Integrating spatial decision support system with graph mining technique

Abstract

Large scale crop productivity requires current and accurate information. The choice of a remotely spatial data source entails tradeoffs between cost and accuracy in a timely manner. A primary challenge to large-scale data integration is creating heterogeneous reliability data from different data sources to the same real-world entity. Spatial Decision Support System for agricultural sector can play an important role to facilitate users requiring a large amount of information that must be easily accessible. A fast developing trend in agropedagogical scenario analysis is the combination of multiple association data into coherent interaction networks to enable integrated scenario analysis. This paper explores data integration for spatial data sets using graph mining approach during the development of a prototype spatial DSS as a support tool for the farm manager. The results of mining spatial association rules could be implemented to explore alternative states of the environment and policy options to correlate key parameters conducted and better knowledge discovery. © 2012 Springer-Verlag Berlin Heidelberg.