Online analytical processing

Online analytical processing

In computing, online analytical processing, or OLAP (play /ˈlæp/), is an approach to swiftly answer multi-dimensional analytical (MDA) queries.[1] OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining.[2] Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM),[3] budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture.[4] The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing).[5]

OLAP tools enable users to interactively analyze multidimensional data from multiple perspectives. OLAP consists of three basic analytical operations: consolidation, drill-down, and slicing and dicing.[6] Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. In contrast, the drill-down is a technique that allows users to navigate through the details. For instance, users can access to the sales by individual products that make up a region’s sales. Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the cube and view (dicing) the slices from different viewpoints.

Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. [7] They borrow aspects of navigational databases, hierarchical databases and relational databases.

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.

Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure.

A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale.

Any number of dimensions can be added to the structure such as Store, Cashier, or Customer by adding a foreign key column to the fact table. This allows an analyst to view the measures along any combination of the dimensions.

For example:

 Sales Fact Table  
| sale_amount | time_id  |
+-------------+----------+            Time Dimension 
|      2008.10|     1234 |---+     +---------+-------------------+
+-------------+----------+   |     | time_id | timestamp         |
                             |     +---------+-------------------+
                             +---->|   1234  | 20080902 12:35:43 |


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”.[8] 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”.[9] Even when data is manipulated it remains easy to access and continues to constitute a compact database format. 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 to complex business queries swiftly. Data can be viewed from different angles, which gives a broader perspective of a problem unlike other models.[10]


It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time required for the same query on OLTP relational data.[11][12] The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions. The number of possible aggregations is determined by every possible combination of dimension granularities.

The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data .[13]

Because usually there are many aggregations that can be calculated, often only a predetermined number are fully calculated; the remainder are solved on demand. The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time. View selection is NP-Complete. Many approaches to the problem have been explored, including greedy algorithms, randomized search, genetic algorithms and A* search algorithm.


OLAP systems have been traditionally categorized using the following taxonomy.[14]


MOLAP is the 'classic' form of OLAP and is sometimes referred to as just OLAP. MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database. Therefore it requires the pre-computation and storage of information in the cube - the operation known as processing.


ROLAP works directly with relational databases. The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. Depends on a specialized schema design.This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality. In essence, each action of slicing and dicing is equivalent to adding a "WHERE" clause in the SQL statement.


There is no clear agreement across the industry as to what constitutes "Hybrid OLAP", except that a database will divide data between relational and specialized storage. For example, for some vendors, a HOLAP database will use relational tables to hold the larger quantities of detailed data, and use specialized storage for at least some aspects of the smaller quantities of more-aggregate or less-detailed data.


Each type has certain benefits, although there is disagreement about the specifics of the benefits between providers.

  • Some MOLAP implementations are prone to database explosion, a phenomenon causing vast amounts of storage space to be used by MOLAP databases when certain common conditions are met: high number of dimensions, pre-calculated results and sparse multidimensional data.
  • MOLAP generally delivers better performance due to specialized indexing and storage optimizations. MOLAP also needs less storage space compared to ROLAP because the specialized storage typically includes compression techniques.[15]
  • ROLAP is generally more scalable.[15] However, large volume pre-processing is difficult to implement efficiently so it is frequently skipped. ROLAP query performance can therefore suffer tremendously.
  • Since ROLAP relies more on the database to perform calculations, it has more limitations in the specialized functions it can use.
  • HOLAP encompasses a range of solutions that attempt to mix the best of ROLAP and MOLAP. It can generally pre-process swiftly, scale well, and offer good function support.

Other types

The following acronyms are also sometimes used, although they are not as widespread as the ones above:

  • WOLAP - Web-based OLAP
  • DOLAP - Desktop OLAP
  • RTOLAP - Real-Time OLAP
  • BROLAP - Bi-directional Rotational OLAP

APIs and query languages

Unlike relational databases, which had SQL as the standard query language, and widespread APIs such as ODBC, JDBC and OLEDB, there was no such unification in the OLAP world for a long time. The first real standard API was OLE DB for OLAP specification from Microsoft which appeared in 1997 and introduced the MDX query language. Several OLAP vendors - both server and client - adopted it. In 2001 Microsoft and Hyperion announced the XML for Analysis specification, which was endorsed by most of the OLAP vendors. Since this also used MDX as a query language, MDX became the de-facto standard.[16] Since September-2011 LINQ can be used to query SSAS OLAP cubes from Microsoft .NET[17].



