ROLAP
Relational OLAP (ROLAP) implementations are similar in functionality to MOLAP. However, these systems use an underlying RDBMS, rather than a specialized MDDB. This gives them better scalability since they are able to handle larger volumes of data than the MOLAP architectures. Also, ROLAP implementations typically have better drill-through because the detail data resides on the same database as the multidimensional data .
The ROLAP environment is typically based on the use of a data structure known as a star or snowflake schema. Analogous to a virtual MDDB, a star or snowflake schema is a way of representing multidimensional data in a two-dimensional RDBMS. The data modeler builds a fact table, which is linked to multiple dimension tables. The dimension tables consist almost entirely of keys, such as location, time, and product, which point back to the detail records stored in the fact table. This type of data structure requires a great deal of initial planning and set up, and suffers from some of the same operational and flexibility concerns of MDDBs. Additionally, since the data structures are relational, SQL must be used to access the detail records. Therefore, the ROLAP engine must perform additional work to do comparisons, such as comparing the current quarter with this quarter last year. Again, IT must be heavily involved in defining, implementing, and maintaining the database. Furthermore, the ROLAP architecture often restricts the user from performing OLAP operations in a mobile environment.
Source: Business Insight Beyond OLAP - CorVu Corporation
http://www.corvu.com/library/whitepapers/beyondolap.html