work at a variety of levels, it is possible that another Ensemble instance can be used by an instituion as its primary institutional portal backend, and therefore its output can be fed into other Ensemble instances that are collating data on a larger or more focused scale (e.g. a National Portal, or a Subject Specific portal).

Have a look at the test Steeple Podcast portal to see how an ENSEMBLE engine might work for an end user searching for material. This podcast portal allows you to search live across 4+ UK HE podcast repositories.