The first product that performed OLAP queries was Express, which was released in 1970 (and acquired by Oracle in 1995 from Information Resources).[18] However, the term did not appear until 1993 when it was coined by Edgar F. Codd, who has been described as "the father of the relational database". Codd's paper[1] resulted from a short consulting assignment which Codd undertook for former Arbor Software (later Hyperion Solutions, and in 2007 acquired by Oracle), as a sort of marketing coup. The company had released its own OLAP product, Essbase, a year earlier. As a result Codd's "twelve laws of online analytical processing" were explicit in their reference to Essbase. There was some ensuing controversy and when Computerworld learned that Codd was paid by Arbor, it retracted the article. OLAP market experienced strong growth in late 90s with dozens of commercial products going into market. In 1998, Microsoft released its first OLAP Server - Microsoft Analysis Services, which drove wide adoption of OLAP technology and moved it into mainstream.

Product comparison

See: Comparison of OLAP Servers.

Market structure

Below is a list of top OLAP vendors in 2006, with figures in millions of US Dollars.[19]

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.[20]

See also


  • Erik Thomsen. (1997). OLAP Solutions: Building Multidimensional Information Systems, 2nd Edition. John Wiley & Sons. ISBN 978-0471149316. 
  • O’Brien, J. A., & Marakas, G. M. (2009). Management information systems (9th ed.). Boston, MA: McGraw-Hill/Irwin.


  1. ^ a b Codd E.F., Codd S.B., and Salley C.T. (1993). "Providing OLAP (On-line Analytical Processing) to User-Analysts: An IT Mandate". Codd & Date, Inc. Retrieved 2008-03-05. 
  2. ^ Deepak Pareek (2007). Business Intelligence for Telecommunications. CRC Press. pp. 294 pp. ISBN 0849387922. Retrieved 2008-03-18. 
  3. ^ Apostolos Benisis (2010). Business Process Management:A Data Cube To Analyze Business Process Simulation Data For Decision Making. VDM Verlag Dr. Müller e.K.. pp. 204 pp. ISBN 978-3-639-22216-6. 
  4. ^ Abdullah, Ahsan (2009). "Analysis of mealybug incidence on the cotton crop using ADSS-OLAP (Online Analytical Processing) tool , Volume 69, Issue 1". Computers and Electronics in Agriculture 69 (1): 59–72. doi:10.1016/j.compag.2009.07.003. 
  5. ^ "OLAP Council White Paper" (PDF). OLAP Council. 1997. Retrieved 2008-03-18. 
  6. ^ O'Brien & Marakas, 2011, p. 402-403
  7. ^ Hari Mailvaganam (2007). "Introduction to OLAP - Slice, Dice and Drill!". Data Warehousing Review. Retrieved 2008-03-18. 
  8. ^ O'Brien & Marakas, 2009, pg 177
  9. ^ O'Brien & Marakas, 2009, pg 178
  10. ^ Williams, C., Garza, V.R., Tucker, S, Marcus, A.M. (1994, January 24). Multidimensional models boost viewing options. InfoWorld, 16(4)
  11. ^ MicroStrategy, Incorporated (1995). "The Case for Relational OLAP" (PDF). Retrieved 2008-03-20. 
  12. ^ Surajit Chaudhuri and Umeshwar Dayal (1997). "An overview of data warehousing and OLAP technology". SIGMOD Rec. (ACM) 26 (1): 65. doi:10.1145/248603.248616. Retrieved 2008-03-20. 
  13. ^ Gray, Jim; Chaudhuri, Surajit; Layman, Andrew; Reichart, Hamid; Venkatrao; Pellow; Pirahesh (1997). "Data Cube: {A} Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals". J. Data Mining and Knowledge Discovery 1 (1): 29–53. Retrieved 2008-03-20. 
  14. ^ Nigel Pendse (2006-06-27). "OLAP architectures". OLAP Report. Retrieved 2008-03-17. [dead link]
  15. ^ a b Bach Pedersen, Torben; S. Jensen, Christian (December 2001). "Multidimensional Database Technology" (PDF). Distributed Systems Online (IEEE): 40–46. ISSN 0018-9162. 
  16. ^ Nigel Pendse (2007-08-23). "Commentary: OLAP API wars". OLAP Report. Retrieved 2008-03-18. [dead link]
  17. ^ "SSAS Entity Framework Provider for LINQ to SSAS OLAP". 
  18. ^ Nigel Pendse (2007-08-23). "The origins of today’s OLAP products". OLAP Report. Retrieved November 27, 2007. [dead link]
  19. ^ Nigel Pendse (2006). "OLAP Market". OLAP Report. Retrieved 2008-03-17. 
  20. ^ Nigel Pendse (2008-03-07). "Consolidations in the BI industry". Retrieved 2008-03-18. 

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