The paper is a overview of the major qualitative spatial representation and reasoning techniques. We survey the main aspects of the representation of qualitative knowledge including ontological aspects, topology, distance, orientation and shape. We also consider qualitative spatial reasoning including reasoning about spatial change. Finally there is a discussion of theoretical results and a glimpse of future work. The paper is a revised and condensed version of [33, 34].

Spatial-Query-by-Sketch is the design of a query language for geographic information systems. It allows a user to formulate a spatial query by drawing the desired configuration with a pen on a touch-sensitive computer screen and translates this sketch into a symbolic representation that can the processed against a geographic database. Since the configurations queried usually do not match exactly the sketch, it is necessary to relax the spatial constraints drawn. This paper describes the representation of a sketch and outlines the design of the constraint relaxation methods used during query processing.

The 4-intersection, a model for the representation of topological relations between 2-dimensional objects with connected boundaries and connected interiors, is extended to cover topological relations between 2-dimensional objects with arbitrary holes, called regions with holes. Each region with holes is represented by its generalized region---the union of the object and its holes--- and the closure of each hole. The topological relation between two regions with holes, A and B, is described by the set of all individual topological relations between (1) A 's generalized region and B's generalized region, (2) A 's generalized region and each of B's holes, (3) B's generalized region with each of A 's holes, and (4) each of A 's holes with each of B's holes. As a side product, the same formalism applies to the description of topological relations between 1-spheres. An algorithm is developed that minimizes the number of individual topological relations necessary to describe a configuration completely. This model of representing complex topological relations is suitable for a multi-level treatment of topological relations, at the least detailed level of which the relation between the generalized regions prevails. It is shown how this model applies to the assessment of consistency in multiple representations when, at a coarser level of less detail, regions are generalized by dropping holes.

A new issue that arises in modern applications involves the efficient manipulation of (static or moving) spatial objects, and the relationships among them. As a result, modern database systems should be able to efficiently support that type of data. Towards this goal, appropriate extensions of multidimensional access methods can be exploited in order to index and retrieve spatiotemporal objects, satisfying users' demands. This paper introduces the basic specifications such a spatiotemporal index structure should follow, evaluates existing proposals with respect to the above specifications, and illustrates issues of interest involving object representation, query processing, and index maintenance.

The standard mathematical approaches to topology, point-set topology and algebraic topology, treat points as the fundamental, undefined entities, and construct extended spaces as sets of points with additional structure imposed on them. Point-set topology in particular generalises the concept of a `space' far beyond its intuitive meaning. Even algebraic topology, which concentrates on spaces built out of `cells' topologically equivalent to n-dimensional discs, concerns itself chiefly with rather abstract reasoning concerning the association of algebraic structures with particular spaces, rather than the kind of topological reasoning which is required in everyday life, or which might illuminate the metaphorical use of topological concepts such as `connection' and `boundary'. This paper explores an alternative to these approaches, RCC theory, which takes extended spaces (`regions') rather than points as fundamental. A single relation, C (x; y) (read `Region x connects with reg...

Spatial relations are important in numerous domains, such as Spatial Query Languages, Image and Multimedia Databases, Reasoning and Geographic Applications. This paper is concerned with the retrieval of topological and direction relations using spatial data structures based on Minimum Bounding Rectangles. We describe topological and direction relations between region objects and we study the spatial information that Minimum Bounding Rectangles convey about the actual objects they enclose. Then we apply the results in R-trees and their variations, R+-trees and R*- trees, in order to minimise the number of disk accesses for queries involving topological and direction relations. We also investigate queries that express complex spatial conditions in the form of disjunctions and conjunctions, and we discuss possible extensions.

AbstractÐSelection and join queries are fundamental operations in Data Base Management Systems (DBMS). Support for nontraditional data, including spatial objects, in an efficient manner is of ongoing interest in database research. Toward this goal, access methods and cost models for spatial queries are necessary tools for spatial query processing and optimization. In this paper, we present analytical models that estimate the cost (in terms of node and disk accesses) of selection and join queries using R-treebased structures. The proposed formulae need no knowledge of the underlying R-tree structure(s) and are applicable to uniform-like and nonuniform data distributions. In addition, experimental results are presented which show the accuracy of the analytical estimations when compared to actual runs on both synthetic and real data sets. Index TermsÐSpatial databases, access methods, query optimization, cost models, R-trees. 1

Similarity is the assessment of deviation from equivalence. Spatial similarity is complex due to the numerous constraining properties of geographic objects and their embedding in space. Among these properties, the spatial relations between geographic objects---topological, directional, and metrical---are critical, because they capture the essence of a scene's structure. These relations can be categorized as a basis for similarity assessment. This paper describes a computational method to formally assess the similarity of spatial scenes based on the ordering of spatial relations. One scene is transformed into another through a sequence of gradual changes of spatial relations. The number of changes required yields a measure that is compared against others, or against a pre-existing scale. Two scenes that require a large number of changes are less similar than scenes that require fewer changes.

We study topological queries over two-dimensional spatial databases. First, we show that the topological properties of semi-algebraic spatial regions can be completely specified using a classical finite structure, essentially the embedded planar graph of the region boundaries. This provides an invariant characterizing semi-algebraic regions up to homeomorphism. All topological queries on semi-algebraic regions can be answered by queries on the invariant whose complexity is polynomially related to the original. Also, we show that for the purpose of answering topological queries, semi-algebraic regions can always be represented simply as polygonal regions. We then study query languages for topological properties of two-dimensional spatial databases, starting from the topological relationships between pairs of planar regions introduced by Egenhofer. We show that the closure of these relationships under appropriate logical operators yields languages which are complete for topological prope...

Semantic similarity plays an important role in geographic information systems as it supports the identification of objects that are conceptually close, but not identical. Similarity assessments are particularly important for retrieval of geospatial data in such settings as digital libraries, heterogeneous databases, and the World Wide Web. Although some computational models for semantic similarity assessment exist, these models are typically limited by their inability to handle such important cognitive properties of similarity judgments as their inherent asymmetry and their dependence on context. This paper defines the Matching-Distance Similarity Measure (MDSM) for determining semantic similarity among spatial entity classes, taking into account the distinguishing features of these classes (parts, functions, and attributes) and their semantic interrelations (is-a and part-whole relations). A matching process is combined with a semantic-distance calculation to obtain asymmetric values of similarity that depend on the degree of generalization of entity classes. MDSM's matching process is also driven by contextual considerations, where the context determines the relative importance of distinguishing features. Based on a human-subject experiment, MDSM results correlate well with people's judgments of similarity. When contextual information is used for determining the importance of distinguishing features, this correlation increases; however, the major component of the correlation between MDSM results and people's judgments is due to a detailed definition of entity classes.