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Hierarchical representations of collections of small rectangles
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Source ACM Computing Surveys (CSUR) archive
Volume 20 ,  Issue 4  (December 1988) table of contents
Pages: 271 - 309  
Year of Publication: 1988
ISSN:0360-0300
Author
Hanan Samet  Univ. of Maryland, College Park
Publisher
ACM  New York, NY, USA
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ABSTRACT

A tutorial survey is presented of hierarchical data structures for representing collections of small rectangles. Rectangles are often used as an approximation of shapes for which they serve as the minimum rectilinear enclosing object. They arise in applications in cartography as well as very large-scale integration (VLSI) design rule checking. The different data structures are discussed in terms of how they support the execution of queries involving proximity relations. The focus is on intersection and subset queries. Several types of representations are described. Some are designed for use with the plane-sweep paradigm, which works well for static collections of rectangles. Others are oriented toward dynamic collections. In this case, one representation reduces each rectangle to a point in a higher multidimensional space and treats the problem as one involving point data. The other representation is area based—that is, it depends on the physical extent of each rectangle.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
ABEL, D. J. 1985. Some elemental operations on linear quadtrees for geographic information systems. Comput. J. 28, i (February), 73-77.
 
2
ABEL, D. J., AND SMITH, J. L. 1983. A data structure and algorithm based on a linear key for a rectangle retrieval problem. Comput. Vision Graph. Image Process. 24, 1 (Oct.), 1-13.
 
3
4
 
5
BENTLEY, J. L. 1977. Algorithms for Klee's rectangle problems. Computer Science Department, Carnegie-Mellon University, Pittsburgh.
 
6
BENTLEY, J. L. 1979. Decomposable searching problems. Inf. Process. Lett. 8, 5 (June), 244-251.
 
7
BENTLEY, J. L., AND MAURER, H. A. 1980. Efficient worst-case data structures for range searching. Acta Inf. 13, 2, 155-168.
 
8
BENTLEY, J. L., AND OTTMANN, T. A. 1979. Algorithms for reporting and counting geometric intersections. IEEE Trans. Comput. 28, 9 (Sept.), 643-647.
 
9
BENTLEY, J. L., AND WOOD, D. 1980. An optimal worst-case algorithm for reporting intersections of rectangles. IEEE Trans. Comput. 29, 7 (July), 571-577.
 
10
BUCHER, W., AND EOELSBRUNNER, H. 1983. On expected and worst-case segment trees. In Advances in Computing Research, vol. 1, Computational Geometry, F. P. Preparata, Ed. JAI Press, Greenwich, Conn., pp. 109-125.
 
11
CHAZELLE, B., AND GUIBAS, L. J. 1986a. Fractional cascading: I. A data structuring technique. Algorithmica 1, 2, 133-162.
 
12
CHAZELLE, B., AND GUIBAS, L. J. 1986b. Fractional cascading: II. Applications. Algorithmica 1, 2, 163-191.
13
 
14
EDELSBRUNNER, H. 1980a. Dynamic rectangle intersection searching. Institute for Information Processing Rept. 47, Technical University of Graz, Graz, Austria.
 
15
EDELSBRUNNER, H. 1980b. Dynamic data structures for orthogonal intersection queries. Institute for Information Processing. Rept. 59, Technical University of Graz, Graz, Austria.
 
16
EDELSBRUNNER, I-I. 1982. Intersection problems in computational geometry. Institute for Information Processing Rept. 93, Technical Univ. of Graz, Graz, Austria.
 
17
EDELSBRUNNER, H. 1983a. A new approach to rectangle intersections: Part I. Int. J. Comp. Math. 13, 3-4, 209-219.
 
18
EDELSBRUNNER, H. 1983b. A new approach to rectangle intersections: Part II. Int. J. Comput. Math. 13, 3-4, 221-229.
 
19
20
 
21
FINKEL, R. A., AND BENTLEY, J. L. 1974. Quad trees: A data structure for retrieval on composite keys. Acta Inf. 4, 1, 1-9.
22
 
23
GUIBAS, L. J., AND SEOGEWICK, R. 1978. A dichromatic framework for balanced trees. In Proceedings of the 19th Annual IEEE Symposium on the Foundations of Computer Science (Ann Arbor, Mich., Oct.). IEEE, New York, pp. 8-21.
 
24
25
 
26
HARARY, F. 1969. Graph Theory. Addison-Wesley, Reading, Mass., 1969.
 
27
HINRICHS, K. 1985a. The grid file system: Implementation and case studies of applications. Ph.D. dissertation, Institut fur Informatik, ETH, Zurich, Switzerland.
 
28
 
29
HINRICHS, K., AND NIEVERGELT, J. 1983. The grid file: A data structure designed to support proximity queries on spatial objects. In Proceedings of the WG'83 (International Workshop on Graphtheoretic Concepts in Computer Science), M. Nagl and J. Perl, Eds. (Trauner Verlag, Linz, Austria), pp. 100-113.
 
30
 
31
HUNTER, G. M., AND STEIGLITZ, K. 1979. Operations on images using quad trees. IEEE Trans. Pattern Anal. Mach. Intell. 1, 2 (Apr.), 145-153.
 
