In general, OLAP applications are characterized by the rendering of enterprise data into multidimensional perspectives. This is achieved through complex, adhoc queries that frequently aggregate and consolidate data, often using statistical formulae. For example, a retail organization is often interested in comparing the total sales for the current year with the total sales for the previous year, or identifying sequences of 5 years or more when sales has increased within a 50-year envelope. It has been conjectured that relational database technology is well suited to fulfilling the needs of OLAP. This situation is somewhat analogous to the situation in the mid 1970s, when data processing experts would suggest special purpose algorithms to perform operations such as selection, projection, and join. warehouses. However, unrealistic restrictions are placed in these models, restrictions are imposed on either the number of attributes per dimension or the number of total measures representable in the cube. Moreover dimensions and measures are treated asymmetrically, leading to the inability of these models to answer particular types of queries with out requiring expensive redesign.