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Online Analytical Processing, or OLAP (IPA: /ˈoʊlæp/), is an approach to quickly answer multi-dimensional analytical queries.¹ OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining.² The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas. The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing).³

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.⁴

Nigel Pendse has suggested that an alternative and perhaps more descriptive term to describe the concept of OLAP is Fast Analysis of Shared Multidimensional Information (FASMI).⁵

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.

Contents 1 Concept 2 Aggregations 3 Types 3.1 Multidimensional 3.2 Relational 3.3 Hybrid 3.4 Comparison 3.5 Other types 4 APIs and query languages 5 Products 5.1 History 5.2 Market structure 6 See also 7 Bibliography 8 References

Concept

In the core of any OLAP system is a concept of 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 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
|      2008.08|     1234|---+     +----------------------------+
+-----------------------+   |     | time_id | timestamp        |
                            |     +----------------------------+
                            +---->|   1234  | 20080902 12:35:43|
                                  +----------------------------+

Aggregations

It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time for the same query on OLTP relational data. ^{6 7} The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations, also called views. 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 ⁸.

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.

A very effective way to support aggregation and other common OLAP operations is the use of bitmap indexes.

Types

OLAP systems have been traditionally categorized using the following taxonomy.⁹

Multidimensional

Main article: MOLAP

MOLAP is the 'classic' form of OLAP and is sometimes referred to as just OLAP. MOLAP uses database structures that are generally optimal for attributes such as time period, location, product or account code. The way that each dimension will be aggregated is defined in advance by one or more hierarchies.

Relational

Main article: ROLAP

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.

Hybrid

Main article: HOLAP

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.

Comparison

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. Database explosion is 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. The typical mitigation technique for database explosion is not to materialize all the possible aggregation, but only the optimal subset of aggregations based on the desired performance vs. storage trade off.

• 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.¹⁰

• ROLAP is generally more scalable.¹⁰ However, large volume pre-processing is difficult to implement efficiently so it is frequently skipped. ROLAP query performance can therefore suffer.

• 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 quickly, 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

APIs and query languages

Unlike relational databases, which had SQL as the standard query language, and wide-spread 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.¹¹

Products

History

The first product that performed OLAP queries was Express, which was released in 1970 (and acquired by Oracle in 1995 from Information Resources)¹². However, the term did not appear until 1993 when it was coined by Ted Codd, who has been described as "the father of the relational database". Codd's paper¹ 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.

Market structure

Below is a list of top OLAP vendors in 2006, with figures in millions of United States Dollars.¹³

Vendor	Global Revenue
Microsoft Corporation	1,801
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.¹⁴

See also

• Business intelligence
• Data warehousing
• Data mining
• Predictive analytics
• Business analytics
• OLTP

Bibliography

• Daniel Lemire (2007-12). "Data Warehousing and OLAP-A Research-Oriented Bibliography" (in English).

• (1997) OLAP Solutions: Building Multidimensional Information Systems, 2nd Edition. John Wiley & Sons. ISBN 978-0471149316. 

References

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 on 2008-03-05.
2. ^ Deepak Pareek (2007). Business Intelligence for Telecommunications. CRC Press, 294 pp. Retrieved on 2008-03-18. 
3. ^ "OLAP Council White Paper". OLAP Council (1997). Retrieved on 2008-03-18.
4. ^ Hari Mailvaganam (2007). "Introduction to OLAP - Slice, Dice and Drill!". Data Warehousing Review. Retrieved on 2008-03-18.
5. ^ Nigel Pendse (2008-03-03). "What is OLAP? An analysis of what the often misused OLAP term is supposed to mean". OLAP Report. Retrieved on 2008-03-18.
6. ^ MicroStrategy, Incorporated (1995). "The Case for Relational OLAP". Retrieved on 2008-03-20.
7. ^ Surajit Chaudhuri and Umeshwar Dayal (1997). "An overview of data warehousing and OLAP technology". SIGMOD Rec. (ACM) 26: 65. doi:10.1145/248603.248616. ISSN 0163-5808, http://doi.acm.org/10.1145/248603.248616. Retrieved on 20 March 2008. 
8. ^ 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): pp. 29–53, http://citeseer.ist.psu.edu/gray97data.html. Retrieved on 20 March 2008. 
9. ^ Nigel Pendse (2006-06-27). "OLAP architectures". OLAP Report. Retrieved on 2008-03-17.
10. ^ ^{a b} Bach Pedersen, Torben; S. Jensen (December 2001). "Multidimensional Database Technology". Distributed Systems Online (IEEE): 40-46. ISSN 0018-9162, http://ieeexplore.ieee.org/iel5/2/20936/00970558.pdf. 
11. ^ Nigel Pendse (2007-08-23). "Commentary: OLAP API wars". OLAP Report. Retrieved on 2008-03-18.
12. ^ Nigel Pendse (2007-08-23). "The origins of today’s OLAP products". OLAP Report. Retrieved on November 27, 2007.
13. ^ Nigel Pendse (2006). "OLAP Market". OLAP Report. Retrieved on 2008-03-17.
14. ^ Nigel Pendse (2008-03-07). "Consolidations in the BI industry". Retrieved on 2008-03-18.

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