• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.811-830
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• 2022

Purpose: In this paper, we will discuss 1) prioritizing databases to be integrated; 2) which data elements should be emphasized in federated database integration; and 3) the degree of efficiency in the integration. This paper aims to lay the groundwork for building data governance by presenting guidelines for database integration using metrics to identify and evaluate the capabilities of the UK's air quality databases. Methods: This paper intends to perform relative efficiency analysis using Data Envelope Analysis among the multi-criteria decision-making methods. In federated database integration, it is important to identify databases with high integration efficiency when prioritizing databases to be integrated. Results: The outcome of this paper aims not to present performance indicators for the implementation and evaluation of data governance, but rather to discuss what criteria should be used when performing 'federated integration'. Using Data Envelope Analysis in the process of implementing intelligent data governance, authors will establish and present practical strategies to discover databases with high integration efficiency. Conclusion: Through this study, it was possible to establish internal guidelines from an integrated point of view of data governance. The flexiblity of the federated database integration under the practice of the data governance, makes it possible to integrate databases quickly, easily, and effectively. By utilizing the guidelines presented in this study, authors anticipate that the process of integrating multiple databases, including the air quality databases, will evolve into the intelligent data governance based on the federated database integration when establishing the data governance practice in the railway industry.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.139-148
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• 1998

CALS IDB (Integrated database) is one of core technologies that embodies the principle of a shared data environment for the life cycle related data in CALS environment. In this study, to successfully share the data, we first classified the data types employed in the CALS environment and then discussed the data heterogeneity issued in data integration processes. To effectively solve this heterogeneity, we proposed the federated database systems as a candidate system especially focusing on the major functions and core element technologies.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.95-103
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• 2008

Recently, the need for integration of military-related information is increasing. The fundamental problem for integrating existing information systems is semantic inconsistency. To solve this problem, the use of metadata is essential, because metadata is a key component for effective information integration. In this paper, we present a metadata-based data grid system for integrating military data sources. First, we investigate federated database approach and data grid approach for information integration, and deal with metadata-based data grid. Then we present a data grid system for the integration of military information.

• Database Research
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• v.34 no.3
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• pp.69-85
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• 2018

Many researches proposed federated query engines to perform query on several homogeneous or heterogeneous datasets simultaneously that significantly improve the quality of query results. The existing techniques allow querying only over a few heterogeneous datasets considering the static binding using the non-standard query. However, we observe that a simultaneous system considering the integration of heterogeneous metadata standards can offer better opportunity to generalize the query over any homogeneous and heterogeneous datasets. In this paper, we propose a transparent federated engine (DRAZ) to query over multiple data sources using SPARQL. In our system, we first develop the ontology for a non-RDF metadata standard based on the metadata kernel dictionary elements, which are standardized by the metadata provider. For a given SPARQL query, we translate any triple pattern into an API call to access the dataset of corresponding non-RDF metadata standard. We convert the results of every API call to N-triples and summarize the final results considering all triple patterns. We evaluated our proposed DRAZ using modified Fedbench benchmark queries over heterogeneous metadata standards, such as DCAT and DOI. We observed that DRAZ can achieve 70 to 100 percent correctness of the results despite the unavailability of the JOIN operations.