Generalization for calendar attributes using domain generalization graphs

Abstract

The paper addresses the problem of generalizing temporal data based on calendar (date and time) attributes The proposed method is based on a domain generalization graph, i.e., a lattice defining a partial order that represents a set of generalization relations for the attribute. The authors specify the components of a domain generalization graph suited to calendar attributes. They define granularity, subset, lookup, and algorithmic methods for specifying generalizations between calendar domains. To reduce the size of the domain generalization graph used in generalization and the number of results shown to the user, they use six types of pruning: reachability pruning, preliminary manual pruning, data range pruning, previous discard pruning, pregeneralization manual pruning, and post generalization pruning.