Data Reduction in Spatial-Temporal Databases
in spatial databases have allowed for the collection of huge amounts of data in
various GIS applications ranging from remote sensing and satellite telemetry
systems, to computer cartography and environmental planning. In addition,
majority of such collected data may also change through time, so we have two
aspects to be considered in these databases: spatial and temporal dimension. A
subfield of data mining that deals with the extraction of implicit knowledge and
spatial relationships not explicitly stored in spatial-temporal databases is
called spatial-temporal data mining or spatial-temporal knowledge discovery.
such situations, both the number and the size of spatial-temporal databases are
rapidly growing, and therefore the need for data reduction of very large spatial
databases is of fundamental importance for efficient spatial data analysis.
main purpose of the developed knowledge discovering software is to attempt to
reduce the size of spatial-temporal database through spatial statistical
analysis, spatial-temporal modeling and sensitivity analysis as well as through
identifying data subsets that are most likely to be reduced.