Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University
Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University
Applying OLAP Pre-Aggregation Techniques to ... - Jacobs University
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2.1 Array Databases 17<br />
Figure 2.2 shows a graphical classification of grid functions according <strong>to</strong> map algebra.<br />
Figure 2.2. Map Algebra Functions<br />
Map algebra is primarily oriented <strong>to</strong>ward 2D static data. Each layer is associated<br />
with a particular moment or period of time, and analytical operations are intended <strong>to</strong><br />
deal with spatial relationships. In its original form, map algebra was never intended<br />
<strong>to</strong> handle spatial data with a temporal component.<br />
2.1.3 Multidimensional Data Models<br />
AQL<br />
Libkin et al. [63] presented an array data model called AQL that embeds array support<br />
in<strong>to</strong> specific nested relational calculus and treats arrays as functions rather than<br />
collection types. The AQL data model combines complex objects such as sets, bags,<br />
and lists with multidimensional arrays. To express complex object values, the core<br />
calculus on which AQL is based has been extended with concepts such as comprehensions,<br />
pattern matching, and block structures that strengthen the expressive power of<br />
the language. Still, AQL does not provide a declarative mechanism <strong>to</strong> define the order<br />
in which queries manipulate data.<br />
Array Manipulation Language (AML)<br />
AML is a query language for multidimensional array data [80]. The model is aimed<br />
<strong>to</strong>wards applications in image databases, particularly for remote sensing, but it is cus<strong>to</strong>mizable<br />
<strong>to</strong> support a wide variety of application domains. An interesting characteristic<br />
of this language is the use of bit patterns, an array indexing mechanism that<br />
allows for a more powerful access structure <strong>to</strong> arrays. AML’s algebra consists of three<br />
opera<strong>to</strong>rs that enable the manipulation of arrays: subsample, merge, and apply. Each<br />
opera<strong>to</strong>r takes one or more arrays as arguments, and produces an array as result. Subsample<br />
is a unary opera<strong>to</strong>r that eliminates cells from an array by cutting out slices.<br />
Merge is a binary opera<strong>to</strong>r that combines two arrays defined over the same domain.<br />
The Apply opera<strong>to</strong>r applies a user-defined function <strong>to</strong> an array, thereby producing a<br />
new array. All AML opera<strong>to</strong>rs take bit patterns as parameters.