Efficient and compact indexing structure for processing of spatial queries in line-based databases

Abstract

Points, lines and regions are the three basic entities for constituting vector-based objects in spatial databases. Many indexing schemes have been widely discussed for handling point or region data. These traditional schemes can efficiently organize point or region objects in a space into a hashing or hierarchical directory, and they provide efficient access methods for accurate retrievals. However, two difficulties arise when applying such methods to line segments: (1) the spatial information of line segments may not be precisely expressed in terms of that of points and/or regions, and (2) traditional methods for handling line segments can generate a large amount of dead space and overlapping areas in internal and external nodes in the hierarchical directory. The first problem impedes high-quality spatial conservation of line segments in a line-based database, while the second degrades the system performance over time. This study develops a novel indexing structure of line segments based on compressed B + trees. The proposed method significantly improves the time and space efficiencies over that of the R-tree indexing scheme.