VLDBJournal,3, 479-516 (1994), Ralf Hartmut Gfiting, Editor 479 QVLDB Qualitative Representation of Spatial Knowledge in Two-Dimensional Space Dimitris Papadias and Timos Sellis Received July 5, 1993; revised version received April 6, 1994; accepted May 20, 1994. Abstract. Various relation-based systems, concerned with the qualitative repre- sentation and processing of spatial knowledge, have been developed in numer- ous application domains. In this article, we identify the common concepts under- lying qualitative spatial knowledge representation, we compare the representa- tional properties of the different systems, and we outline the computational tasks involved in relation-based spatial information processing. We also describe sym- bolic spatial indexes, relation-based structures that combine several ideas in spatial knowledge representation. A symbolic spatial index is an array that preserves only a set of spatial relations among distinct objects in an image, called the modeling space; the index array discards information, such as shape and size of objects, and irrelevant spatial relations. The construction of a symbolic spatial index from an input image can be thought of as a transformation that keeps only a set of repre- sentative points needed to define the relations of the modeling space. By keeping the relative arrangements of the representative points in symbolic spatial indexes and discarding all other points, we maintain enough information to answer queries regarding the spatial relations of the modeling space without the need to access the initial image or an object database. Symbolic spatial indexes can be used to solve problems involving route planning, composition of spatial relations, and update operations. Key Words. Spatial data models, spatial query languages, representation of direc- tion and topological relations, qualitative spatial information processing. 1. Introduction The term spatial knowledge refers to configurations among distinct spatial entities (i.e., spatial representations preserve location in space without incorporating information such as shape, size, texture, or color of objects; Glasgow and Papadias, 1992). As an Dimitris Papadias, M.Sc., is a Ph.D. candidate, and Timos Sellis, Ph.D., is Associate Professor, Computer Science Division, Department of Electrical and Computer Engineering, National Technical University of Athens, Greece 15780.