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

Chapter 6
Conclusion
One of the biggest challenges of database technology is to effectively and efficiently
provide solutions for extremely large volumes of multidimensional array data archiving
and management. This thesis focuses on investigating the problem of applying
OLAP pre-aggregation technology to speed up aggregate query processing in array
databases for GIS and remote-sensing imaging applications.
We presented a study of fundamental imaging operations in GIS. By using a formal
algebraic framework, Array Algebra, we were able to classify GIS operations
according to three basic algebraic operators, and thus identify a set of operations that
can benefit from pre-aggregation techniques. We argued that OLAP pre-aggregation
techniques cannot be applied in a straight-forward manner to array databases for our
target applications. The reason is that although similar, data structures in both application
domains differ in fundamental aspects. In OLAP, multidimensional data spaces
are spanned by axes where cell values sit on the grid at intersection points. This
is paralleled by raster image data that are discretized during acquisition. Thus, the
structure of an OLAP data cube is rather similar to a raster array. Dimension hierarchies
in OLAP serve to group value ranges along an axis. Querying data by referring
to coordinates on the measure axes yields ground data, whereas queries using axes
higher up in a dimension hierarchy will return aggregated values. A main differentiating
criterion between data in OLAP and raster image data is density: OLAP data
are sparse, typically 5%, whereas, raster image datasets are 100% dense. Note also
that dimensions in OLAP are treated as business perspectives such as products and/or
stores, and these are non-spatial dimensions, which contrast with the spatial nature of
raster image datasets. There are, however, core similarities that motivated us to further
research OLAP pre-aggregation techniques. For example, we observed that array
databases and OLAP systems both employ multidimensional data models to organize
their data. Also, the operations convey a high degree of similarity: a roll-up (aggregate)
operation in OLAP is very similar to a scaling operation in a raster domain.
Moreover, both application domains make use of pre-aggregation approaches to speed
up query processing, however, each has different levels of maturity and scalability.
We presented a framework that focuses on computing basic aggregate operations
using pre-aggregated data. We argued that the decision of computing an aggregate
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