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Top-k Spatial Joins
April 2005 (vol. 17 no. 4)
pp. 567-579
ASCII Text
x 
 
Manli Zhu, Dimitris Papadias, Jun Zhang, Dik Lun Lee, "Top-k Spatial Joins," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 4, pp. 567-579, April, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2005.65,
author = {Manli Zhu and Dimitris Papadias and Jun Zhang and Dik Lun Lee},
title = {Top-k Spatial Joins},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {17},
number = {4},
issn = {1041-4347},
year = {2005},
pages = {567-579},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.65},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Top-k Spatial Joins
IS - 4
SN - 1041-4347
SP567
EP579
EPD - 567-579
A1 - Manli Zhu,
A1 - Dimitris Papadias,
A1 - Jun Zhang,
A1 - Dik Lun Lee,
PY - 2005
KW - Database
KW - spatial database
KW - spatial joins.
VL - 17
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Given two spatial data sets A and B, a top-k spatial join retrieves the k objects from A or B that intersect the largest number of objects from the other data set. Depending on the application requirements, there exist several variations of the problem. For instance, B may be a point data set, and the goal may be to retrieve the regions of A that contain the maximum number of points. The processing of such queries with conventional spatial join algorithms is expensive. However, several improvements are possible based on the fact that we only require a small subset of the result (instead of all intersection/containments pairs). In this paper, we propose output-sensitive algorithms for top-k spatial joins that utilize a variety of optimizations for reducing the overhead.

