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

22 2. Background and Related Work
on the output raster. Other resampling methods such as bilinear and cubic interpolation
consider a subset of cells to calculate each of the cell values in the output rasters.
Fig. 2.5 shows three common options for interpolating output cell values. Note that
the bold outline (center image) indicates the current target cell for which a value is
being interpolated.
(a) Portion of
original raster
(b) Portion of
output raster
(c) Input cells used by common
resampling methods
Figure 2.5. Nearest Neighbor, Bilinear and Cubic Interpolation Methods
A characteristic of the pyramid approach is that it increases the size of a raster
dataset by approximately 33 percent. This is because the additionally reduced resolution
representations are stored in the system together with the original dataset. This is
offset, however, by the increasing response time obtained in return. The choice of resampling
method for constructing the pyramid is influenced by the data characteristics
and type of analysis performed on the data. For example, visual appearance of remote
sensing imagery is best using nearest-neighbor resampling, whereas scientific interpretation
may require cubic interpolation. Rasters representing categorical data e.g.,
land use data, do not allow interpolation since it is important that original data values
remain unchanged; hence only nearest-neighbor resampling can be applied [64].
The reason why categorical data should not be interpolated is because intermediate
terms cannot be derived with meaningful results. For example, soil type data cannot
be interpolated since a soil type 14 and a soil type 15 cannot sensibly be averaged
to derive a soil type 14.5. Creating pyramids for different resampling methods is not
efficient due to the additional resources required for storage and maintenance. Thus,
the hard-wired resampling approach possess significant flexibility limitations to users
when analytic objectives diverge.
Fast retrieval of raster image datasets has also been investigated in distributed
database systems. Kitamoto [14] proposed a caching mechanism that allows twodimensional
satellite imagery to be cached with minimum resolution to provide a
coarse view of the images in distributed satellite image databases. The cache management
problem is treated as the knapsack problem [14], where the relevance and size
of the data is considered to determine if the data will be cached or not. Additionally,
access patterns influence the relevance of the data. The frequency of requests for a