Value range queries on earth science data via histogram clustering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2007, Issue , 2001, Pages 62-76

  Yang, Ruixin ^a   Yang, Kwang Su ^a   Kafatos, Menas ^a   Sean Wang, X ^a  

^a GEORGE MASON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EARTH SCIENCES; GRAPHIC METHODS; QUERY PROCESSING; REMOTE SENSING;

ACCURACY ASSESSMENT; ACTUAL DATA VALUES; CLUSTERING TECHNIQUES; EARTH SCIENCE DATA; HISTOGRAM CLUSTERING; INDEX SYSTEMS; LARGE VOLUMES; REMOTE SENSING DATA;

DATA MINING;

EID: 84943259169     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45244-3_6     Document Type: Conference Paper

References (25)

1. G. Asrar and R. Greenstone, eds., 1999 EOS Reference Handbook. NASA (Washington, DC), 1999.
2. NASA’s ESIP, “Earth Science Information Partners,” 2000. http://www.esipfed.org/.
3. Content-based Search and Data Mining Cluster of ESIP, “Science Scenarios for Content-based Search and Data Mining,” 2000. http://esipfed.org:8080/Clusters/Content Based/sci scen.html.
4. J. Gary, A. Bosworth, A. Layman, and H. Pirahesh, “Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-tabs, and Sub-totals,” in Proceedings of IEEE Conf. on Data Engineering, pp. 152–159, 1996.
5. J. M. Hellerstein, P. J. Haas, and H. J. Wang, “Online Aggregation,” in Proce. 1997 ACM SIGMOD Intl. Conf. Management of Data, pp. 171–182, ACM Press, 1997.
6. P. J. Haas, “Techniques for Online Exploration of Large Object-Relational Databases,” in Proceedings of the 11th International Conference on Scientific and Statistical Database Management (Z. M. Ozsoyoglu, G. Ozsoyoglu, and W.-C. Hou, eds.), pp. 4–12, IEEE, Computer Society, 1999.
7. V. Poosala, Y. Ioannidis, P. Haas, and E. Shekita, “Improved Histograms for Selectivity Estimation of Range Predicates,” in Proce. 1996 ACM SIGMOD Intl. Conf. Management of Data, pp. 294–305, ACM Press, 1996.
8. V. Poosala and V. Ganti, “Fast Approximation Answers to Aggregate Queries on a Data Cube,” in Proceedings of the 11th International Conference on Scientific and Statistical Database Management (Z. M. Ozsoyoglu, G. Ozsoyoglu, and W.-C. Hou, eds.), pp. 24–33, IEEE, Computer Society, 1999.
9. D. Carr and A. R. Olsen, “Simplifying Visual Applearance by Sorting: An Example Using 159 AVHRR Classes,” Statistical Computing & Statistical Graphics Newsletter, pp. 10–16, April 1996.
10. A. K. Jain, M. N. Murty, and P. J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264–323, 1999.
11. B. Everitt, Cluster Analysis. John Wiley & Sons, 1993.
12. J. Puzicha, T. Hofmann, and J. M. Buhmann, “Histogram Clustering for Unsupervised Segmentation and Image Retrieval,” Pattern Recognition Letters, vol. 20, pp. 899–909, 1999.
13. Z. Li, X. S. Wang, M. Kafatos, and R. Yang, “A Pyramid Data Model for Supporting Content-based Browsing and Knowledge Discovery,” in Proceedings of the 10th International Conference on Scientific and Statistical Database Management (M. Rafanelli and M. Jarke, eds.), pp. 170–179, IEEE, Computer Society, 1998.
14. W. Venables and B. Ripley, Modern Applied Statistics with S-Plus. Springer- Verlag, 1994.
15. R. T. Ng and J. Han, “Efficient and Effective Clustering Methods for Spatial Data Mining,” in Proce. of the 20th VLDB Conference Santiago, Chile, pp. 144–155, 1994.
16. T. Zhang, R. Ramakrishnan, and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Databases,” SIGMOD Rec., vol. 25, no. 2, pp. 103–114, 1996.
17. Z. Huang, “Extension to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values,” Data Mining and Knowledge Discovery, vol. 2, no. 3, pp. 283–304, 1998.
18. H. Kyle, J. McManus, S. Ahmad, and et al., Climatology Interdisciplinary Data Collection, Volumes 1-4, Monthly Means for Climate Studies. NASA Goddard DAAC Science Series, Earth Science Enterprise, National Aeronautics & Space Administration, NP-1998(06)-029-GSFC, 1998.
19. A. P. Cracknell, The Advanced Very High Resolution Radiometer. Taylor&Francis Inc., 1997.
20. J. D. Jobson, Applied Multivariate Data Analysis, vol. 2. Springer, 1992.
21. A. Guttman, “R-trees: A Dynamic Index Structure for Spatial Searching,” in Proc. ACM SIGMOD, pp. 47–57, June 1984.
22. F. Korn, T. Johnson, and H. V. Jagadish, “Range Selectivity Estimation for Continuous Attributes,” in Proceedings of the 11th International Conference on Scientific and Statistical Database Management (Z. M. Ozsoyoglu, G. Ozsoyoglu, and W.-C. Hou, eds.), pp. 244–253, IEEE, Computer Society, 1999.
23. M. Kafatos, Z. Li, R. Yang, and et al., “The Virtual Domain Application Data Center: Serving Interdisciplinary Earth Scientists,” in Proceedings of the 9th International Conference on Scientific and Statistical Database Management (D. Hansen and Y. Ioannidis, eds.), pp. 264–276, IEEE, Computer Society, 1997.
24. M. Kafatos, X. Wang, Z. Li, R. Yang, and D. Ziskin, “Information Technology Implementation for a Distributed Data System Serving Earth Scientists: Seasonal to Interannual ESIP,” in Proceedings of the 10th International Conference on Scientific and Statistical Database Management (M. Rafanelli and M. Jarke, eds.), pp. 210–215, IEEE, Computer Society, 1998.
25. R. Yang, C. Wang, M. Kafatos, X. Wang, and T. El-Ghazawi, “Remote Data Access via SIESIP Distributed Information System,” in Proceedings of the 11th International Conference on Scientific and Statistical Database Management (Z. M. Ozsoyoglu, G. Ozsoyoglu, and W.-C. Hou, eds.), p. 284, IEEE, Computer Society, 1999.