On Spatial-Range Closest-Pair Query

An important query for spatial database research is to find the closest pair of objects in a given space. Existing work assumes two objects of the closest pair come from two different data sets indexed by R-trees. The closest pair in the whole space will be found via an optimzed R-tree join technique. However, this technique doesn’t perform well when the two data sets are identical. And it doesn’t work when the search range is some area other than the whole space. In this paper, we address the closest pair problem within the same data set. Further more, we propose a practical extension to the closest pair problem to involve a query range. The problem now becomes finding the closest pair of objects among those inside a given range. After extending the existing techniques to solve the new problem, we proposed two index structures based on augmenting the R-tree and we also give algorithms for maintaining these structrures. Experimental results show that our structures are more robust than earlier approaches.

[1]  Divyakant Agrawal,et al.  Reverse Nearest Neighbor Queries for Dynamic Databases , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[2]  King-Ip Lin,et al.  An index structure for efficient reverse nearest neighbor queries , 2001, Proceedings 17th International Conference on Data Engineering.

[3]  Hanan Samet,et al.  Incremental distance join algorithms for spatial databases , 1998, SIGMOD '98.

[4]  Nick Roussopoulos,et al.  K-Nearest Neighbor Search for Moving Query Point , 2001, SSTD.

[5]  Hans-Peter Kriegel,et al.  Optimal multi-step k-nearest neighbor search , 1998, SIGMOD '98.

[6]  Dimitrios Gunopulos,et al.  Nearest Neighbor Queries in a Mobile Environment , 1999, Spatio-Temporal Database Management.

[7]  Nick Roussopoulos,et al.  Nearest neighbor queries , 1995, SIGMOD '95.

[8]  S. Muthukrishnan,et al.  Influence sets based on reverse nearest neighbor queries , 2000, SIGMOD '00.

[9]  King-Ip Lin,et al.  An index structure for improving closest pairs and related join queries in spatial databases , 2002, Proceedings International Database Engineering and Applications Symposium.

[10]  Hanan Samet,et al.  Distance browsing in spatial databases , 1999, TODS.

[11]  Oliver Günther,et al.  Multidimensional access methods , 1998, CSUR.

[12]  Hans-Peter Kriegel,et al.  Fast nearest neighbor search in high-dimensional space , 1998, Proceedings 14th International Conference on Data Engineering.

[13]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[14]  Shin'ichi Satoh,et al.  The SR-tree: an index structure for high-dimensional nearest neighbor queries , 1997, SIGMOD '97.

[15]  Bernhard Seeger,et al.  Efficient temporal join processing using indices , 2002, Proceedings 18th International Conference on Data Engineering.

[16]  Martti Penttonen,et al.  A Reliable Randomized Algorithm for the Closest-Pair Problem , 1997, J. Algorithms.

[17]  Michiel Smid,et al.  Closest-Point Problems in Computational Geometry , 2000, Handbook of Computational Geometry.

[18]  Bernhard Seeger,et al.  XXL - A Library Approach to Supporting Efficient Implementations of Advanced Database Queries , 2001, VLDB.

[19]  V. Tsotras,et al.  A Comparison of Indexed Temporal Joins , 2001 .

[20]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[21]  Yannis Manolopoulos,et al.  Closest pair queries in spatial databases , 2000, SIGMOD '00.