Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University

28 2. Background and Related Work
aggregate queries [68]. One difficulty that MOLAP poses, however, pertains to the
sparseness of the data. Sparseness means that many events did not take place and
valuable processing time is taken by adding up zeros [91]. For example, a company
may not sell every item every day in every store, so no values appear at the intersection
where products are not sold in a particular region at a particular time. On the other
hand, MOLAP can be much faster for applications where subsets of the data cube
are dense [100]. Another limitation of this approach is that the computation of a
cube requires a complex aggregate query across all data in a warehouse. Though
it is possible to incrementally update cubes as new data arrives, it is impractical to
dynamically create new cubes to answer ad-hoc queries [68].
Figure 2.10. MOLAP Storage Scheme
ROLAP
In ROLAP, underlying data is stored in a relational database, see Fig. 2.11(a). The
relational model, however, does not include concepts of dimension, measure, and hierarchy.
Thus specific types of schemata must be created so the multidimensional
model can be represented in terms of basic relational elements such as attributes, relations,
and integrity constraints [68]. Such representations are done using a star schema
data model, although the snowflake schema is also often adopted.
ROLAP implementations can handle large amounts of data and leverage all functionalities
of the relational database [72]. Disadvantages are that overall performance
is slow and each ROLAP report represents an SQL query with the limitations of the
genre. ROLAP vendors tried to mitigate this problem by including out-of-the-box
complex functions in their product offering and providing users the capability of defining
their own functions. Another problem with ROLAP implementations results from
the performance hit caused by costly join operations between large tables [68]. To
overcome this issue, fact tables in data-warehouses are usually de-normalized. Sub-