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 The client addresses the tree through its image that the splits can make outdated.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 Specific messages towards the clients incrementally correct the outdated images.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.
Many applications rely on sequence databases and use extensively pattern-matching queries to retrieve data of interest. This paper extends the traditional pattern-matching expressions to parameterized patterns, featuring variables. Parameterized patterns are more expressive and allow to define concisely regular expressions that would be very complex to describe without variables. They can also be used to express additional constraints on patterns' variables.We show that they can be evaluated without additional cost with respect to traditional techniques (e.g., the KnuthMorris-Pratt algorithm). We describe an algorithm that enjoys low memory and CPU time requirements, and provide experimental results which illustrate the gain of the optimized solution.
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