This Article 
 Bibliographic References 
 Add to: 
Object-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes
November/December 2000 (vol. 12 no. 6)
pp. 938-958

Abstract—With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and OLAP of spatial data. In this paper, we study methods for spatial OLAP, by integration of nonspatial OLAP methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies proposed, including approximation and selective materialization of the spatial objects resulted from spatial OLAP operations. The focus of our study is on a method for spatial cube construction, called object-based selective materialization, which is different from cuboid-based selective materialization proposed in previous studies of nonspatial data cube construction. Rather than using a cuboid as an atomic structure during the selective materialization, we explore granularity on a much finer level, that of a single cell of a cuboid. Several algorithms are proposed for object-based selective materialization of spatial data cubes and the performance study has demonstrated the effectiveness of these techniques.

[1] S. Agrawal, R. Agrawal, P.M. Deshpande, A. Gupta, J.F. Naughton, R. Ramakrishnan, and S. Sarawagi, "On the Computation of Multidimensional Aggregates," Proc. 22nd Int'l Conf. Very Large Databases, pp. 506-521,Mumbai (Bombay), India, Sept. 1996.
[2] S. Chaudhuri and U. Dayal, “An Overview of Data Warehousing and OLAP Technology,” SIGMOD Record, vol. 26, no. 1, Mar. 1997.
[3] M. Egenhofer, “Spatial SQL: A Query and Presentation Language” IEEE Trans. Knowledge and Data Eng., vol. 6, no. 1, pp. 86-95, Feb. 1994.
[4] M. Ester, H.-P. Kriegel, and J. Sander, “Spatial Data Mining: A Database Approach,” Proc. Fifth Int'l Symp. Spatial Databases (SSD), pp. 47–66, 1997.
[5] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases,” Proc. Second Int'l Conf. Knowledge Discovery and Data Mining (KDD '96), pp. 226–231, Aug. 1996.
[6] C. Faloutsos and K.I. Lin, “Fastmap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets,” Proc. SIGMOD, Int'l Conf. Management of Data, pp. 163-174, 1995.
[7] Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, eds., AAAI/MIT Press, 1996.
[8] J. Gray et al., "Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals," J. Data Mining and Knowledge Discovery, Vol. 1, No. 1, 1997, pp. 29-53.
[9] O. Günther, “Efficient Computation of Spatial Joins,” Proc. Ninth Conf. Data Eng., pp. 50-60, 1993.
[10] R.H. Gueting, “An Introduction to Spatial Database Systems,” VLDB J., vol.3, no. 4, pp. 357-399, Oct. 1994.
[11] J. Han, K. Koperski, and N. Stefanovic, “GeoMiner: A System Prototype for Spatial Data Mining,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 553–556, 1997.
[12] V. Harinarayan, A. Rajaraman, and J. D. Ullman, “Implementing Data Cubes Efficiently,” Proc. ACM SIGMOD, pp. 205-216, June 1996
[13] W.H. Inmon, Building the Data Warehouse, second ed. John Wiley and Sons, 1996.
[14] D.A. Keim, H.-P. Kriegel, and T. Seidl, “Supporting Data Mining of Large Databases by Visual Feedback Queries,” Proc. 10th Int'l Conf. Data Eng., pp. 302-313, 1994.
[15] R. Kimball, The Data Warehouse Toolkit. New York: John Wiley&Sons, 1996.
[16] E.M. Knorr and R.T. Ng, “Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining,” IEEE Trans. Knowledge and Data Eng., vol. 8, no. 6, pp. 884–897, 1996.
[17] K. Koperski and J. Han, “Discovery of Spatial Association Rules in Geographic Information Databases,” Proc. Fourth Int'l Symp. Large Spatial Databases (SSD '95), pp. 47–66, Portland, Maine, Aug. 1995.
[18] K. Koperski, J. Han, and N. Stefanovic, “An Efficient Two-Step Method for Classification of Spatial Data,” Proc. Eighth Symp. Spatial Data Handling, pp. 45–55, 1998.
[19] W. Lu, J. Han, and B.C. Ooi, “Knowledge Discovery in Large Spatial Databases,” Proc. Far East Workshop Geographic Information Systems, pp. 275–289, June 1993.
[20] R.T. Ng and J. Han, "Efficient and Effective Clustering Methods for Spatial Data Mining," Proc. 20th Int'l Conf. Very Large Databases, Morgan Kaufmann, 1994, pp. 144-155.
[21] K. Ross and D. Srivastava, “Fast Computation of Sparse Datacubes,” Proc. 1997 Int'l Conf. Very Large Data Bases, pp. 116–125, Aug. 1997.
[22] A. Silberschatz, M. Stonebraker, and J. Ullman, “Database Research: Achievements and Opportunities Into the 21st Century,” ACM Special Interest Group on Management of Data Record (SIGMOD Record '96), vol. 25, no. 1 Mar. 1996.
[23] N. Stefanovic, “Design and Implementation of On-Line Analytical Processing (OLAP) of Spatial Data,” master's thesis, Simon Fraser Univ., Canada, Sept. 1997.
[24] Y. Zhao, P.M. Deshpande, and J.F. Naughton, “An Array-Based Algorithm for Simultaneous Multidimensional Aggregations,” Proc. 1997 ACM-SIGMOD Conf. Management of Data, pp. 159-170, May 1997.
[25] X. Zhou, D. Truffet, and J. Han, “Efficient Polygon Amalgamation Methods for Spatial Olap and Spatial Data Mining,” Proc. Sixth Int'l Symp. Large Spatial Databases (SSD '99), pp. 167–187, July 1999.

Index Terms:
Data warehouse, data mining, online analytical processing (OLAP), spatial databases, spatial data analysis, spatial OLAP.
Nebojsa Stefanovic, Jiawei Han, Krzysztof Koperski, "Object-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes," IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 6, pp. 938-958, Nov.-Dec. 2000, doi:10.1109/69.895803
Usage of this product signifies your acceptance of the Terms of Use.