Data Reduction in Spatial-Temporal Databases

valbul1a.gif (686 bytes)  Investigators

    Lazarevic Aleksandar
    Obradovic Zoran
    Pokrajac Dragoljub

valbul1a.gif (686 bytes)  Problem

    Advances 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. 
    In 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.


valbul1a.gif (686 bytes)  Software System

    The 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.