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, the repository structure (documents and folders), the workflows instances and tasks and the custom metadata of the several Alfresco instances you have to analyze. 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).
Below a picture that describes the available Data Marts: the audited actions, the repository structure (documents and folders), the workflows instances and tasks and the custom metadata.
Compared with the logical representation of the Data Mart, the developed ones are 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.
From a physical point of view the A.A.A.R. Data Mart is a Relational-DBMS composed by dimension tables and fact tables. The Data Marts are stored in the ‘AAAR_DataMart’ DBMS Schema, together with the working tables used for the solution and detailed ahead in the documentation. Below a brief introduction of all the data marts.