An Ontology Approach for Manufacturing Enterprise Data Warehouses Development

Abstract:

Data warehouse (DW) is a powerful and useful technology for decision making in manufacturing enterprises. Because that the operational data often comes from distributed units for manufacturing enterprises, there exits an urgent need to study on the methods of integrating heterogonous data in data warehouse. In This paper, an ontology approach is proposed to eliminate data source heterogeneity. The approach is based on the exploitation of the application of domain ontology methods in data warehouse design, representing the semantic meanings of the data by ontology at database level and pushing the data as data resources to manufacturing units at data warehouse access level. The foundation of our approach is a meta-data model which consists of data, concept, ontology and resource repositories. The model is used in a shipbuilding enterprise data warehouse development project. The result shows that with the guide of the meta-data model, our ontology approach could eliminate the data heterogeneity.

• Periodical: Advanced Materials Research (Volume 215)
• Pages: 77-82
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.215.77
• Online since: March 2011
• Authors: B.Y. Xu, H.M. Cai, C. Xie
• Keywords: Data Warehouse (DW), Domain-Ontology,, Information System Integration

References

[1] F. McFadden, H.J. Watson: The world of data warehousing: issues and opportunities, Journal of Data Warehousing, Vol. 1 (1996), pp.61-71.

[2] K. Seonggyu: A model-based case adapter for data warehouse design, Proceedings of the 2008 International Conference on Information and Knowledge Engineering, pp.286-290.

[3] D. Dori, R. Feldman, A. Sturm: From conceptual models to schemata: An object-process-based data warehouse construction method, Information Systems, Vol. 33(2008), pp.567-593.

DOI: 10.1016/j.is.2008.02.002

[4] L. Zepeda, M. Celma, R. Zatarain: A Mixed Approach for Data Warehouse Conceptual Design with MDA, ICCSA 2008, pp.1204-1217.

DOI: 10.1007/978-3-540-69848-7_96

[5] S. L. Nimmagadda, H. Dreher, A. Rudra: Ontology of Western Australian petroleum data for effective data warehouse design and data mining, 2005 3rd IEEE International Conference on Industrial Informatics, pp.584-592.

DOI: 10.1109/indin.2005.1560442

[6] P. Panov, S. Dzeroski, L. N. Soldatova: OntoDM: An Ontology of Data Mining, 2008 IEEE International Conference on Data Mining Workshops, pp.752-760.

DOI: 10.1109/icdmw.2008.62

[7] Z. Zhang, S. Wang: A Framework Model Study for Ontology-driven ETL Processes, WiCOM 2008, pp.11397-11400.

[8] S. L. Nimmagadda, S. K. Nimmagadda, H. Dreher: Ontology based data warehouse modeling and managing ecology of human body for disease and drug prescription management, 2008 Second IEEE International Conference on Digital Ecosystems and Technologies, pp.212-220.

DOI: 10.1109/dest.2008.4635209

[9] O. Romero, A. Abelló: Automating Multidimensional Design from Ontologies, DOLAP'07, pp.1-8.

[10] C. Y. Lee: A knowledge management scheme for meta-data: an information structure graph, Decision Support Systems, Vol. 36 (2004), pp.341-354.

DOI: 10.1016/s0167-9236(03)00025-3

[11] S. H. Liao, J. L. Chen, T. Y. Hsu: Ontology-based data mining approach implemented for sport marketing, Expert Systems with Applications, Vol. 36 (2009), p.11045–11056.

DOI: 10.1016/j.eswa.2009.02.087

[12] S. S. Sane, A. Shirke: Generating OWL Ontologies from a Relational Databases for the Semantic Web, ICAC3(2009), pp.157-162.

DOI: 10.1145/1523103.1523136