Measuring Semantic and Structural Information for Data Oriented Workflow Retrieval with Cost Constraints

Abstract

The reuse of data oriented workflows (DOWs) can reduce the cost of workflow system development and control the risk of project failure and therefore is crucial for accelerating the automation of business processes. Reusing workflows can be achieved by measuring the similarity among candidate workflows and selecting the workflow satisfying requirements of users from them. However, due to DOWs being often developed based on an open, distributed, and heterogeneous environment, different users often can impose diverse cost constraints on data oriented workflows. This makes the reuse of DOWs challenging. There is no clear solution for retrieving DOWs with cost constraints. In this paper, we present a novel graph based model of DOWs with cost constraints, called constrained data oriented workflow (CDW), which can express cost constraints that users are often concerned about. An approach is proposed for retrieving CDWs, which seamlessly combines semantic and structural information of CDWs. A distance measure based on matrix theory is adopted to seamlessly combine semantic and structural similarities of CDWs for selecting and reusing them. Finally, the related experiments are made to show the effectiveness and efficiency of our approach.