Abstract
The polygon amalgamation operation computes the boundary of the union of a set of polygons. This is an important operation for spatial on-line analytical processing and spatial data mining, where polygons representing different spatial objects often need to be amalgamated by varying criteria when the user wants to aggregate or reclassify these objects. The processing cost of this operation can be very high for a large number of polygons. Based on the observation that not all polygons to be amalgamated contribute to the boundary, we investigate in this paper efficient polygon amalgamation methods by excluding those internal polygons without retrieving them from the database. Two novel algorithms, adjacency-based and occupancy-based, are proposed. While both algorithms can reduce the amalgamation cost significantly, the occupancy-based algorithm is particularly attractive because: 1) it retrieves a smaller amount of data than the adjacency-based algorithm; 2) it is based on a simple extension to a commonly used spatial indexing mechanism; and 3) it can handle fuzzy amalgamation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
D.J. Abel. SIRO-DBMS: A database toolkit for geographical information systems. Int. J. Geographical Information Systems, 4(3):443–464, 1989.
D.J. Abel and J.L. Smith. A data structure and algorithm based on a linear key for a rectangle retrieval problem. Computer Vision, Graphics and Image Processing, 24(1):1–13, 1983.
T. Brinkho, H.P. Kriegel, and B. Seeger. Efficient processing of spatial joins using R-trees. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 237–246, Washington, D. C., 1993.
S. Chaudhuri and U. Dayal. An overview of data warehousing and olap technology. SIGMOD Record, 26(1), 1997.
D.J. DeWitt and R.H. Gerber. Multiprocessor hash-based join algorithms. In Proc. 1985 Int. Conf. on Very Large Data Bases, pages 151–164, Austin, Texas, 1985.
M.J. Egenhofer. Reasoning about binary topological relationships. In LNCS 552: Proceedings of 2nd Int. Symp. on Spatial Databases (SSD’91), pages 143–160. Springer-Verlag, 1991.
M. Ester, H.-P. Kriegel, and J. Sander. Spatial data mining: A database approach. In LNCS 1262: Proceedings of the 5th Int. Symp. on Spatial Databases (SSD’97), pages 47–66, Berlin, Germany, 1997. Springer-Verlag.
M. Ester, H.-P. Kriegel, J. Sander, and X. XU. Density-connected sets and their application for trend detection in spatial databases. In Proc. 3rd Int. Conf. Knowledge Discovery and Data Mining (KDD’97), pages 10–15, Newport Beach, California, 1997.
V. Gaede. Optimal redundancy in spatial database systems. In M. J. Egenhofer and J. R. Herring, editors, LNCS 951: Proc. 4th Int. Symp. on Spatial Databases (SSD’95), pages 96–116, Portland, Maine, 1995. Springer-Verlag.
V. Gaede and O. Günther. Multidimensional access methods. ACM Computing Surveys, 30(2):170–231, 1998.
O. Günther. Efficient computation of spatial joins. In Proceedings of 9th International Conference on Data Engineering, pages 50–59, Vienna, Austria, 1993.
R.H. Güting. An introduction to spatial database systems. /LDB Journal, 3(4):357–399, 1994.
A. Guttman. R-trees: A dynamic index structure for spatial searching. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 47–54, Boston, Massachusetts, 1984.
J. Han, K. Koperski, and N. Stefanovic. GeoMiner: A system prototype for spatial data mining. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 560–563, Tucson, Arizona, 1997.
J. Han, N. Stefanovic, and K. Koperski. Selective materialization: An efficient method for spatial data cube construction. In Proc. Pacific-Asia Conf. on Knowledge Discovery and Data Mining, pages 144–158, Melbourne, Australia, 1998.
V. Harinarayan, A. Rajaraman, and J.D. Ullman. Implementing data cubes efficiently. In Proc. 1996 ACM SIGMOD Int. Conf. on Management of Data, pages 206–216, Montreal, Canada, 1996.
W. Lu, J. Han, and B.C. Ooi. Knowledge discovery in large spatial databases. In Proc. Far East Workshop Geographic Information Systems, pages 275–289, Singapore, 1993.
R. Ng and J. Han. Efficient and efective clustering method for spatial data mining. In Proceedings of 1994 International Conference on Very Large Data Bases, pages 144–155, Santiago, Chile, 1994.
J. Orenstein. Redundancy in spatial databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 294–305, Portland, Oregon, 1989.
J. Orenstein and F.A. Manola. Probe spatial data modeling and query processing in an image database application. IEEE Trans. on Software Eng., 14(5):611–629, 1988.
J.M. Patel and D.J. DeWitt. Partition based spatial-merge join. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 259–270, Montreal, Canada, 1996.
F.P. Preparata and M.I. Shamos. Computational Geometry: an introduction. Springer-Verlag, 1985.
H. Samet. Applications of Spatial Data Structures. Addison-Wesley, 1990.
T. Sellis, N. Roussopoulos, and C. Faloutsos. The R+-tree: a dynamic index for multi-dimensional objects. In Proc. 13th Int. Conf. on Very Large Data Bases, pages 3–11, Brighton, England, 1987.
D. Truffet and M.E. Orlowska. Two phase query processing with fuzzy approximations. In Proc. 4th ACM International Workshop on Advances in Geographic Information Systems (ACM-GIS’96), Rockville, USA, 1996.
X. Zhou, D.J. Abel, and D. Truffet. Data partitioning for parallel spatial join processing. In LNCS 1262: Proc. 5th Int. Symp. on Spatial Databases (SSD’97), pages 178–196, Berlin, Germany, 1997. Springer-Verlag.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, X., Truffet, D., Han, J. (1999). Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining. In: Güting, R.H., Papadias, D., Lochovsky, F. (eds) Advances in Spatial Databases. SSD 1999. Lecture Notes in Computer Science, vol 1651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48482-5_12
Download citation
DOI: https://doi.org/10.1007/3-540-48482-5_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66247-1
Online ISBN: 978-3-540-48482-0
eBook Packages: Springer Book Archive