## Spatial Join Techniques

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Citations: | 20 - 3 self |

### BibTeX

@MISC{Jacox_spatialjoin,

author = {Edwin H. Jacox and Hanan Samet},

title = {Spatial Join Techniques},

year = {}

}

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### Abstract

A variety of techniques for performing a spatial join are reviewed. Instead of just summarizing the literature and presenting each technique in its entirety, distinct components of the different techniques are described and each is decomposed into an overall framework for performing a spatial join. A typical spatial join technique consists of the following components: partitioning the data, performing internal-memory spatial joins on subsets of the data, and checking if the full polygons intersect. Each technique is decomposed into these components and each component addressed in a separate section so as to compare and contrast similar aspects of each technique. The goal of this survey is to describe the algorithms within each component in detail, comparing and contrasting competing methods, thereby enabling further analysis and experimentation with each component and allowing the best algorithms for a particular situation to be built piecemeal, or, even better, enabling an optimizer to choose which algorithms to use. Categories and Subject Descriptors: H.2.4 [Database Management]: Systems—Query processing; H.2.8 [Database Management]: Database Applications—Spatial databases and GIS

### Citations

8982 | Introduction to Algorithms - Cormen, Leiserson, et al. - 1990 |

2354 | R-trees: A dynamic index structure for spatial searching - Guttman - 1984 |

2321 | The Art of Computer Programming - Knuth - 1968 |

1843 | Computational Geometry: An Introduction - Preparata, Shamos - 1985 |

1233 | The Design and Analysis of Spatial Data Structures - Samet - 1989 |

1200 | Multidimensional binary search trees used for associative searching - BENTLEY - 1975 |

707 | Obb-tree: A hierarchical structure for rapid interference detection - Gottschalk, Manocha |

689 | Query Evaluation Techniques for Large Databases - Graefe - 1993 |

673 |
Fundamentals of Database Systems
- Elmasri, Navathe
- 2000
(Show Context)
Citation Context ...tion under Grants EIA-99-00268, and IIS-00-86162 is gratefully acknowledged. 0sTable 1: Spatial join components. Section 3 Internal Memory Methods 3.1 Nested Loop Join [72] 3.2 Index Nested-Loop Join =-=[28]-=- 3.3 Plane Sweep [9, 91] 3.4 Z-Order [5, 77] Section 4.1 External Memory Methods 4.1.1 Hierarchical Traversal [20, 36, 48, 53] Both Sets Indexed 4.1.2 Non-Hierarchical Methods [42, 54] 4.1.3 Multi-Dim... |

596 | Multidimensional Access Methods - Gaede, Günther - 1998 |

505 | Principles of distributed database systems - Ozsu, Valduriez - 1991 |

396 | Algorithms for constraint-satisfaction problems: A survey - Kumar - 1992 |

395 | The Grid File: An Adaptable Symmetric Multikey File Structure - Nievergelt, Hinterberger, et al. - 1984 |

395 | The SR-Tree: An Index Structure for High-Dimensional Nearest Neighbor Queries - Katayama, Satoh - 1997 |

337 | Lists: A probabilistic alternative to balanced trees, ommunications 31 (2008) 358–374 373been a member of over 40 international programme, scientific and conference committees. Dr Paul Sage received the degrees of B.Sc. Computer Science (Honours) and Ph.D - Pugh, Skip |

334 | Efficient Processing of Spatial Join Using R-trees - Brinkhoff, Kriegel, et al. - 1993 |

304 | The R+- Tree: A Dynamic Index for Multi-dimensional Objects - Sellis, Roussopoulus, et al. - 1987 |

289 | Quad trees: A data structure for retrieval on composite keys - Finkel, Bentley - 1974 |

254 | Foundations of Multidimensional and Metric Data Structures - Samet - 2006 |

245 | Efficient Collision Detection Using Bounding Volume Hierarchies of k-DOPs - Klosowski, Held, et al. - 1998 |

225 | On Packing R–trees - Kamel, Faloutsos - 1993 |

219 | Generalized search trees for database systems - Hellerstein, Naughton, et al. - 1995 |

