In order to adapt to changes in industrial world (customers and markets) and to competition, to create economic or strategic partnerships with external players or simply to integrate a connector to exchange information between the various services and software of a company, it is essential to have the necessary software tools (by development or deployment) that guarantee effective communication between the various parties, which are often heterogeneous and not known in advance, and overcome certain difficulties such as the multiplicity of information sources and the quality of the data.In such a context, the exchange or migration of data is a critical step. In order to facilitate the exchange, our approach aims at implementing federated interoperability with automated model transformation, supported by an interoperability evaluation, in order to ensure data retention despite the unforeseen uses (for example, some fields divert or mislabel) and to ensure their consistency during the transformation.
The problem of schema matching is one of the main tasks in several processes dealing with databases, data transformation and data integration. Schema matching involves the generation and linking of heterogeneous schemas. This heterogeneity makes the implementation of a solution difficult and the sources of heterogeneity are multiple. The need for a methodology that can adapt to the largest number of points of variation is necessary. In this paper, we propose a flexible, global and generic approach to meet this need where the main idea is to exploit as much information available in the schemas and this through the implementation of federated interoperability that allows the interpretation of information on the fly. To this end, the approach uses graph theory, more specifically hypergraphs and optimization models for the modelling of schemas and the automation of the solution of the schema matching problem by converting it into a graph matching problem. Our contributions include the proposal of an optimization model to add specific constraints to the schema matching problem, as well as the propagation algorithms to propagate the matching relationships between the source and target schemas. The approach is finally evaluated on two use cases, one academic and one industrial.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.