[1] L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter, “Scalable Sweeping-Based Spatial Join,” Proc. 24th Int'l Conf. Very Large Data Bases (VLDB), 1998.
[2] N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, “The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles,” Proc. ACM SIGMOD, 1990.
[3] S. Berchtold, D. Keim, and H. Kriegel, “The X-Tree: An Index Structure for High-Dimensional Data,” Proc. 22nd Int'l Conf. Very Large Databases (VLDB), 1996.
[4] B. Boehm, B. Braunmuller, F. Krebs, and H. Kriegel, “Epsilon Grid Order: An Algorithm for the Similarity Join on Massive High Dimensional Data,” Proc. ACM SIGMOD, 2001.
[5] T. Brinkhoff, H.P. Kriegel, and B. Seeger, “Efficient Processing of Spatial Joins Using R-Trees,” Proc. ACM SIGMOD, 1993.
[6] A. Corral, Y. Manolopoulos, Y. Theodoridis, and M. Vassilakopoulos, “Closest Pair Queries in Spatial Databases,” Proc. ACM SIGMOD, 2000.
[7] M. Denny, M. Franklin, P. Castro, and A. Purakayastha, “Mobiscope: A Scalable Spatial Discovery Service for Mobile Network Resources,” Proc. Fourth Int'l Conf. Mobile Data Management (MDM), 2003.
[8] S. Govindarajan, P. Agarwal, and L. Arge, “CRB-Tree: An Efficient Indexing Scheme for Range Aggregate Queries,” Proc. Int'l Conf. Database Theory (ICDT), 2003.
[9] A. Guttman, “R-Trees: A Dynamic Index Structure for Spatial Searching,” Proc. ACM SIGMOD, 1984.
[10] G. Hjaltason and H. Samet, “Incremental Distance Join Algorithms for Spatial Databases,” Proc. ACM SIGMOD, 1998.
[11] G. Hjaltason and H. Samet, “Distance Browsing in Spatial Databases,” ACM Trans. Database Systems, vol. 24, no. 2, pp. 265-318, 1999.
[12] Y. Huang, N. Jing, and E. Rundensteiner, “Spatial Joins Using R-Trees: Breadth First Traversal with Global Optimizations,” Proc. 23rd Int'l Conf. Very Large Data Bases, 1997.
[13] I. Ilyas, W. Aref, and A. Elmagarmid, “Supporting Top-k Join Queries in Relational Databases,” Proc. 29th Int'l Conf. Very Large Data Bases, 2003.
[14] M. Jurgens and H. Lenz, “PISA: Performance Models for Index Structures with and without Aggregated Data,” Proc. 11th Int'l Conf. Scientific and Statistical Database Management (SSDBM), 1999.
[15] N. Koudas and K. Sevcik, “Size Separation Spatial Join,” Proc. ACM SIGMOD, 1997.
[16] M.-L. Lo and C. Ravishankar, “Spatial Joins Using Seeded Trees,” Proc. ACM SIGMOD, 1994.
[17] M.-L. Lo and C. Ravishankar, “Spatial Hash-Joins,” Proc. ACM SIGMOD, 1996.
[18] G. Luo, J. Naughton, and C. Ellman, “A Non-Blocking Parallel Spatial Join Algorithm,” Proc. IEEE Int'l Conf. Data Eng., 2002.
[19] N. Mamoulis and D. Papadias, “Multiway Spatial Joins,” ACM Trans. Database Systems, vol. 26, no. 4, pp. 424-475, 2001.
[20] N. Mamoulis and D. Papadias, “Slot Index Spatial Join,” IEEE Trans. Knowledge and Data Eng., vol. 15, no. 1, pp. 211-231, 2003.
[21] M. Mokbel, W. Aref, and I. Kamel, “Performance of Multidimensional Space-Filling Curves,” Proc. 10th ACM Int'l Symp. Advances in Geographic Information Systems, 2002.
[22] A. Natsev, Y. Chang, J. Smith, C. Li, and J. Vitter, “Supporting Incremental Join Queries on Ranked Inputs,” Proc. 27th Int'l Conf. Very Large Data Bases, 2001.
[23] J. Orenstein, “Spatial Query Processing in an Object-Oriented Database System,” Proc. ACM SIGMOD, 1986.
[24] D. Papadias, P. Kalnis, J. Zhang, and Y. Tao, “Efficient OLAP Operations in Spatial Data Warehouses,” Proc. Seventh Int'l Symp. Spatial and Temporal Databases (SSTD), 2001.
[25] A.N. Papadopoulos, P. Rigaux, and M. Scholl, “A Performance Evaluation of Spatial Join Processing Strategies,” Proc. Sixth Int'l Symp. Spatial Databases, 1999.
[26] J.M. Patel and D.J. DeWitt, “Partition Based Spatial-Merge Join,” Proc. ACM SIGMOD, 1996.
[27] D. Rotem, “Spatial Join Indices,” Proc. IEEE Int'l Conf. Data Eng., 1991.
[28] N. Roussopoulos, S. Kelly, and F. Vincent, “Nearest Neighbor Queries,” Proc. ACM SIGMOD, 1995.
[29] Rtree portal, http:/www.rtreeportal.org, 2003.
[30] S. Saltenis, C. Jensen, S. Leutenegger, and M. Lopez, “Indexing the Positions of Continuously Moving Objects,” Proc. ACM SIGMOD, 2000.
[31] T. Sellis, N. Roussopoulos, and C. Faloutsos, “The ${\rm{R^+{\hbox{-}}tree}}$ : A Dynamic Index for Multidimensional Objects,” Proc. 13th Int'l Conf. Very Large Data Bases, 1987.
[32] J. Shan, D. Zhang, and B. Salzberg, “On Spatial-Range Closest-Pair Query,” Proc. Ninth Int'l Symp. Spatial and Temporal Databases, 2003.
[33] S.Y. Shou, N. Mamoulis, H. Cao, D. Papadias, and D. Cheung, “Evaluation of Iceberg Distance Joins,” Proc. Ninth Int'l Symp. Spatial and Temporal Databases, 2003.
[34] Y. Tao and D. Papadias, “Range Aggregate Processing in Spatial Databases,” IEEE Trans. Knowledge and Data Eng., to appear and available at: www.cs.ust.hk~dimitris/.
[35] D. Zhang, V. Tsotras, and D. Gunopulos, “Efficient Aggregation over Objects with Extent,” Proc. 21st ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems, 2002.

Index Terms:
Database, spatial database, spatial joins.
Citation:
Manli Zhu, Dimitris Papadias, Jun Zhang, Dik Lun Lee, "Top-k Spatial Joins," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 4, pp. 567-579, April 2005, doi:10.1109/TKDE.2005.65
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