||It has been suggested that this article be merged into Online Analytical Processing. (Discuss) Proposed since February 2013.|
MOLAP stands for Multidimensional Online Analytical Processing.
MOLAP is an alternative to the ROLAP (Relational OLAP) technology. While both ROLAP and MOLAP analytic tools are designed to allow analysis of data through the use of a multidimensional data model, MOLAP differs significantly in that (in some software) it requires the pre-computation and storage of information in the cube — the operation known as processing. Most MOLAP solutions store these data in an optimized multidimensional array storage, rather than in a relational database (i.e. in ROLAP).
There are many methodologies and algorithms for efficient data storage, aggregation and implementation specific business logic with a MOLAP Solution. As a result there are many misconceptions to what the term specifically implies.
Advantages of MOLAP
- Fast query performance due to optimized storage, multidimensional indexing and caching.
- Smaller on-disk size of data compared to data stored in relational database due to compression techniques.
- Automated computation of higher level aggregates of the data.
- It is very compact for low dimension data sets.
- Array models provide natural indexing.
- Effective data extraction achieved through the pre-structuring of aggregated data.
Disadvantages of MOLAP
- Within some MOLAP Solutions the processing step (data load) can be quite lengthy, especially on large data volumes. This is usually remedied by doing only incremental processing, i.e., processing only the data which have changed (usually new data) instead of reprocessing the entire data set.
- MOLAP tools traditionally have difficulty querying models with dimensions with very high cardinality (i.e., millions of members).
- Some MOLAP products have difficulty updating and querying models with more than ten dimensions. This limit differs depending on the complexity and cardinality of the dimensions in question. It also depends on the number of facts or measures stored. Other MOLAP products can handle hundreds of dimensions.
- Some MOLAP methodologies introduce data redundancy.
Examples of commercial products that use MOLAP are Cognos Powerplay, Oracle Database OLAP Option, MicroStrategy, Microsoft Analysis Services, Essbase, TM1, Board Toolkit, Lilith Hicare and Daptech Keystone. There is also an open source MOLAP server Palo.
Not all MOLAP are generic databases. Some commercial products focus on a specific business function or type, such as Oracle© RPAS (Retail Predictive Application Server)
- Bach Pedersen, Torben; S. Jensen, Christian (December 2001). "Multidimensional Database Technology". Distributed Systems Online (IEEE): 40–46. ISSN 0018-9162.
- "Oracle© Retail Predictive Analysis Server User Guide", December 2012, Oracle© Corporation.