Jens Lemcke

Computing a Canonical Hierarchical Schema

We present a novel approach to constructing a canonical data model from a set of hierarchical schemas. Canonical data model is a well-known pattern for enterprise integration and the integral enabler for many business applications such as business warehousing, business intelligence, data cleansing, and forsustainable business-to-business integration. After knowing the correspondences between schemas by applying existing schema or ontology matching, building the overarching canonical schema remains. A canonical schema must be able to integrate extremely different and even conflicting structures. Furthermore, the schema should exhibit the most commonly used structures of the sources and be stable with respect to the order of importing. Due to these properties, the manual construction is cumbersome and error-prone and becomes a major cost driver of integration projects. Our approach models that task as finding an optimal solution of a constraint satisfaction problem. Our comparison with manual integration shows that our prototype quickly reduces human effort by multiple person days with growing size of the integration task. With our techniques as a baseline, data models of enterprise applications can be converged and kept in synch to reduce integration costs in the long run.

Managing Variants of USDL

Different variants of USDL are required for different contexts. This is already shown by the Legal Module which requires different contents depending on the jurisdiction of a country. The issue aggravates if more and more parameters are relevant to determine the correct variant. The chapter presents one possible solution for variant management consisting of a canonical data model, a context driver mechanism, governance processes, and appropriate tooling. Although the solution for variant management is targeted at existing business documents, such as a purchase order, it provides a powerful and adequate means for dealing with USDL variants as well.

A Refined Canonical Data Model for Multi-schema Integration and Mapping

Current approaches in e-Business schema mapping and matching focus on pair-wise comparison of schemas. This paper gives an overview of how n-way comparison of e-Business schemas via a canonical data model can benefit to schema matching and mapping processes. We construct a refined canonical data model by integrating a set of input schemas into one comprehensive representation. This canonical data model represents the closure of all integrated schemas. In several use cases, the advantages of the canonical data model are demonstrated. Our work is based on excerpts from realistic input schemas and provides a concrete, ideal canonical data model as a reference for further research.