OLAP (online analytical processing) enables a user to easily and selectively extract and view
data from different points-of-view. For example, a user can request that data be analyzed to
display a spreadsheet showing all of a company's beach ball products sold in Florida in the month
of July, compare revenue figures with those for the same products in September, and then see a
comparison of other product sales in Florida in the same time period. To facilitate this kind of
analysis, OLAP data is stored in a "multidimensional" database. Whereas a relational database can
be thought of as two-dimensional, a multidimensional database considers each data attribute (such
as product, geographic sales region, and time period) as a separate "dimension." OLAP software can
locate the intersection of dimensions (all products sold in the Eastern region above a certain
price during a certain time period) and display them. Attributes such as time periods can be broken
down into subattributes.
OLAP can be used for data mining or the discovery of previously undiscerned relationships between
data items. An OLAP database does not need to be as large as a data warehouse, since not all
transactional data is needed for trend analysis. Using Open Database Connectivity (ODBC), data can
be imported from existing relational databases to create a multidimensional database for
OLAP.
Two leading OLAP products are Arbor Software's Essbase and Oracle's Express Server.
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OLAP products
are typically designed for multiple-user environments, with the cost of the software based on the
number of users.This was first published in June 2001
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