Abstract
This paper derives and compares two strategies for minimizing topological constraints in a query expressed by a visual example: (1) elimination of topological relations that are implied uniquely by composition and (2) restriction to topological relations that relate near-neighbor objects, as determined by a Delaunay triangulation. In both cases, the query processing approach is to solve a constraint satisfaction problem over a graph of binary topological relations. Individuals and the combination of the composition- and neighborhood-based strategies were implemented and compared with respect to their ability to reduce topological constraints, and with respect to the quality of the results obtained by a similarity-based searching that uses these pre-processing strategies. The main conclusion of this work is that similarity queries that are formulated in a visual language should exploit the metric characteristics of the configuration, even if only topological constraints are considered for making matches.
Keywords
- Query Processing
- Delaunay Triangulation
- Constraint Satisfaction Problem
- Spatial Object
- Topological Relation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This work was partially funded by FONDECYT 1010897 and 7010897 and the National Imagery and Mapping Agency under grant number NMA202-97-1-1023. Max Egenhofer.s work is further support by grants from the National Science Foundation under grant numbers IIS-9970123 and EPS- 9983432, the National Imagery and Mapping Agency under grant numbers NMA201-01-1-2003, NMA201-00-1-2009, NMA401-02-1-2009, and National Institute of Environmental Health Sciences, NIH, under grant number 1 R 01 ES09816-01.
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Rodríguez, M.A., Egenhofer, M.J., Blaser, A.D. (2003). Query Pre-processing of Topological Constraints: Comparing a Composition-Based with Neighborhood-Based Approach. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds) Advances in Spatial and Temporal Databases. SSTD 2003. Lecture Notes in Computer Science, vol 2750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45072-6_21
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