STING + : An Approach to Active Spatial Data

Spatial data mining presents new challenges due to the large size of spatial data, the complexity of spatial data types, and the special nature of spatial access methods. Most research in this area has focused on eecient query processing of static data. This paper introduces an active spatial data mining approach which extends the current spatial data mining algorithms to eeciently support user-deened triggers on dynamically evolving spatial data. To exploit the locality of the eeect of an update and the nature of spatial data, we employ a hierarchical structure with associated statistical information at the various levels of the hierarchy and decompose the user-deened trigger into a set of sub-triggers associated with cells in the hierarchy. Updates are suspended in the hierarchy until their cumulative eeect might cause the trigger to re. It is shown that this approach achieves three orders of magnitude improvement over the naive approach that re-evaluate the condition over the database for each update, while both approaches produce the same result without any delay. Moreover, this scheme can support incremental query processing as well.