This paper presents DEDALE, a spatial database syst,cm irltended 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 [KKRSO]. 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 pract,ical applications.The logical representation of spatial dat,il although slightly more costly in memory, has the advantage of simplifying the algorithms. DEUALE relics on nest,c,tl rrlations, in which all sorts of data (thematic, spst,ial, etc.) arc stored in a uniform fashion. This new d&a model supports declarative query languages, which allow an intuitive and efficient manipulation of spatial objects. Their forma1 foundation constitutes a basis for practical query optimization. We describe several evaluation rules tailored for geometric data and give the specification of an optimizer module for spatial queries. Except for the latter module, the system has been fully implemented upon the 02 DBMS, thus proving the effectiveness of a constraint-based approach for the design of spatial database systems
We propose a scalable distributed data structure (SDDS) called SD-Rtree. We intend our structure for large spatial datasets over clusters of interconnected servers. SD-Rtree generalizes the well-known Rtree structure. It uses a distributed balanced binary spatial tree that scales with insertions to potentially any number of storage servers through splits of the overloaded ones. A user/application manipulates the structure from a client node. The client addresses the tree through its image that the splits can make outdated. This may generate addressing errors, solved by the forwarding among the servers. Specific messages towards the clients incrementally correct the outdated images. We present the build-up of an SD-Rtree through insertions, focusing on the split and balancing algorithms. We follow with the point and window query algorithms. Experiments show additional aspects of SD-Rtree behavior. The results justify our various design choices and the overall utility of the structure.
We present a data model for tracking mobile objects and reporting the result of queries. The model relies on a discrete view of the spatio-temporal space, where the 2D space and the time axis are respectively partitioned in a finite set of user-defined areas and in constant-size intervals. We define a generic query language to retrieve objects that match mobility patterns describing a sequence of moves. We also identify a subset of restrictions to this language in order to express only deterministic queries for which we discuss evaluation techniques to maintain incrementally the result of queries. The model is conceptually simple, efficient, and constitutes a practical and effective solution to the problem of continuously tracking moving objects with sequence queries.
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