Incremental distance join algorithms for spatial databases

Two new spatial join operations, distance join and distance semi-join, are introduced where the join output is ordered by the distance between the spatial attribute values of the joined tuples. Incremental algorithms are presented for computing these operations, which can be used in a pipelined fashion, thereby obviating the need to wait for their completion when only a few tuples are needed. The algorithms can be used with a large class of hierarchical spatial data structures and arbitrary spatial data types in any dimensions. In addition, any distance metric may be employed. A performance study using R-trees shows that the incremental algorithms outperform non-incremental approaches by an order of magnitude if only a small part of the result is needed, while the penalty, if any, for the incremental processing is modest if the entire join result is required.

[1]  Hanan Samet,et al.  Ranking in Spatial Databases , 1995, SSD.

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

[3]  Daniel P. Miranker,et al.  Processing queries for first-few answers , 1996, CIKM '96.

[4]  Oliver Giinther,et al.  Efficient Computation of Spatial Joins , 1993 .

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

[6]  Hans-Peter Kriegel,et al.  The Buddy-Tree: An Efficient and Robust Access Method for Spatial Data Base Systems , 1990, VLDB.

[7]  Klaus H. Hinrichs,et al.  Probabilistic analysis of an algorithm for solving thek-dimensional all-nearest-neighbors problem by projection , 1991, BIT Comput. Sci. Sect..

[8]  Pravin M. Vaidya,et al.  AnO(n logn) algorithm for the all-nearest-neighbors Problem , 1989, Discret. Comput. Geom..

[9]  Hanan Samet,et al.  Data-Parallel Spatial Join Algorithms , 1994, 1994 International Conference on Parallel Processing Vol. 3.

[10]  Helen J. Wang,et al.  Online aggregation , 1997, SIGMOD '97.

[11]  A. N. Wilschut,et al.  Dataflow query execution in a parallel main-memory environment , 1991, [1991] Proceedings of the First International Conference on Parallel and Distributed Information Systems.

[12]  Doron Rotem.  Spatial join indices , 1991, [1991] Proceedings. Seventh International Conference on Data Engineering.

[13]  Ming-Ling Lo,et al.  Spatial joins using seeded trees , 1994, SIGMOD '94.

[14]  D. B. Lomet,et al.  A robust multi-attribute search structure , 1989, [1989] Proceedings. Fifth International Conference on Data Engineering.

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

[16]  Oliver Günther,et al.  Efficient computation of spatial joins , 1993, Proceedings of IEEE 9th International Conference on Data Engineering.

[17]  Andreas Henrich.  A Distance Scan Algorithm for Spatial Access Structures , 1994, ACM-GIS.

[18]  Klaus H. Hinrichs,et al.  A new algorithm for computing joins with grid files , 1993, Proceedings of IEEE 9th International Conference on Data Engineering.

[19]  Derick Wood,et al.  Counting and Reporting Intersections of d-Ranges , 1982, IEEE Transactions on Computers.

[20]  Elke A. Rundensteiner,et al.  A cost model for estimating the performance of spatial joins using R-trees , 1997, Proceedings. Ninth International Conference on Scientific and Statistical Database Management (Cat. No.97TB100150).

[21]  Dennis Shasha,et al.  Query Processing for Distance Metrics , 1990, VLDB.

[22]  Rakesh Agrawal,et al.  Parallel Algorithms for High-Dimensional Proximity Joins , 1998 .

[23]  Douglas Comer,et al.  Ubiquitous B-Tree , 1979, CSUR.

[24]  Michael J. Carey,et al.  On saying “Enough already!” in SQL , 1997, SIGMOD '97.

[25]  Sergei Bespamyatnikh,et al.  An Optimal Algorithm for Closest-Pair Maintenance , 1998, Discret. Comput. Geom..

[26]  Sergei N. Bespamyatnikh.  An optimal algorithm for closest pair maintenance (extended abstract) , 1995, SoCG 1995.

[27]  Rakesh Agrawal,et al.  Parallel Algorithms for High-dimensional Similarity Joins for Data Mining Applications , 1997, Very Large Data Bases Conference.

[28]  Elke A. Rundensteiner,et al.  Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations , 1997, VLDB.

[29]  Kenneth L. Clarkson,et al.  Fast algorithms for the all nearest neighbors problem , 1983, 24th Annual Symposium on Foundations of Computer Science (sfcs 1983).

[30]  Hans-Peter Kriegel,et al.  Multi-step processing of spatial joins , 1994, SIGMOD '94.

[31]  Hanan Samet,et al.  The Design and Analysis of Spatial Data Structures , 1989 .

[32]  Masaru Kitsuregawa,et al.  Join strategies on KD-tree indexed relations , 1989, [1989] Proceedings. Fifth International Conference on Data Engineering.

[33]  Hans-Peter Kriegel,et al.  Efficient processing of spatial joins using R-trees , 1993, SIGMOD Conference.