On the transferability of process-oriented cases

Abstract

This paper studies the feasibility of using transfer learning for process-oriented case-based reasoning. The work introduces a novel approach to transfer workflow cases from a loosely related source domain to a target domain. The idea is to develop a representation mapper based on workflow generalization, workflow abstraction, and structural analogy between the domain vocabularies. The approach is illustrated by a pair of sample domains in two sub-fields of customer relationship management that have similar process objectives but different tasks and data to fulfill them. An experiment with expert ratings of transferred cases is conducted to test the feasibility of the approach with promising results for workflow modeling support.