Size separation spatial join

Nick Koudas, Kenneth C. Sevcik
1997 Proceedings of the 1997 ACM SIGMOD international conference on Management of data - SIGMOD '97  
We introduce a new algorithm to compute the spatial join of two or more spatial data sets, when indexes are not available on them. Size Separation Spatial Join (S3 J) imposes a hierarchical decomposition of the data space and, in contrast with previous approaches, requires no replication of entities from the input data sets. Thus its execution time depends only on the sizes of the joined data sets. We describe S3.J and present an analytical evaluation of its 1/0 and processor requirements
more » ... ing them with those of previously proposed algorithms for the same problem. We show that S3 J has relatively simple cost estimation formulas that can be exploited by a query optimizer. S3 J can be efficiently implemented using software already present in many relational systems. In addition, we introduce Dynamic Spatial Bitmaps (DSB), a new technique that enables S3 J to dynamically or statically exploit bitmap query processing techniques. Fkmlly, we present experimental results for a prototype implementation of S3.l involving real and synthetic data sets for a variety of data distributions. Our experimental results are consistent with our analytical observations and demonstrate the performance benefits of S3 J over alternative approaches that have been proposed recently. Permissionto make digitallhardcopy of part or all this work for personal or classroomuse is granted without fae provided that copies are not msde or distributed for profit or commercial advantage, the copyright notice, the title of the publication and its dste aPPear, and notice is given that copying is by permission of ACM, Inc. To copy otherwise, to republish, to post on aervera, or to radiatribute to lists, requirea prior specific parmiasion and/or a fee,
doi:10.1145/253260.253340 dblp:conf/sigmod/KoudasS97 fatcat:digjphhffvhutilszov5heirui