212 |
The Art of Computer Programming, Sorting and Searching
- Knuth
- 1997
(Show Context)
Citation Context ...ed datasets, more sophisticated structures are appropriate. Some examples of data structures that will work as sweep structures are: (1) A simple linked list [Cormen et al. 1990]. (2) Interval tries [=-=Knuth 1973-=-], as used by Dittrich and Seeger [2000]. (3) A dynamic segment tree [Cormen et al. 1990]. (4) An interval tree [Edelsbrunner 1983] with a skip list [Pugh 1990], as described by Hanson [1991] and used... |

205 | and Andries Van Dam Fundamentals of Interactive Computer Graphics Addison-Wesley - Foley - 1982 |

201 | Linear clustering of objects with multiple attributes - Jagadish - 1990 |

193 | Approximating polyhedra with spheres for time-critical collision detection - Hubbard - 1996 |

179 | Priority search trees - McCreight - 1985 |

178 | Spatial query processing in an object-oriented database system - Orenstein - 1986 |

175 | Partition Based Spatial-Merge Join - Patel, DeWitt - 1996 |

168 | An optimal algorithm for intersecting line segments in the plane - Chazelle, Edelsbrunner - 1992 |

162 | Beyond Uniformity and Independence : Analysis of R-trees Using the Concept of Fractal Dimension - Faloutsos, Kamel - 1995 |

141 | Multi-step processing of spatial joins - Brinkhoff, Kriegel, et al. - 1994 |

139 | Join processing in relational databases - Mishra, Eich - 1992 |

126 | Estimating the selectivity of spatial queries using the ‘correlation’ fractal dimension - Belussi, Faloutsos - 1995 |

110 | Database System Implementation - Garcia-Molina, Ullman, et al. - 2000 |

107 | The LSD tree: Spatial access to multidimensional point and non-point objects - HENRICH, SIX, et al. - 1989 |

106 | PROBE: Spatial Data Modeling and Query Processing in an Image Database Application - Orenstein, Manola - 1988 |

105 | Spatial Joins Using Seeded Trees - Lo, Ravishankar |

101 |
The Art of Computer Programming: Sorting and Searching (Volume 3
- Knuth
- 1998
(Show Context)
Citation Context ... or highly skewed data sets, more sophisticated structures are appropriate. Some examples of data structures that will work as sweep structures are: 13s1. A simple linked list [23]. 2. Interval tries =-=[55]-=-, as used by Dittrich and Seeger [26]. 3. A dynamic segment tree [23]. 4. An interval tree [27] with a skip list [92], as described by Hanson [41] and used by Arge et al. [9]. Except for the linked li... |

98 | Spatial hash-joins - Lo, Ravishankar - 1996 |

94 | Ecient computation of spatial joins - Gunther - 1993 |

88 | Spatial Joins using R-trees: Breadth-First Traversal with Global Optimizations - Huang, Jing, et al. - 1997 |

75 | Redundancy in spatial databases - Orenstein - 1989 |

74 | Spatial Join Indices - Rotem - 1991 |

70 | The SEQUOIA 2000 project
- Stonebraker
- 1993
(Show Context)
Citation Context ...o not discuss most experimental results. Nevertheless, we do point out the data set generator of Günther et al. [37], which helps to establish a benchmark for spatial joins beyond the typical Sequoia =-=[97]-=- and Tiger [76] data sets. Benchmarks are an important step towards achieving the goal of repeatable and predictable algorithm performance. Also, to simplify the discussion of the techniques, it is as... |

68 | An optimal algorithm for finding segment intersections - Balaban - 1995 |

64 | Scalable Sweeping-Based Spatial Join - Arge, Procopiuc, et al. - 1998 |

62 | Size separation spatial join - Koudas, Sevcik - 1997 |

62 | Five balltree construction algorithms - Omohundro - 1989 |

61 | Comparison of Approximations of Complex Objects Used for Approximation-based Query Processing - Brinkhoff - 1993 |