Online analytical processing (OLAP) systems--which have their roots
in decision support systems (DSSs) and executive information systems
(EISs)--store data in multidimensional databases. You then access the databases
to perform financial and statistical analyses on different combinations of the
data. OLAP products are particularly useful for time-series analyses and
recursive calculations.
Vendors offer a variety of OLAP products that you can group into three
categories: relational OLAP (ROLAP), multidimensional OLAP (MOLAP), and hybrid
OLAP (HOLAP). ROLAP products (e.g., Informix's MetaCube ROLAP Option for the
Informix Dynamic Server, MicroStrategy's DSS Agent) adapt traditional relational
databases to support OLAP. Vendors often use a star schema structure to extend
and adapt an underlying relational database as an OLAP server.
MOLAP products (such as Arbor Software's Arbor Essbase OLAP Server 5 and
Oracle's Oracle Express Server) provide multi-dimensional analyses of data by
putting data in a cube structure. Most successful MOLAP products use a multicube
approach in which a series of small, dense, precalculated cubes make up a
hypercube.
HOLAP products (e.g., Microsoft SQL Server OLAP Services and Pilot
Software's Pilot Decision Support Suite) combine MOLAP and ROLAP. With HOLAP
products, a relational database stores most of the data. A separate
multi-dimensional database stores the most dense data, which is typically a
small proportion of the data.