Data Warehouse Concepts - Data Warehouse Systems: Design and Implementation - page 71

Table 3.1 Summary of the OLAP operations
Operation Purpose
Add measure Adds new measures to a cube computed from other measures or
dimensions.

Aggregation
operations

Aggregate the cells of a cube, possibly after performing a group-
ing of cells.
Dice Keeps the cells of a cube that satisfy a Boolean condition over
dimension levels, attributes, and measures.
Difference Removes the cells of a cube that are in another cube. Both cubes
must have the same schema.
Drill-across Merges two cubes that have the same schema and instances using
a join condition.
Drill-down Disaggregates measures along a hierarchy to obtain data at a
finer granularity. It is the opposite of the roll-up operation.
Drill-through Shows data in the operational systems from which the cube was
derived.
Drop measure Removes measures from a cube.
Pivot

Rotates the axes of a cube to provide an alternative presentation
of its data.

Recursive
roll-up

Performs an iteration of roll-ups over a recursive hierarchy until
the top level is reached.

Rename Renames one or several schema elements of a cube.

Roll-up Aggregates measures along a hierarchy to obtain data at a coarser

granularity. It is the opposite of the drill-down operation.

Roll-up* Shorthand notation for a sequence of roll-up operations.

Slice Removes a dimension from a cube by fixing a single value in a

level of the dimension.

Sort Orders the members of a dimension according to an expression.

Union Combines the cells of two cubes that have the same schema but

disjoint members.

names but the same data), homonyms (fields with the same name but
different meanings), multiplicity of occurrences of data, and many others.
In operational databases these problems are typically solved in the design
phase.
￿ Nonvolatile means that durability of data is ensured by disallowing data
modification and removal, thus expanding the scope of the data to a longer
period of time than operational systems usually offer. A data warehouse
gathers data encompassing several years, typically 5-10 years or beyond,
while data in operational databases is often kept for only a short period
of time, for example, from 2 to 6 months, as required for daily operations,
and it may be overwritten when necessary.
￿ Time varying indicates the possibility of retaining different values for
the same information, as well as the time when changes to these values
occurred. For example, a data warehouse in a bank might store information