Spatial query processing in an object-oriented database system
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
ACM Transactions on Database Systems (TODS)
Hierarchical representations of collections of small rectangles
ACM Computing Surveys (CSUR)
Redundancy in spatial databases
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Multi-step processing of spatial joins
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Partition based spatial-merge join
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Constraint Databases
PROBE Spatial Data Modeling and Query Processing in an Image Database Application
IEEE Transactions on Software Engineering
Proceedings of the Seventh International Conference on Data Engineering
A New Algorithm for Computing Joins with Grid Files
Proceedings of the Ninth International Conference on Data Engineering
Efficient Computation of Spatial Joins
Proceedings of the Ninth International Conference on Data Engineering
Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A Raster Approximation For Processing of Spatial Joins
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Scalable Sweeping-Based Spatial Join
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The Buffer Tree: A New Technique for Optimal I/O-Algorithms (Extended Abstract)
WADS '95 Proceedings of the 4th International Workshop on Algorithms and Data Structures
Filter Trees for Managing Spatial Data over a Range of Size Granularities
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Index Structure for Spatial Joins in Linear Constraint Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Geometric intersection problems
SFCS '76 Proceedings of the 17th Annual Symposium on Foundations of Computer Science
External-memory computational geometry
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
On Multi-way Spatial Joins with Direction Predicates
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Toward Spatial Joins for Polygons
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
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Spatial joins are very important but costly operations in spatial databases. A typical evaluation strategy of spatial joins is to perform the join on approximations of spatial objects and then evaluate the join of the real objects based on the results. The common approximation is the minimum bounding rectangle. Minimum bounding rectangles are coarse approximations of spatial objects and may cause a large number of "false hits". In this paper, we consider a more general form of approximation with rectilinear polygons for spatial objects in the context of spatial join evaluation. A naive approach is to decompose rectilinear polygons into rectangles and use an exisiting rectangle join algorithm. This may require additional cost for sorting, index construction, and decomposition and prohibits the join evaluation to be pipelined. The main contribution of the paper is a technique for extending plane sweeping based rectangle join algorithms to perform a spatial join on rectilinear polygons directly. We show that the join of two sets of rectilinear polygons can be computed in O(bN logb N/b + l2k) IOs directly, where N is the total number of boundary points in each input set, l the maximum number of boundary points of a rectilinear polygon, b the page size, and k the number of rectilinear polygon intersections. When the rectilinear polygons are y-monotone, the IO complexity becomes O(bN logb N/b + lk.