Iterative Effort Reduction in B2B Schema Integration via a Canonical Data Model

Abstract

Nowadays, B2B integration still remains a big cost driver for companies. On the one hand, standardization efforts were able to reduce the mapping effort between e-Business schemas. However, the effort for creating customized messages from the huge and underspecified standard templates increased. Due to the myriad of different requirements by different companies, a great variety of standards coexist. Instead of forcing companies to adopt huge standards, this article propagates an iteratively improving schema and mapping derivation system in the cloud. Thus, we provide flexibility, but streamline companies' integration efforts based on an evolving canonical data model. This approach reduces the need for explicit standardization to a minimum. Our simulation based on real schemas shows a potential to reduce guide creation effort by 50% and mapping effort from 6% to almost 100%.