Cost modeling of spatial operators using non-parametric regression

Information Sciences

Volumn 177, Issue 2, 2007, Pages 607-631

  Jiang, Songtao ^a   Lee, Byung Suk ^a   He, Zhen ^b  

^a University of Vermont ^*  (United States)

^b LA TROBE UNIVERSITY   (Australia)

Author keywords

Cost model; Non parametric regression; Spatial database; Spatial operator

Indexed keywords

COSTS; DATA PROCESSING; DATA STRUCTURES; OPTIMIZATION; PARAMETER ESTIMATION; QUERY LANGUAGES; REGRESSION ANALYSIS;

COST MODEL; NON-PARAMETRIC REGRESSION; SPATIAL DATABASE; SPATIAL OPERATORS;

DATABASE SYSTEMS;

EID: 33750324776     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2006.04.010     Document Type: Article

References (70)

1. A. Aboulnaga, J. Naughton, Accurate estimation of the cost of spatial selections, in: Proceedings of the 16th IEEE International Conference on Data Engineering, San Diego, CA, 2000, pp. 123-134.
2. S. Acharya, V. Poosala, S. Ramaswamy, Selectivity estimation in spatial database, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Philadelphia, PA, 1999, pp. 13-24.
3. W. Aref, H. Samet, Optimization strategies for spatial query processing, in: Proceedings of the 17th International Conference on Very Large Data Bases, Barcelona, Spain, 1991, pp. 81-90.
4. Arya S., Mount D.M., Netanyahu N.S., Silverman R., and Wu A. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. Journal of ACM 45 6 (1998) 891-923
5. C. Baptista, A geolibrary for multimedia data sets: design and implementation issues, in: Proceedings of the ACM symposium on Applied computing, Madrid, Spain, 2002, pp. 488-492.
6. *-tree: an efficient and robust access method for points and rectangles, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Atlantic City, NJ, 1990, pp. 322-331.
7. A. Belussi, C. Faloutsos, Estimating the selectivity of spatial queries using the 'correlation' fractal dimension, in: Proceedings of the 21st International Conference on Very Large Data Bases, Zurich, Switzerland, 1995, pp. 299-310.
8. S. Berchtold, C. Böhm, D. Keim, H. Kriegel, A cost model for nearest neighbor search in high-dimensional data spaces, in: Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of database systems, New York, NY, 1997, pp. 78-86.
9. S. Berchtold, D. Keim, H. Kriegel, The X-tree: an index structure for high-dimensional data, in: Proceedings of the International Conference on Very Large Data Bases, Bombay, India, 1996, pp. 28-39.
10. Bishop J. Urban Areas of Counties in the Pennsylvania State (1998), Pennsylvania State University, University Park, PA. Available from:
11. Boulos J., and Ono K. Cost estimation of user-defined methods in object-relational database systems. SIGMOD Record 28 3 (1999) 22-28
12. Böhm C. A cost model for query processing in high dimensional data spaces. ACM Transactions on Database Systems 25 2 (2000) 129-178
13. Böhm C., Berchtold S., and Keim D. Searching in high-dimensional spaces - index structures for improving the performance of multimedia databases. ACM Computing Surveys 33 3 (2001) 322-373
14. Bookstein F. Principal warps: thin plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (1989) 567-585
15. T. Brinkhoff, H. Kriegel, R. Schneider, B. Seeger, Multi-step processing of spatial joins, in: Proceedings of the ACM SIGMOD Inernational Conference on Management of Data, Minneapolis, MN, 1994, pp. 237-246.
16. Cenzov N. Evaluation of an unknown distribution density from observations. Soviet Mathematical Doklady 3 (1962) 1559-1562
17. Y. Chen, Y. Hung, C. Fuh, Fast algorithm for nearest neighbor search based on a lower bound tree, in: Proceedings of the IEEE International Conference on Computer Vision, Vancouver, Canada, 2001, pp. 446-453.
18. A. Corral, Y. Manolopoulos, Y. Theodoridis, M. Vassilakopoulos, Closest pair queries in spatial databases, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, TX, 2000, pp. 189-200.
19. R. deSilva, K. Wood, J. Beaman, An algebraic approach to geometric query processing in CAD/CAM applications, in: Proceedings of the ACM Symposium on Solid Modeling Foundations and CAD/CAM Applications, Austin, Texas, 1991, pp. 73-86.
