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dimensions in the query, (2) the selection clause in the query can imply the selection in
the cuboid, and (3) the abstraction levels for the item and location dimensions in these
cuboids are at a finer level than brand and province or state, respectively.
“How would the costs of each cuboid compare if used to process the query?” It is likely
that using cuboid 1 would cost the most because both item name and city are at a lower
level than the brand and province or state concepts specified in the query. If there are
not many year values associated with items in the cube, but there are several item names
for each brand, then cuboid 3 will be smaller than cuboid 4, and thus cuboid 3 should
be chosen to process the query. However, if efficient indices are available for cuboid 4,
then cuboid 4 may be a better choice. Therefore, some cost-based estimation is required
to decide which set of cuboids should be selected for query processing.

4.4.4 OLAP Server Architectures: ROLAP versus MOLAP
versus HOLAP
Logically, OLAP servers present business users with multidimensional data from data
warehouses or data marts, without concerns regarding how or where the data are stored.
However, the physical architecture and implementation of OLAP servers must consider
data storage issues. Implementations of a warehouse server for OLAP processing include
the following:

Relational OLAP (ROLAP) servers: These are the intermediate servers that stand in
between a relational back-end server and client front-end tools. They use a rela-
tional or extended-relational DBMS to store and manage warehouse data, and OLAP
middleware to support missing pieces. ROLAP servers include optimization for
each DBMS back end, implementation of aggregation navigation logic, and addi-
tional tools and services. ROLAP technology tends to have greater scalability than
MOLAP technology. The DSS server of Microstrategy, for example, adopts the
ROLAP approach.
Multidimensional OLAP (MOLAP) servers: These servers support multidimensional
data views through array-based multidimensional storage engines. They map multi-
dimensional views directly to data cube array structures. The advantage of using a
data cube is that it allows fast indexing to precomputed summarized data. Notice
that with multidimensional data stores, the storage utilization may be low if the data
set is sparse. In such cases, sparse matrix compression techniques should be explored
(Chapter 5).
Many MOLAP servers adopt a two-level storage representation to handle dense
and sparse data sets: Denser subcubes are identified and stored as array struc-
tures, whereas sparse subcubes employ compression technology for efficient storage
utilization.
Hybrid OLAP (HOLAP) servers: The hybrid OLAP approach combines ROLAP and
MOLAP technology, benefiting from the greater scalability of ROLAP and the faster
computation of MOLAP. For example, a HOLAP server may allow large volumes