32
 
33
KLEE, V. 1977. Can the measure of U {a,, bi} be computed in less than O(n log n) steps? Am. Math. Monthly 84, 4 (Apr.), 284-285.
 
34
KLINGER, A. 1971. Patterns and search statistics. In Optimizing Methods in Statistics, J. S. Rustagi, Ed. Academic Press, Orlando, Fla., pp. 303-337.
 
35
 
36
LAUTHER, U. 1978. Four-dimensional binary search trees as a means to speed up associative searches in design rule verification of integrated circuits. J. Design Automat. Fault-Tolerant Comput. 2, 3 (July), 241-247.
 
37
LEE, D. T. 1983. Maximum clique problem of rectangle graphs. In Advances in Computing Research. Vol. 1, Computational Geometry. F. P. Preparata, Ed. JAI Press, Greenwich, Conn., pp. 91-107.
 
38
LEE, D. T., AND PREPARATA, F. P. 1982. An improved algorithm for the rectangle enclosure problem. J. Algorithms 3, 3 (Sept.), 218-224.
 
39
MATSUYAMA, T., HAO, L. V., AND NAGAO, M. 1984. A file organization for geographic information systems based on spatial proximity. Cornput. Vision Graph. Image Process. 26, 3 (June), 303-318.
 
40
MCCREIGHT, E. M. 1980. Efficient algorithms for enumerating intersecting intervals and rectangles. Report CSL-80-9, Xerox Palo Alto Research Center, Palo Alto, Calif. (June).
 
41
MCCREIGHT, E. M. 1985. Priority search trees. SIAM J. Comput. 14, 2 (May), 257-276.
42
 
43
ORENSTEIN, J. A. 1982. Multidimensional tries used for associative searching. Inf. Process. Lett. 14, 4 (June), 150-157.
 
44
OVERMARS, M. H. 1988. Geometric data structures for computer graphics: An overview. In Theoretical Foundations of Computer Graphics and CAD, R. A. Earnshaw, Ed. Springer-Verlag, Berlin, pp. 21-49.
 
45
 
46
47
48
 
49
ROSENBERG, J. B. 1985. Geographical data structures compared: A study of data structures supporting region queries. IEEE Trans. Comput. Aided Design 4, i (Jan.), 53-67.
 
50
51
 
52
SAMET, H. 1980. Region representation: Quadtrees from binary arrays. Comput. Graph. Image Process. 13, i (May), 88-93.
53
 
54
SAMET, H. 1989a. Fundamentals of Spatial Data Structures. Addison-Wesley, Reading, Mass.
 
55
 
56
SAMET, H., AND TAMMINEN, M. 1985. Computing geometric properties of images represented by linear quadtrees. IEEE Trans. Pattern Anat. Mach. InteU. 7, 2 (Mar.), 229-240.
 
57
58
 
59
SELLIS, T., ROUSSOPOULOS, N., AND FALOUTSOS, C. 1987. The R*-tree: A dynamic index for multidimensional objects. Computer Science TR-1795, Univ. of Maryland, College Park, Md.
 
60
 
61
SHAMOS, M. I., AND HOEY, D. 1976. Geometric intersection problems. In Proceedings of the 17th Annual IEEE Symposium on the Foundations of Computer Science (Houston, October). IEEE, New York, pp. 208-215.
 
62
SHNEIER, M. 1981. Calculations of geometric properties using quadtrees, Comp. Graph. Image Process. 16, 3 (July), 296-302.
 
63
SIX, H. W., AND WOOD, D. 1980. The rectangle intersection problem revisited. BIT 20, 4, 426- 433.
 
64
SIX, H. W., AND WOOD, D. 1982. Counting and reporting intersections of d-ranges. IEEE Trans. Comput. 31, 3 (March), 181-187.
 
65
STONEBRAKER, M., SELLIS, T., AND HANSON, E. 1986. An analysis of rule indexing implementations in data base systems. In Proceedings of the 1st International Conference on Expert Database Systems (Charleston, S.C., Apr.), pp. 353- 364.
 
66
TARJAN, R. E. 1983. Updating a balanced search tree in 0(1) rotations. Inf. Process. Lett. 16, 5 (June), 253-257.
 
67
 
68
VAISHNAVI, V., AND WOOD, D. 1980. Data structures for the rectangle containment and enclosure problems. Comp. Graph. Image Process. 13, 4 (Aug.), pp. 372-384.
 
69
VAISHNAVI, V., AND WOOD, D. 1982. Rectilinear line segment intersection, layered segment trees and dynamization. J. Algorithms 3, 2 (June), pp. 160- 176.
 
70
VAN LEEUWEN, J. AND WOOD, D. 1981. The measure problem for rectangular ranges in d-space. J. Algorithms 2, 3 (Sept.), 282-300.
 
71
VOELCKER, H. B., AND REQUICHA, A. A. G. 1977. Geometric modeling of mechanical parts and processes. IEEE Comp. 10, 12 (Dec.), 48-57.
 
72
WEIDE, B. W. 1978. Statistical methods in algorithm design and analysis. Res. Rep. CMU-CS-78-142, Computer Science Department, Carnegie-Mellon University, Pittsburgh, Pa.

CITED BY  14