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

26 2. Background and Related Work
(facts) are mapped at the intersection of a specific combination of dimensions.
Different attributes along each dimension are often organized in hierarchical structures
that determine the different levels in which data can be further analyzed [26].
For example, within the time dimension, one may have levels composed of years,
months, and days. Similarly, within the geography dimension, one may have levels
such as country, region, state/province, or city. Hierarchical structures are used to infer
summarization (aggregation), that is, whether an aggregate view (query) defined
for some category can be correctly derived from a set of precomputed views defined
for other categories.
Figure 2.7. OLAP Data Cube
2.2.2 OLAP Operations
OLAP includes a set of operations for manipulation of dimensional data organized
in multiple levels of abstraction. Basic OLAP operations are roll-up, drill-down, slice,
dice and pivot [44]. A roll-up (aggregation) operation computes higher aggregations
from lower aggregations or base facts according to their hierarchies, whereas drilldown
(disaggregation) is an analytic technique whereby the user navigates among
levels of data ranging from most summarized/aggregated, to most detailed. Typical
OLAP aggregate functions include average, maximum, minimum, count, and sum.
Drilling paths may be defined by the hierarchies within dimensions or other relationships
dynamic within or between dimensions. A slice consists of the selection of a
smaller data cube or even the reduction of a multidimensional data cube to fewer dimensions
by a point restriction in some dimension. The dice operation works similarly
to the slice except that it performs a selection on two or more dimensions. Figure 2.8
provides a graphical description of these operations.
2.2.3 OLAP Architectures
Figure 2.9 shows different approaches for the implementation of OLAP functionalities:
Multidimensional OLAP (MOLAP), Relational OLAP (ROLAP), Hybrid OLAP