Generalizing Graphs Using Amalgamation and Selection

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 simpliications, and the paper provides a formal framework in which simpliications can be computed (for example, as compositions of other simpliications). A detailed case study is presented to illustrate the techniques developed, and directions for further work are discussed.