Rage analytics Overview

RAGE Analytics environment

The RAGE Analytics Environment collects, analyzes and displays analytics data from games. It uses an authentication & authorization module so other RAGE server-side assets to enjoy single-sign on functionality and share data, and other RAGE clients to contact them conveniently and securely.

Rage-analytics is composed by several modules:

• A2 - Authentication & Authorization
• Rage-analytics Real-time - Default real-time analysis.
• Rage-analytics Front-end - Analysis configuration and dashboards.
• Rage-analytics Back-end - Data collection and analysis of data sent by the tracker.

Figure 1.: RAGE modules include A2 which connects the different clients (e.g. front-end) to the applications (e.g. back-end -Gleaner-) and the information stored in a Learning Record Store.

Figure 2 comprises the architecture and technologies of RAGE Analytics:

Figure 2.: overview of RAGE architecure and technologies. The client side contains the players, the analytics front-end (for students, teachers and developers) and the administration A2 front-end. A2 controls authentication and authorization of users and roles via a JSON Web Token. The different applications accessed via A2 include the analytics back-end, analysis and visualizations tools.

Three main sides can be found:

• Client
  • Players access and play games that sends traces through a tracker.
  • The analytics front-end allows access for developers, teachers and students, obtaining different information depending on their role.
  • The A2 front-end allow administrators to manage users' accounts and information.
• A2
  • The A2 module controls authentication and authorization of the different users and the information sent in traces.
  • The JSON Web Token helps in the Authorization process.
  • From A2 it is possible to manager users, roles, resources, permissions and applications.
• Applications
  • Experience API is the standard uses for data tracking.
  • The analytics back-end stores the information obtained through A2, including game sessions.
  • Kafka keeps queues of traces to be processed.
  • Storm topologies manage the analysis process.
  • The results are stored in ElasticSearch.
  • Kibana manages the visualization of results via ElasticSearch (by default, for teachers and developers).

For more information you can check out the QuickStart guide. Also, you can integrate the tracker in your code to start generating traces from your game.