SMDM

Xiaoyuan Wang, Xingzhi Sun, Feng Cao, Li Ma, Nick Kanellos, Kang Zhang, Yue Pan, Yong Yu

Proceedings of the VLDB Endowment · 2009 · 15 citations · 2 references

Concepts

TL;DR

The authors propose and implement Semantic Master Data Management (SMDM) to enhance existing MDM solutions for evolving business requirements and novel enterprise applications. SMDM extends IBM MDM by publishing relational master data as a virtual RDF store, adding core and domain ontologies for instant reasoning, and demonstrating use cases on insurance company data. SMDM enables cross‑domain data linking, implicit relationship discovery, and declarative business policy definition, allowing companies to customize applications and services on demand within the MDM hub.

Abstract

Motivated by evolving business requirements and novel enterprise applications, we propose and implement the Semantic Master Data Management (SMDM), a semantics-level enhancement to the existing MDM solutions. The SMDM system publishes relational-based master data as virtual RDF store, and injects instantaneous reasoning capabilities into semantic queries. Two kinds of ontologies are introduced to the system, the core MDM ontology and the external imported domain ontology. SMDM enables data linking among multi-domains, implicit relationship discovery, and declarative definition and extension of business policies and entities. Based on these functions, modern companies can customize their applications and services on demand within the MDM hub. In the demonstration, we build the system environment based on IBM's MDM solution, and run the use cases on the master data of an insurance company.

References

2