Abstract
This work is a contribution to the developing literature on multi-resolution data models. It considers operations for model-oriented generalization in the case where the underlying data is structured as a graph. The paper presents a new approach in that a distinction is made between generalizations that amalgamate data objects and those that select data objects. We show that these two types of generalization are conceptually distinct, and provide a formal framework in which both can be understood. Generalizations that are combinations of amalgamation and selection are termed simplifications, and the paper provides a formal framework in which simplifications can be computed (for example, as compositions of other simpliffcations). A detailed case study is presented to illustrate the techniques developed, and directions for further work are discussed.
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Stell, J.G., Worboys, M.F. (1999). Generalizing Graphs Using Amalgamation and Selection. 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_4
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DOI: https://doi.org/10.1007/3-540-48482-5_4
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