Multi-Way Distance Join Queries in Spatial Databases

  • Authors:

  • Antonio Corral;Yannis Manolopoulos;Yannis Theodoridis;Michael Vassilakopoulos

  • Affiliations:

  • Department of Languages and Computation, University of Almeria, 04120 Almeria, Spain acorral@ual.es;Department of Informatics, Aristotle University, GR-54006 Thessaloniki, Greece manolopo@csd.auth.gr;Department of Informatics, University of Piraeus, GR-18534 Piraeus, Greece ytheod@unipi.gr;Department of Informatics, TEI of Thessaloniki, GR-54101, Thessaloniki, Greece vasilako@it.teithe.gr

  • Venue:
  • Geoinformatica
  • Year:
  • 2004

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Abstract

Let a tuple of n objects obeying a query graph (QG) be called the n-tuple. The “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 finding the K n-tuples between n spatial datasets that have the smallest D_distance-values, the so-called K-multi-way distance join query (K-MWDJQ), where each set is indexed by an R-tree-based structure. This query can be viewed as an extension of K-closest-pairs query (K-CPQ) [8] for n inputs. In addition, a recursive non-incremental branch-and-bound algorithm following a depth-first search for processing synchronously all inputs without producing any intermediate result is proposed. Enhanced pruning techniques are also applied to n R-trees nodes in order to reduce the total response time and the number of distance computations of the query. Due to the exponential nature of the problem, we also propose a time-based approximate version of the recursive algorithm that combines approximation techniques to adjust the quality of the result and the global processing time. Finally, we give a detailed experimental study of the proposed algorithms using real spatial datasets, highlighting their performance and the quality of the approximate results.