Term/Acronym dust-off

KPI for Key Performance Indicator

ROLAP - Relational Online Analytical Processing.

ROLAP tools access the data in a relational database and generate SQL queries to calculate information at the appropriate level when an end user requests it.


Multidimensional OLAP, or MOLAP:

Multidimensional structure is defined as “a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data” (O'Brien & Marakas, 2009, pg 177). 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” (pg. 178).


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 model provides natural indexing
  • Effective data extract achieved through the pre-structuring of aggregated data.

Disadvantages of MOLAP

  • 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 has 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.
  • MOLAP approach introduces data redundancy.