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

2.1 Array Databases 19
toward the storage of large amounts of data on tertiary storage media considering
tiling techniques in multidimensional database management systems is presented in
[23, 24, 25].
Figure 2.3. Image Tiling
A key factor influencing the effectiveness of a tiling scheme is compression. Raster
data compression algorithms are the same as algorithms for compression of other image
data. However, remote-sensing images are usually of much higher resolution,
are multi-spectral and have significant larger volumes than natural images. To effectively
compress raster data in GIS environments, emphasis must be placed on the
management of schemas to deal with large volumes of remote-sensing imagery, and
on the integration of various types of datasets such as vector and multidimensional
datasets [3, 87].
Dehmel [3] proposed a comprehensive framework for the compression of multidimensional
arrays based on different model layers, including various kinds of predictors
and a generic wavelet engine for lossy compression with arbitrary quality levels.
In particular, the author introduces concepts such as channel separation to compress
values for each channel separately, and predictors that calculate approximate values
for some cells and express those cell values relative to the approximate values. Further,
the proposed method applies wavelets to transform the channels individually into
multi-resolution representations with coarse approximations and various levels of detail
information. This led to a wavelet engine architecture consisting of three major
components: transformation, quantization and compression that helps improve compression
rates considerably in array databases.
2.1.5 2D Pre-Aggregation
Aggregate operations on GIS and remote-sensing applications have been shown to
be computationally expensive due to the size and complexity of the operations [8].
One such operation is zooming (scaling), which is carried out by interpolating the values
of the original dataset to downsample it to a lower resolution. This is particularly
necessary in web-based raster applications, where limitations such as bandwidth and
other resources prevent the efficient processing of the original raster datasets. For
smooth interactive panning, browsers load the image in tiles and quantities larger than
actually displayed. Zooming far out results in large scale factors, meaning that large
amounts of data must be moved to deliver minimal results.