Adaptive Recursive Tessellations (ART) is a conceptual and generalized framework for a series of hierarchical tessellation models characterized by a variable decomposition ratio and rectangular cells. ART offers more flexibility in cell size and shape than the quadtree which is constrained by its fixed 1:4 decomposition ratio and square cells. Thus the variable resolution storage characteristic of the hierarchical tessellations can be fully utilized. A data structure for the implementation of the ART, called Adaptive Recursive Run-Encoding (ARRE), is proposed. Then a spatial database management system specially for ART, the Tessellation Manager, is constructed based on the ARRE. Space efficiency analysis of three ART models are conducted using the Tessellation Manager. The result shows that ART models have similar space efficiency with the quadtree model. ART also has many potential applications in GIS and is suitable as a spatial data model for raster GIS.