- A. Aji, F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, and J. Saltz. Hadoop GIS: A High Performance Spatial Data Warehousing System over Mapreduce. VLDB, 6(11), Aug. 2013. Google ScholarDigital Library
- N. An, Z.-Y. Yang, and A. Sivasubramaniam. Selectivity Estimation for Spatial Joins. In ICDE, 2001. Google ScholarDigital Library
- L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, J. Vahrenhold, and J. S. Vitter. A Unified Approach for Indexed and Non-indexed Spatial Joins. In EDBT, 2000. Google ScholarDigital Library
- J. L. Bentley. Multidimensional Binary Search Trees Used for Associative Searching. CACM, 1975. Google ScholarDigital Library
- T. Brinkhoff, H.-P. Kriegel, R. Schneider, and B. Seeger. Multi-step Processing of Spatial Joins. SIGMOD Record, 23(2):197--208, may 1994. Google ScholarDigital Library
- T. Brinkhoff, H.-P. Kriegel, and B. Seeger. Efficient Processing of Spatial Joins Using R-trees. In SIGMOD, 1993. Google ScholarDigital Library
- T. Brinkhoff, H.-P. Kriegel, and B. Seeger. Parallel Processing of Spatial Joins using R-trees. In ICDE, pages 258--265, 1996. Google ScholarDigital Library
- M. de Berg, O. Cheong, M. van Kreveld, and M. Overmars. Computational Geometry: Algorithms and Applications. Springer, 2008. Google ScholarDigital Library
- J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. CACM, 51(1):107--113, jan. 2008. Google ScholarDigital Library
- J. V. den Bercken, B. Seeger, and P. Widmayer. The Bulk Index Join: A Generic Approach to Processing Non-Equijoins. In ICDE, 1999.Google ScholarCross Ref
- A. Eldawy, Y. Li, M. F. Mokbel, and R. Janardan. CGHadoop: Computational Geometry in MapReduce. In SIGSPATIAL, 2013. Google ScholarDigital Library
- A. Eldawy and M. F. Mokbel. SpatialHadoop: A MapReduce Framework for Spatial Data. In ICDE, pages 1352--1363, Seoul, Korea, apr. 2015.Google ScholarCross Ref
- A. Eldawy and M. F. Mokbel. The Era of Big Spatial Data (Tutorial). In ICDE, 2016.Google Scholar
- C. Faloutsos, B. Seeger, A. Traina, and C. T. Jr. Spatial Join Selectivity Using Power Laws. In SIGMOD, 2000. Google ScholarDigital Library
- R. Finkel and J. Bentley. Quad Trees a Data Structure for Retrieval on Composite Keys. Acta Informatica, 1974. Google ScholarDigital Library
- M. R. Fornari, J. L. D. Comba, and C. Iochpe. Query Optimizer for Spatial Join Operations. In GIS, 2006. Google ScholarDigital Library
- H. Gao, H. Zhang, D. Hu, R. Tian, and D. Guo. Multi-scale Features of Urban Planning Spatial Data. In Geoinformatics, 2010.Google Scholar
- O. Gunther, V. Oria, P. Picouet, J.-M. Saglio, and M. Scholl. Benchmarking Spatial Joins A La Carte. In SSDM, 1998. Google ScholarDigital Library
- H. Gupta, B. Chawda, S. Negi, T. A. Faruquie, L. V. Subramaniam, and M. Mohania. Processing Multi-way Spatial Joins on Map-reduce. In EDBT, 2013. Google ScholarDigital Library
- C. Gurret and P. Rigaux. The Sort/Sweep Algorithm: A New Method for R-tree based Spatial Joins. In SSDM, 2000. Google ScholarDigital Library
- L. Harada, M. Nakano, M. Kitsuregawa, and M. Takagi. Query Processing for Multi-Attribute Clustered Records. VLDB, pages 59--70, 1990. Google ScholarDigital Library
- E. G. Hoel and H. Samet. Benchmarking Spatial Join Operations with Spatial Output. In VLDB, pages 606--618, 1995. Google ScholarDigital Library
- E. H. Jacox and H. Samet. Iterative Spatial Join. TODS, 28(3):230--256, sep. 2003. Google ScholarDigital Library
- E. H. Jacox and H. Samet. Spatial Join Techniques. TODS, 32(1), mar. 2007. Google ScholarDigital Library
- J.-D. Kim and B.-H. Hong. Parallel Spatial Join Algorithms using Grid Files. In DANTE, pages 226--234, 1999. Google ScholarDigital Library
- S. T. Leutenegger, M. A. Lopez, and J. Edgington. STR: A Simple and Efficient Algorithm for R-tree Packing. In ICDE, pages 497--506, 1997. Google ScholarDigital Library
