Bayesian Nearest Neighbor (BNN) queries are normally used in GIS and CAD/CAM applications to identify the nearest spatial objects closest to some given query points in spatial database. Most previous BNN search assumed that the spatial databases to be queried are local and the processing of queries is a tedious process. To make the BNN search more effective, the previous work introduced BNN search based on Marginal Object Weight ranking scheme. Based on events occurring in the nearest object, BNN starts its search using MOW. The MOW is done by computing the weight of each NN objects and rank each object based on its frequency and distance of NN object for an efficient NN search in spatial databases.