This project develops data mining algorithms, both association rules and prediction, targeted to geospatial data. This GOALI research is performed in collaboration with the SIVAM project underway at Raytheon Systems Company. Raytheon Systems Company, Garland Division, has the responsibility for the hardware and software development of SIVAM (System for the Vigilance of the Amazon). SIVAM's objective is to implement a surveillance and analysis infrastructure, including a very large (multi terabyte) geospatial database and associated visualization tools that will provide the Brazilian Government with the necessary information for the protection and sustainable development of the Amazon region. This research focuses on an important aspect of the overall problem: development of new algorithms for a specific target application. The developed algorithms scale to the massive amounts of data present as well as adapt to the available amount of main memory. In the SIVAM project, data is obtained on an ongoing basis (as time advances). The prediction algorithms are used to predict environmental catastrophes (such as flooding or deforestation) and are incremental in nature. State information is kept which "remembers" previous environmental data collected. As new data arrives, the state is advanced based on the data found. In addition, these structures used to save this state information are modified as learning takes place. The results of this research will advance the field of data mining and provide enabling technologies for the environment research and management. www.seas.smu.edu/~mhd/dm.html