- M.-L. Lo and C. V. Ravishankar. Spatial Joins Using Seeded Trees. In SIGMOD, 1994. Google ScholarDigital Library
- W. Lu, Y. Shen, S. Chen, and B. C. Ooi. Efficient Processing of K Nearest Neighbor Joins Using MapReduce. PVLDB, 5(10), jun. 2012. Google ScholarDigital Library
- G. Luo, J. F. Naughton, and C. J. Ellmann. A Non-blocking Parallel Spatial Join Algorithm. In ICDE, pages 697--705, 2002. Google ScholarDigital Library
- N. Mamoulis, P. Kalnis, S. Bakiras, and X. Li. Optimization of Spatial Joins on Mobile Devices. In SSTD, 2003.Google ScholarCross Ref
- H. Markram, K. Meier, T. Lippert, S. Grillner, R. Frackowiak, S. Dehaene, A. Knoll, H. Sompolinsky, K. Verstreken, J. DeFelipe, S. Grant, J.-P. Changeux, and A. Saria. Introducing the human brain project. Procedia Computer Science, 2011.Google ScholarCross Ref
- J. Nievergelt, H. Hinterberger, and K. C. Sevcik. The Grid File: An Adaptable, Symmetric Multikey File Structure. TODS, 9(1):38--71, mar. 1984. Google ScholarDigital Library
- OpenStreetMap. https://www.openstreetmap.org/.Google Scholar
- A. Papadopoulos and Y. Manolopoulos. Multiple Range Query Optimization in Spatial Databases. In ADBIS, 1998. Google ScholarDigital Library
- A. Papadopoulos, P. Rigaux, and M. Scholl. A Performance Evaluation of Spatial Join Processing Strategies. Advances in Spatial Databases, 1999. Google ScholarDigital Library
- J. M. Patel and D. J. DeWitt. Partition Based Spatial-merge Join. In SIGMOD, 1996. Google ScholarDigital Library
- J. M. Patel and D. J. DeWitt. Clone Join and Shadow Join: Two Parallel Spatial Join Algorithms. In GIS, pages 54--61, 2000. Google ScholarDigital Library
- S. Puri, D. Agarwal, X. He, and S. K. Prasad. MapReduce Algorithms for GIS Polygonal Overlay Processing. In IPDPSW, 2013. Google ScholarDigital Library
- D. Sidlauskas and C. S. Jensen. Spatial Joins in Main Memory: Implementation Matters! PVLDB, 8(1):97--100, sep. 2014. Google ScholarDigital Library
- B. Sowell, M. V. Salles, T. Cao, A. Demers, and J. Gehrke. An Experimental Analysis of Iterated Spatial Joins in Main Memory. VLDB, 6(14), sep. 2013. Google ScholarDigital Library
- C. Sun, D. Agrawal, and A. E. Abbadi. Selectivity Estimation for Spatial Joins with Geometric Selections. In EDBT, 2002. Google ScholarDigital Library
- M. Ubell. The Montage Extensible DataBlade Architecture. In SIGMOD, 1994. Google ScholarDigital Library
- K. Wang, J. Han, B. Tu, J. Dai, W. Zhou, and X. Song. Accelerating Spatial Data Processing with MapReduce. In ICPADS, 2010. Google ScholarDigital Library
- K. Wang, Y. Huai, R. Lee, F. Wang, X. Zhang, and J. H. Saltz. Accelerating Pathology Image Data Cross-comparison on CPU-GPU Hybrid Systems. PVLDB, 2012. Google ScholarDigital Library
- C. Xia, H. Lu, B. C. Ooi, and J. Hu. Gorder: An Efficient Method for KNN Join Processing. In VLDB, 2004. Google ScholarDigital Library
- C. Zhang, F. Li, and J. Jestes. Efficient Parallel kNN Joins for Large Data in MapReduce. In EDBT, 2012. Google ScholarDigital Library
- S. Zhang, J. Han, Z. Liu, K. Wang, and S. Feng. Spatial Queries Evaluation with MapReduce. In GCC, pages 287--292, Aug. 2009. Google ScholarDigital Library
- S. Zhang, J. Han, Z. Liu, K. Wang, and Z. Xu. SJMR: Parallelizing spatial join with MapReduce on clusters. In CLUSTER, pages 1--8, Aug. 2009.Google ScholarCross Ref
- Y. Zhong, J. Han, T. Zhang, Z. Li, J. Fang, and G. Chen. Towards Parallel Spatial Query Processing for Big Spatial Data. In IPDPSW, 2012. Google ScholarDigital Library
- X. Zhou, D. J. Abel, and D. Truffet. Data Partitioning for Parallel Spatial Join Processing. Geoinformatica, 1998. Google ScholarDigital Library
Index Terms
- Optimizing Spatial Queries in MapReduce
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