Abstract
The family of R-trees is suitable for indexing various kinds of multidimensional objects. TPR*-trees are R-tree based structures that have been proposed for indexing a moving object database, e.g. a data-base of moving boats. Region Quadtrees are suitable for indexing 2-dimensional regional data and their linear variant (Linear Region Quadtrees) is used in many Geographical Information Systems (GIS) for this purpose, e.g. for the representation of stormy, or sunny regions. Although, both are tree structures, the organization of data space, the types of spatial data stored and the search algorithms applied on them are different in R-trees and Region Quadtrees. In this paper, we examine a spatio-temporal problem that appears in many practical applications: processing of predictive joins between moving objects and regions (e.g. discovering the boats that will enter a storm), using these two families of data structures as storage and indexing mechanisms, and taking into account their similarities and differences. With a thorough experimental study, we show that the use of a synchronous Depth-First traversal order has the best performance balance (on average), taking into account the I/O activity and response time as performance measurements.
Supported by the Almacenes de Datos Espacio-Temporales basados en Ontologias project (TIN2005-09098-C05-03), funded by the Spanish Ministry of Science and Technology.
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Corral, A., Torres, M., Vassilakopoulos, M., Manolopoulos, Y. (2008). Predictive Join Processing between Regions and Moving Objects. In: Atzeni, P., Caplinskas, A., Jaakkola, H. (eds) Advances in Databases and Information Systems. ADBIS 2008. Lecture Notes in Computer Science, vol 5207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85713-6_5
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