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This paper addresses the problem of finding the K closest pairs between two spatial data sets, where each set is stored in a structure belonging in the R-tree family. Five different algorithms (four recursive and one iterative) are presented for solving this problem. The case of 1 closest pair is treated as a special case. An extensive study, based on(More)
Efficient processing of distance-based queries (DBQs) is of great importance in spatial databases due to the wide area of applications that may address such queries. The most representative and known DBQs are the K Nearest Neighbors Query (KNNQ), q Distance Range Query (qDRQ), K Closest Pairs Query (KCPQ) and q Distance Join Query (qDJQ). In this paper, we(More)
This paper addresses the problem of finding the K closest pairs between two spatial da-tasets (the so called, K Closest Pairs Query, K-CPQ), where each dataset is stored in an R-tree. There are two different techniques for solving this kind of distance-based query. The first technique is the incremental approach, which returns the output elements one-by-one(More)
One of the most representative and studied Distance-Based Queries in Spatial Databases is the K-Closest-Pairs Query (KCPQ). This query involves two spatial data sets and a distance function to measure the degree of closeness, along with a given number K of elements of the result. The output is a set of pairs of objects (with one object element from each(More)
In modern database applications the similarity or dissimilar-ity of complex objects is examined by performing distance-based queries (DBQs) on data of high dimensionality. The R-tree and its variations are commonly cited multidimensional access methods that can be used for answering such queries. Although, the related algorithms work well for(More)
Let a tuple of n objects obeying a query graph (QG) be called the n-tuple. Thè`D distance-value'' of this n-tuple is the value of a linear function of distances of the n objects that make up this n-tuple, according to the edges of the QG. This paper addresses the problem of ®nding the K n-tuples between n spatial datasets that have the smallest D(More)
The family of R-trees is suitable for storing various kinds of multidimensional objects and is considered an excellent choice for indexing a spatial database. Region Quadtrees are suitable for storing 2-dimensional regional data and their linear variant is used in many Geographical Information Systems for this purpose. In this report, we present ve(More)
In this paper, the most appropriate buffer structure, page replacement policy and buffering scheme for closest pairs queries, where both spatial datasets are stored in R-trees, are investigated. Three buffer structures (i.e. single, hybrid and by levels) over two buffering schemes (i.e. local to each R-tree, and global to the query) using several page(More)
The development of location-based services and advances in the field of mobile computing have motivated an intensive research effort devoted to the efficient processing of location-dependent queries. In this context, it is usually assumed that location data are expressed at a fine geographic precision. Adding support for location granules means that the(More)