As described before, a Data Mart is a structure that is usually oriented to a specific business line or team and, in this case, represents the audited actions and the repository structure in the Alfresco E.C.M. As described in literature of the Data Warehousing techniques, the technical foundation of a Data Mart is the Star Schema. For the ones of you that doesn’t feel confident with the Data Warehousing techniques, a Star Schema is able to reply to most of the business queries a user could do on the stored data. For that reason we think the used Data Warehousing’s techniques are the right choice to fully analyze the Alfresco audit data (and not only).
- Alfresco instances to manage multiple sources of auditing data.
- Alfresco users with a complete name.
- Alfresco contents complete with the repository path.
- Alfresco actions (login, failedLogin, read, addAspect, etc.).
- Date of the action. Groupable in day, month and year.
- Time of the action. Groupable in minute and hour.
In the image below the logical representation of the Star Schema representing the Audit Data Mart.
Fig. – The logical representation of the Audit Star Schema
Compared with the logical representation of the Data Mart, the developed one is a special case of Star Schema called Snowflake Schema. This kind of schema is used to optimize queries and stored data from a technical point of view but don’t mind if you don’t know the difference in detail, they will be described ahead in the documentation.
From a physical point of view the A.A.A.R. Data Mart is a DBMS composed by dimension tables and one fact table. The A.A.A.R. Data Mart is stored in the ‘AAAR_DataMart’ DBMS Schema, together with the working tables used for the solution and detailed ahead in the documentation.
Fig. – The ‘AAAR_DataMart’ DBMS Schema
Below all the entities are described with more detail.
- Alfresco instances dimension
- Alfresco users dimension
- Alfresco content dimension
- Alfresco actions dimension
- Date dimension
- Time dimension
- Audit actions fact