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TopOlap Database Systems
OLAP databases are capable of capturing the structure of business data in the form of multi-dimensional tables which are known as data cubes that form an essential part of information systems, like DSS, MIS, and ERP systems. Manipulation and presentation of such information through interactive multi-dimensional tables and graphical displays provide important support for the business analyst.
The highly normalized form of the relational data model for OLTP databases is inappropriate in an OLAP database for performance reasons (Kimball, 1996). Therefore, OLAP database implementations typically employ a star model, which stores data de-normalized in a central fact table and associated dimension tables. This type of data model allows for fast query access because the number of table joins is heavily reduced compared to the relational model.
In a star scheme, data is organized into measures and dimensions. Measures are the basic numerical units of interest for analysis and textual dimensions correspond to different perspectives for viewing measures. Dimensions are usually organized as dimension hierarchies, which offer the possibility to inspect measures on different dimension hierarchy levels. Aggregating measures up to a certain dimension level with aggregation functions like SUM, COUNT, and AVERAGE, creates a multi-dimensional view of the data, also known as the data or OLAP cube.
Drill-down equations are formed by the application of a specific aggregation function f on a measure y(C), somewhere in the lattice L (see Appendix A for details). The aggregation we consider here is the common SUM function. The measure y is an additive drill-down measure if for every cell 
where C is a cube in the lattice L, we have
(1)The latter equation is a used for expanding a dimension that is of interest. Equations in OLAP are simple SUM, COUNT, MAX, or MIN equations, in general however we can have arbitrary equations of the form:
(2) where and
y and
are measures on the same cube
C.