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Title
Systems for data processing, anaylsis and representation

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and no abstraction methods are provided to the
users. In addition most commercial systems
generally do not allow the query language to be
extended to capture the semantics of large objects.
Therefore new functions and operators defined by
users may not be able to use internal data indexing
and clustering techniques provided by the system to
manage BLOBs and make the user defined methods
or operators inefficient.
To make large object manageable within a GIS
environment, some new methods such as data
compression, data ordering, controlled data
redundancy, indexing and data browsing needs to be
investigated, evaluated and implemented based on
our data model and the possible GIS query
patterns. This paper will mainly discuss these
methods and present our implementation
strategies.
Using the object oriented data model, we have
implemented a visual based schema browser to
help users to define and modify the data base
schema[Zhou and Wilkinson, 1993]. An example of
the data base schema browser is shown in Figure 1.
5. LARGE SPATIAL OBJECT HANDLING
METHODS
Many current generation GISs have been designed
to effectively manage point, line, surface and their
attribute data. However, large spatial objects such
as remotely sensed image, scanned document and
DTM are series of large multidimensional arrays
With various temporal, spatial and spectral
characteristics. They are unformatted and quite
often require long delayed processing in order to
answer user's query or provide other useful
information. Interactive, network based GIS
queries may be very slow when large objects are
concerned. Therefore efficient support of large
objects must be designed into GISs.
In order to manage large spatial objects effectively
and efficiently, special techniques that allow
applications to quickly store and retrieve large
spatial object onto and from secondary memory
devices such as a disk need to be developed. The
traditional methods of storing large objects, such as
imagery is linear allocation where the data is laid
out linearly by a nested traversal of very
dimension in a predefined order. The method may
be optimized when the whole data of the large
object is needed. Due to the limitation of the
Current viewing system such as computer screen(e.g.
1000 by 1000 screen pixels vs. 20 by 20 K scanned
Map) and the nature of data usage(e.g. quite often
only a small part of data is used at a time), the
linear type data storage can be very inefficient in
many GIS applications. This situation will get
worse when the dimension and size of data
increase.
5.1 Data Compression
One area that needs to be investigated is how we
can reduce the amount of large object data while
keeping most GIS queries satisfied. One obvious
answer is data compression techniques such as
reducing data quantization level or resolution, or
using Run Length Encoding and JPEG compression
[Wallace, 1991]. These techniques can significantly
reduce the size of large objects and make large
objects more manageable in data bases.
The compression is one of the techniques for data
reduction. It takes advantage of patterns in image
data by removing redundancies and in effect,
squeezing the data to maximize the information
contained in each byte. Scanned maps, digital air
photos and satellite images are considered good
candidates for compression because of high
correlation in the image data. The compression can
be lossless(e.g. the decompressed data is identical
to the original, Run Length compression is an
example) or lossy (some information is lost. JPEG is
an example for this type of compression). Lossy
compression generally yields higher compression
ratios than lossless. For some GIS applications,
such as image display, this is a reasonable trade-
off. However, some applications will not allow the
lossy compression because of the precision
requirement.
Depending on the nature of image data, either
lossless or lossy compression may significantly
reduce the size of the image data to be managed.
For scanned maps, lossless compression typically
reduces the size by a factor of 2-10. This allows the
database system to transfer more information with
fewer bytes, thus making effective use of available
band width. This can be very significant for
network based GIS systems.
Another method to perform data reduction is to
reduce the data resolution and quantization level.
This technique can significantly reduce the amount
of data while satisfying most GIS queries. Table 1
gives the example of various data reduction
methods and their data sizes before and after the
data reduction. Since most GIS queries are "Gold
Mining” or “Landscape” type that do not require
detailed information in the first several queries,
this technique can be significant for GIS
applications.
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