Pangeanic Corporate Solution Architecture: Difference between revisions
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The solution is implemented on a fully distributed architecture with different modules: | The solution is implemented on a fully distributed architecture with different modules and covers 3 main areas: | ||
# SaaS | |||
# Data Management | |||
# Machine Learning | |||
*Users access the functionalities with a variety of client interfaces (described later) such as the PGB, Web applications, CAT tools or programmatically with a RESTFul API for integrations. | *Users access the functionalities with a variety of client interfaces (described later) such as the PGB, Web applications, CAT tools or programmatically with a RESTFul API for integrations. | ||
*The Production Access Server manages user requests and orchestrates the rest of modules. It requires a standard SQL database to store the required data to fulfil the requests. | *The Production Access Server manages user requests and orchestrates the rest of modules. It requires a standard SQL database to store the required data to fulfil the requests. | ||
Revision as of 15:44, 3 December 2021
Pangeanic Architecture Block Diagrams
The diagram shows the different logical blocks.
The solution is implemented on a fully distributed architecture with different modules and covers 3 main areas:
- SaaS
- Data Management
- Machine Learning
- Users access the functionalities with a variety of client interfaces (described later) such as the PGB, Web applications, CAT tools or programmatically with a RESTFul API for integrations.
- The Production Access Server manages user requests and orchestrates the rest of modules. It requires a standard SQL database to store the required data to fulfil the requests.
- The engines, either local (managed by the organization on their own premises or on their own cloud) or operated by Pangeanic with a SaaS model will perform the actual language processing.
- A file processor is in charge of dealing with converting files and documents when this feature is installed.
- An on-line trainer module is in charge of evolving the models according to the user preferences. This is integrated in the engine package when the on-line learning option is installed.