20. Cleveland W.S. Robust locally-weighted regression and smoothing scatterplots. Journal of American Statistical Association 74 (1979) 829-836
21. Cleveland W., and Grosse E. Computational methods for local regression. Statistics and Computing 1 (1991) 47-62
22. Egenhofer M. Spatial SQL: a query and presentation language. IEEE Transactions on Knowledge and Data Engineering 6 1 (1994) 86-95
23. C. Faloutsos, I. Kamel, Beyond uniformity and independence: analysis of R-trees using the concept of fractal dimension, in: Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Minneapolis, MN, 1994, pp. 4-13.
24. C. Faloutsos, T. Sellis, N. Roussopoulos, Analysis of object oriented spatial access methods, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, San Francisco, CA, 1987, pp. 426-439.
25. Fox J. Nonparametric Simple Regression: Smoothing Scatterplots (2002), Sage Publications, Thousand Oaks, California
26. Friedman J., Baskett F., and Shustek L. An algorithm for finding nearest neighbors. IEEE Transactions on Computers 24 10 (1975) 1000-1006
27. Friedman J., Bentley J., and Finkel R. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software 3 3 (1977) 209-226
28. Fukunaga K., and Narendra P. A branch and bound algorithm for computing k-nearest neighbors. IEEE Transactions on Computers 24 (1975) 750-753
29. Gaede V., and Günther O. Multidimensional access methods. Computing Surveys 30 2 (1998) 170-231
30. G. Gardarin, F. Sha, Z.-H. Tang, Calibrating the query optimizer cost model of IRO-DB: an object-oriented federated database system, in: Proceedings of the 22nd International Conference on Very Large Data Bases, Bombay, India, 1996, pp. 378-389.
31. Gorden L., and Olshen R.A. Consistent nonparametric regression from recursive partioning schemes. Journal of Multivariate Analysis 10 (1980) 611-627
32. Guting R. An introduction to spatial database systems. VLDB Journal 3 (2004) 357-399
33. A. Guttman, R-trees: a dynamic index structure for spatial searching, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Boston, MA, 1984, pp. 47-57.
34. R. Helm, K. Marriott, M. Odensky, Constraint-based query optimization for spatial databases, in: Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Denver, CO, 1991, pp. 181-191.
35. Huber P. Projection pursuit. Annals of Statistics 13 (1985) 435-475
36. N. Katayama, S. Satoh, The SR-tree: an index structure for high-dimensional nearest neighbor queries, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Tucson, AZ, 1997, pp. 368-380.
37. Kim B., and Park S. A fast k nearest neighbor finding algorithm based on the ordered partition. IEEE Transactions on Patten Analysis and Machine Intelligence 8 (1986) 761-766
38. B. Kobler, J. Berbert, NASA earth observing system data information system (EOSDIS), in: Digest of Papers: 11th IEEE Symposium on Mass Storage Systems, Monterey, California, 1991, pp. 18-19.
39. Korn F., Pagel B., and Faloutsos C. On the "dimensionality curse" and the "self-similarity blessing". IEEE Transactions on Knowledge and Data Engineering 13 1 (2001) 96-111
40. H. Kriegel, P. Kunath, M. Pfeifle, M. Renz, Statistic driven acceleration of object-relational spatial index structures, in: Proceedings of the International Conference on Database Systems for Advanced Applications, Jeju Island, Korea, 2004, pp. 169-183.
41. H. Kriegel, P. Knuath, M. Pfeifle, M. Pötke, M. Renz, P.-M. Straus, Stochastic driven relational R-tree, in: Brazilian Symposium on Geoinformatics, São Paulo, Brazil, 2003. Avaolabel from: .
42. H. Kriegel, A. Muller, M. Potke, T. Seidl, Spatial data management for computer-aided design, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Santa Barbara, CA, 2001, p. 614.
43. R. Lanzelotte, P. Valduriez, M. Zait, On the effectiveness optimization search strategies for parallel execution spaces, in: Proceedings of the 19th International Conference on Very Large Data Bases, Dublin, Ireland, 1993, pp. 493-504.
44. B. Lee, V. Kannoth, J. Buzas, Statistical cost-modeling of financial time series functions, Journal of Computers and Applications, in press.
45. Lin K., Jagadish H., and Faloutsos C. The TV-tree: an index structure for high-dimensional data. VLDB Journal 3 (1994) 517-542
