Abstract not found

attention has been focused on spatial databases which combine conventional and spatially related data such as Geographic Information Systems, CAD/CAM, or VLSI. A language has been developed to query such spatial databases. It recognizes the significantly different requirements of spatial data handling and overcomes the inherent problems of the application of conventional database query languages. The spatial query language has been designed as a minimal extension to the interrogative part of SQL and distinguishes from previously designed SQL extensions by (1) the preservation of SQL concepts, (2) the highlevel treatment of spatial objects, and (3) the incorporation of spatial operations and relationships. It consists of two components, a query language to describe what information to retrieve and a presentation language to specify how to display query results. Users can ask standard SQL queries to retrieve non-spatial data based on non-spatial constraints, use Spatial SQL commands to inquire about situations involving spatial data, and give instructions in the Graphical Presentation Language GPL to manipulate or examine the graphical presentation. 1 Index Terms—Geographic Information Systems, graphical presentation, query

This paper presents dedale, a spatial database system intended to overcome some limitations of current systems by providing an abstract and non-specialized data model and query language for the representation and manipulation of spatial objects. dedale relies on a logical model based on linear constraints, which generalizes the constraint database model of [KKR90]. While in the classical constraint model, spatial data is always decomposed into its convex components, in dedale holes are allowed to fit the need of practical applications. The logical representation of spatial data although slightly more costly in memory, has the advantage of simplifying the algorithms. dedale relies on nested relations, in which all sorts of data (thematic, spatial, etc.) are stored in a uniform fashion. This new data model supports declarative query languages, which allow an intuitive and efficient manipulation of spatial objects. Their formal foundation constitutes a basis for practical query optimizati...

Spatial data types or algebras for database systems should (1) be fully general, that is, closed under set operations, (2) have formally defined semantics, (3) be defined in terms of finite representations available in computers, (4) offer facilities to enforce geometric consistency of related spatial objects, and (5) be in-dependent of a particular DBMS data model, but cooperate with any. We present an algebra that uses realms as geometric domains underlying spatial data types. A realm, as a general database concept, is a finite, dynamic, user-defined structure underlying one or more system data types. Problems of numerical robustness and topological correctness are solved within and below the realm layer so that spatial algebras defined above a realm have very nice algebraic properties. Realms also interact with a DMBS to enforce geometric consistency on object creation or up-date. The ROSE algebra is defined on top of realms and offers general types to represent point, line, and region features, together with a comprehensive set of operations. It is described within a polymorphic type system and interacts with a DMBS data model and query language through an abstract object model interface. An example integration of ROSE into the object-oriented data model 02 and its query language is presented.

The data model upon which most of today's commercial database management systems are based has shown to be insufficient for geographic information systems (GISs). The recently promoted object-oriented model provides some useful tools for data abstraction and data structuring, which augment the conventional tools and overcomes some deficiencies inherent to the traditional relational model. In particular, the concepts of complex objects with pertinent operations are more powerful modeling methods than the currently popular structure of relational tables and relational algebra. This survey article presents the concepts of object-oriented modeling applied to geographic data and demonstrates their impact on future GISs.

4-D/RCS is the reference model architecture currently being developed for the Demo III Experimental Unmanned Vehicle program. 4-D/RCS integrates the NIST (National Institute of Standards and Technology) RCS (Real-time Control System) with the German (Universitat der Bundeswehr Munchen) VaMoRs 4-D approach to dynamic machine vision. The 4-D/RCS architecture consists of a hierarchy of computational nodes each of which contains behavior generation (BG), world modeling (WM), sensory processing (SP), and value judgment (VJ) processes. Each node also contains a knowledge database (KD) and an operator interface. These computational nodes are arranged such that the BG processes represent organizational units within a command and control hierarchy.

Abstract. Although maps and partitions are ubiquitous in geographical information systems and spatial databases, there is only little work investigating their foundations. We give a rigorous definition for spatial partitions and propose partitions as a generic spatial data type that can be used to model arbitrary maps and to support spatial analysis. We identify a set of three powerful operations on partitions and show that the type of partitions is closed under them. These basic operators are sufficient to express all known application-specific operations. Moreover, many map operations will be considerably generalized in our framework. We also indicate that partitions can be effectively used as a meta-model to describe other spatial data types. 1

Spatial data mining, i.e., mining knowledge from large amounts of spatial data, is a demanding field since huge amounts of spatial data have been collected in various applications, ranging from remote sensing to geographical information systems (GIS), computer cartography, environmental assessment and planning. The collected data far exceed people's ability to analyze it. Thus, new and efficient methods are needed to discover knowledge from large spatial databases. The goal of this thesis is to analyze methods for mining of spatial data, and to determine environments in which efficient spatial data mining methods can be implemented. In the spatial data mining process, we use (1) non-spatial properties of the spatial objects and (2) attributes, predicates and functions describing spatial relations between described objects and other features located in the spatial proximity of the described objects. The descriptions are generalized, transformed into predicates, and the discovered knowle...

Given a collection S of n line segments in the plane, the planar point location problem is to construct a data structure that can efficiently determine for a given query point p the first segment(s) in S intersected by vertical rays emanating out from p. It is well known that linear-space data structures can be constructed so as to achieve O(log n) query times. But applications, such as those common in geographic information systems, motivate a re-examination of this problem with the goal of improving query times further while also simplifying the methods needed to achieve such query times. In this paper we perform such a re-examination, focusing on the issues that arise in three different classes of pointlocation query sequences: ffl sequences that are reasonably uniform spatially and temporally (in which case the constant factors in the query times become critical), ffl sequences that are non-uniform spatially or temporally (in which case one desires data structures that adapt to s...

Spatial databases have been an active area of research for over two decades, addressing the growing data management and analysis needs of spatial applications such as Geographic Information Systems. This research has produced a taxonomy of models for space, spatial data types and operators, spatial query languages and processing strategies, as well as spatial indexes and clustering techniques. However, more research is needed to improve support for network and eld data, as well as query processing (e.g. cost models, bulk load). Another important need is to apply the spatial data management accomplishments to newer applications such as data warehouses and multimedia information systems. The objective of this paper is to identify recent accomplishments and the research needs in the near term.