Efficient processing of direction joins using R-trees

Spatial joins are one of the most important operations in spatial databases. A direction join is a spatial join with direction predicates as join condition. In recent years, the efficient processing of direction joins has gradually gained attention in geospatial databases applications, such as spatial data mining and GIS. Until now, the research work on processing of spatial joins has primarily focused on topological relations, such as intersection, and distance relations. There is little work on processing joins with direction relations. This paper presents an efficient method for processing direction joins using R-trees. The quad-tuples model is defined to represent direction relations between the minimum bounding rectangles of spatial objects. An algorithm of processing the filter step of joins using R-trees is given and the refinement step processing is further decomposed into three different operations. The method presented can efficiently process direction joins between objects of any data types in 2D space. Performance evaluation of the proposed method is conducted using real world datasets and the experiment results show that it performs well with respect to both, I/O- and CPU-time.