Scalable Sweeping-Based Spatial Join

In this paper, we consider the filter step of the spatial join problem, for the case where neither of the inputs are indexed. We present a new algorithm, Scalable Sweeping-Based Spatial Join (SSSJ), that achieves both efficiency on real-life data and robustness against highly skewed and worst-case data sets. The algorithm combines a method with theoretically optimal bounds on I/O transfers based on the recently proposed distribution-sweeping technique with a highly optimized implementation of internal-memory plane-sweeping. We present experimental results based on an efficient implementation of the SSSJ algorithm, and compare it to the state-ofthe-art Partition-Based Spatial-Merge (PBSM) algorithm of Patel and DeWitt. Center for Geometric Computing, Department of Computer Science, Duke University, Durham, NC 27708–0129. Supported in part by U.S. Army Research Office grant DAAH04–96–1–0013. Email: [email protected]. Center for Geometric Computing, Department of Computer Science, Duke University, Durham, NC 27708–0129. Supported in part by the U.S. Army Research Office under grant DAAH04–96–1–0013 and by the National Science Foundation under grant CCR–9522047. Email: [email protected]. Information Sciences Research Center, Bell Laboratories, 600 Mountain Avenue, Box 636, Murray Hill, NJ 07974–0636. Email: [email protected]. Information Sciences Research Center, Bell Laboratories, 600 Mountain Avenue, Box 636, Murray Hill, NJ 07974–0636. Email: [email protected]. Center for Geometric Computing, Department of Computer Science, Duke University, Durham, NC 27708–0129. Supported in part by the U.S. Army Research Office under grant DAAH04–96–1–0013 and by the National Science Foundation under grant CCR–9522047. Part of this work was done while visiting Bell Laboratories, Murray Hill, NJ. Email: [email protected]. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 24th VLDB Conference New York, USA, 1998