46. Loftsgaarden D., and Queensberry G. A nonparametric estimate of a multivariate density function. Annals of Mathematics and Statististics 36 (1965) 1049-1051
47. Meinguet J. Multivariate interpolation at arbitrary points made simple. Journal of Applied Mathematical Physics 30 (1979) 292-304
48. A. Nanopoulos, Y. Theodoridis, Y. Manolopoulos, An efficient and effective algorithm for density biased sampling, in: Proceedings of the International Conference on Information and Knowledge Management, McLean, VA, 2002, pp. 63-68.
49. Orenstein J., and Manola F. Probe spatial data modeling and query processing in an image database application. IEEE Transactions on Software Engineering 14 5 (1998) 611-629
50. B. Pagel, H. Six, H. Toben, P. Widmayer, Towards an analysis of range query performance in spatial data structures, in: Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Washington, DC, 1993, pp. 214-221.
51. A. Papadopoulos, Y. Manolopoulos, Performance of nearest neighbor queries in R-trees, in: Proceedings of the International Conference on Database Theory, Delphi, Greece, 1997, pp. 394-408.
52. F. Rao, L. Zhang, X. Yu, Y. Li, Y. Chen, Spatial hierarchy and OLAP-favored search in spatial data warehouse, in: Proceedings of the International Workshop on Data Warehousing and OLAP, New Orleans, LA, 2003, pp. 48-55.
53. Reinsch H. Smoothing by spline functions. Numerische Mathematik 10 (1967) 177-183
54. E. Riedel, G. Gibson, C. Faloutsos, Active storage for large-scale data mining and multimedia, in: Proceedings of the 24th International Conference on Very Large Data Bases, New York, NY, 1998, pp. 62-73.
55. J.T. Robinson, The K-D-B-tree: a search structure for large multidimensional dynamic indexes, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Ann Arbor, MI, 1981, pp. 10-18.
56. Rosenblatt M. Remarks on some nonparametric estimates of a density function. Annals of Mathematics and Statistics 27 (1956) 642-669
57. N. Roussopoulos, S. Kelley, F. Vincent, Nearest neighbor queries, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, San Jose, CA, 1995, pp. 71-79.
58. Rupert D., Wand M., and Carroll R. Semiparametric Regression (2003), Cambridge University Press, Cambridge, United Kingdom
59. T. Sellis, N. Roussopoulos, C. Faloutsos, The R+-tree: a dynamic index for multi-dimensional objects, in: Proceedings of the 13th International Conference on Very Large Data Bases, Brighton, England, 1987, pp. 507-518.
60. Shekhar S., and Chawla S. Spatial Databases: a Tour (2003), Prentice-Hall, Upper Saddle River, NJ
61. Stone C. Consistent nonparametric regression (with discussion). Annals of Statistics 5 (1977) 595-645
62. Theodoridis Y., Stefanakis E., and Sellis T. Efficient cost models for spatial queries using R-trees. IEEE Transactions on Knowledge and Data Engineering 12 1 (2000) 19-32
63. Wang W., Yang J., and Muntz R. PK-tree: a spatial index structure for high dimensional point data. Information Organization and Databases: Foundations of Data Organization (1998), Kluwer Academic Publishers, Norwell, Massachusetts 281-293
64. G. Wahba, Spline models for observational data, CBMS-NSF Regional Conference Series in Applied Mathematics, number 59, Society for Industrial & Applied Mathematics (SIAM), Philadelphia, Pennsylvania, 1998.
65. Wahba G., and Wendelberger J. Some new mathematical methods for variational objective analysis using splines and cross-validation. Monthly Weather Review 108 (1980) 1122-1145
66. Watson G. Smooth regression analysis. Sankhya - The Indian Journal of Statistics 26 (1964) 359-372
67. Whaba G. Optimal convergence properties of variable knot, kernel, and orthogonal series methods for density estimation. Annals of Statistics 3 (1975) 15-29
68. C. Yu, B. Ooi, K. Tan, H. Jagadish, Indexing the distance: an efficient method to KNN processing, in: Proceedings of the International Conference on Very Large Data Bases, Roma, Italy, 2001, pp. 421-430.
69. Open GIS consortium, Open GIS simple feature specification for SQL (revision 1.1), OpenGIS Project Document 99-049, May 5, 1999. Available from: .
70. SAS OnlineDoc: SAS/STAT, SAS Institute Inc., Cary, NC, 2004. Available from: .