MOLAP: Multidimensional OLAP
Multidimensional On-Line Analytical Processing (MOLAP) is the more traditional way of OLAP analysis, in which, data is stored in a multidimensional cube. This allows users to view different aspects of data aggregates such as sales by time period, geography, or product. The storage is not in a relational database. If the data is stored in a relational database, it can be viewed multidimensionally, but only by successively accessing and processing a table for each dimension. MOLAP processes data that is already stored in a multidimensional array in which all possible combinations of data are reflected, each in a cell that can be accessed directly.
MOLAP is more appropriate for cubes with frequent use and the necessity for rapid query response.
The chart below highlights advantages and disadvantages of MOLAP.
| Advantages |
Excellent performance since pre-aggregation provides quicker response time. |
| Availability of extensive libraries of complex functions for OLAP analyses. |
| Optimal for slice and dice operations. |
| Performs better than ROLAP when data is dense. |
| Disadvantages |
Usually more than 90% of cells are empty - issue with sparsity. |
| Limited in the amount of data it can handle, since all calculations are performed when the cube is built. Therefore, it is not commonly used above 20-50 GB - scalability problem. |
| Difficult to change dimension without re-aggregation. |
| Data must be copied and moved into data stores. |
| Originated from query tools, thereby lacking the architecture. |
| Requires additional investment since cube technology is often proprietary and does not already exist in organizations. |
| Lacks security and administration features which RDBMSs can bring. |
| Major Players |
Hyperion, Executive Viewer, CFO Vision, BI/Analyze, PowerPlay, Business Objects, Genita, Holos, MS OLAP Services, Pilot, ProCube |
Figure 1. MOLAP architecture |
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