Online analytical processing

·         In computing, online analytical processing, or OLAP is an approach to swiftly answer multi-dimensional analytical queries.

·        OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining.

·        Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture.

·        The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing).

·        Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time.

·        They borrow aspects of navigational databases and hierarchical databases that are faster than relational databases.

·        The output of an OLAP query is typically displayed in a matrix (or pivot) format.

·        The dimensions form the rows and columns of the matrix; the measures form the values.

·        The core of any OLAP system is an OLAP cube (also called a 'multidimensional cube' or a hypercube).

·        It consists of numeric facts called measures which are categorized by dimensions.

·        The cube metadata is typically created from a star schema or snowflake schema of tables in a relational database.

·        Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.

Multidimensional databases

·        Multidimensional structure is defined as “a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data”.

·        The structure is broken into cubes and the cubes are able to store and access data within the confines of each cube.

·        “Each cell within a multidimensional structure contains aggregated data related to elements along each of its dimensions”.

·        Even when data is manipulated it is still easy to access as well as be a compact type of database.

·        The data still remains interrelated.

·        Multidimensional structure is quite popular for analytical databases that use online analytical processing (OLAP) applications (O’Brien & Marakas, 2009).

·        Analytical databases use these databases because of their ability to deliver answers swiftly to complex business queries.

·        Data can be seen from different ways, which gives a broader picture of a problem unlike other models.

·        Below is a list of top OLAP vendors in 2006, with figures in millions of US Dollars.

Vendor	Global Revenue
Microsoft Corporation	1,806
Hyperion Solutions Corporation	1,077
Cognos	735
Business Objects	416
MicroStrategy	416
SAP AG	330
Cartesis SA	210
Applix	205
Infor	199
Oracle Corporation	159
Others	152
Total	5,700

·        Microsoft was the only vendor that continuously exceeded the industrial average growth during 2000-2006. Since the above data was collected, Hyperion has been acquired by Oracle, Cartesis by Business Objects, Business Objects by SAP, Applix by Cognos, and Cognos by IBM.

Reference from:

http://en.wikipedia.org/wiki/Online_analytical_processing