There is also an open source MOLAP server Palo.

MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database.

* 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.

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.

* HOLAP encompasses a range of solutions that attempt to mix the best of ROLAP and MOLAP.

* Online Analytical Processing, commonly known as OLAP ( including HOLAP, ROLAP and MOLAP )-a capability of some management, decision support, and executive information systems that supports interactive examination of large amounts of data from many perspectives.

MOLAP is an alternative to the ROLAP ( Relational OLAP ) technology.

* There is a consensus in the industry that ROLAP tools have slower performance than MOLAP tools.

* ROLAP relies on the general purpose database for querying and caching, and therefore several special techniques employed by MOLAP tools are not available ( such as special hierarchical indexing ).

In the OLAP industry ROLAP is usually perceived as being able to scale for large data volumes, but suffering from slower query performance as opposed to MOLAP.

The OLAP Survey, the largest independent survey across all major OLAP products, being conducted for 6 years ( 2001 to 2006 ) have consistently found that companies using ROLAP report slower performance than those using MOLAP even when data volumes were taken into consideration.

* The survey shows that ROLAP tools have 7 times more users than MOLAP tools within each company.

The undesirable trade-off between additional ETL cost and slow query performance has ensured that most commercial OLAP tools now use a " Hybrid OLAP " ( HOLAP ) approach, which allows the model designer to decide which portion of the data will be stored in MOLAP and which portion in ROLAP.

HOLAP ( Hybrid Online Analytical Processing ) is a combination of ROLAP ( Relational OLAP ) and MOLAP ( Multidimensional OLAP ) which are other possible implementations of OLAP.

HOLAP allows storing part of the data in a MOLAP store and another part of the data in a ROLAP store, allowing a tradeoff of the advantages of each.

In this mode HOLAP stores aggregations in MOLAP for fast query performance, and detailed data in ROLAP to optimize time of cube processing.

In this mode HOLAP stores some slice of data, usually the more recent one ( i. e. sliced by Time dimension ) in MOLAP for fast query performance, and older data in ROLAP.

Moreover, we can store some dices in MOLAP and others in ROLAP, leveraging the fact that in a large cuboid, there will be dense and sparse subregions.

The hybrid OLAP approach combines ROLAP and MOLAP technology, benefiting from the greater scalability of ROLAP and the faster computation of MOLAP.

' MOLAP ' is the ' classic ' form of OLAP and is sometimes referred to as just OLAP.

MOLAP stands for Multidimensional Online Analytical Processing.

* Within some MOLAP Solutions the processing step ( data load ) can be quite lengthy, especially on large data volumes.

* 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.

Other MOLAP products can handle hundreds of dimensions.

* Some MOLAP methodologies introduce data redundancy.

Many commercial OLAP tools now use a " Hybrid OLAP " ( HOLAP ) approach, which allows the model designer to decide which portion of the data will be stored in MOLAP.

Examples of commercial products that use MOLAP are Cognos Powerplay, Oracle Database OLAP Option, Microsoft Analysis Services, Essbase, TM1, Lilith Hicare and Daptech Keystone.

These SQL improvements can mitigate the benefits of the MOLAP tools.

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