• Asia pacific journal of information systems
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• v.10 no.3
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• pp.79-103
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• 2000

Data warehouse is an intelligent store of data. A metadata is critical for implementing data warehouse. Integrating data warehouse with its metadata helps create a more adaptive information system. This paper proposes a metadata-oriented data warehouse architecture that consists of seven components: legacy, extracting, operational data store, data warehouse, data mart, application, and metadata. A taxonomy for dataflow and metaflow is proposed for better understanding of the architecture. In addition, a metadata schema is built within the framework of the seven components. Lastly, the architecture with its metadata component is applied to a real-life data warehouse project for a large medical center in order to illustrate its practical usefulness.

• International Commerce and Information Review
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• v.7 no.1
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• pp.55-74
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• 2005

Recently, many enterprises have attempted to construct data warehousing systems for decision-support. Data warehouse is an intelligent store of data that can aggregate vast amounts of information. Building DW requires two important development issues:(i) DW for the decision making of business users and (ii) metadata within it. Most DW development methodologies have not considered metadata development; it is necessary to adopt a DW development methodology which develops a DW and its metadata simultaneously. Metadata is a key to success of data warehousing system and is critical for implementing DW. That is, metadata is crucial documentation for a data warehousing system where users should be empowered to meet their own information needs; users need to know what data exists, what it represents, where it is located, and how to access it. Furthermore, metadata is used for extracting data and managing DW. However, metadata has failed because its management has been segregated from the DW development process. Metadata must be integrated with data warehousing systems. Without metadata, the decision support of DW is under the control of technical users. Therefore, integrating data warehouse with its metadata offers a new opportunity to create a more adaptive information system. Therefore, this paper proposes a DW development methodology from a metadata perspective. The proposed methodology consists of five phases: preparatory, requirement analysis, data warehouse (informational database) development, metastore development, and maintenance. To demonstrate the practical usefulness of the methodology, one case is illustrated

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.225-233
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• 2007

This paper uses the taxonomy of 3 metadata schema(extraction metadata, warehouse, metadata, and user access metadata) to investigate how to manage metadata and what metadata to manage in data warehouse. In particular, this paper focuses on two kinds of metadata(warehouse metadata and user access metadata) and studies a case about metadata management in a real business world

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.72-79
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• 2007

OMG's Common Warehouse Metamodel is now a single data warehouse metadata standard. Interchange of metadata between data warehousing tools and metadata repositories made easy and convenient by using CWM. In this paper, we present the origin, importance and architecture of CWM, and offer an application case.

• Proceedings of the Korea Database Society Conference
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• 1998.09a
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• pp.448-474
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• 1998

Data warehouse (DW) is important for analytical processing. Metadata is a key to its architecture. This paper proposes an architecture that consists of seven components. To illustrate data warehouse environment (DWE), this Paper proposes taxonomies having four flows. on the basis or the taxonomies and metadata, this paper proposes a methodology for building the data warehouse and metadata simultaneously. This integrated development methodology (IDM) consists of seven phases: (i) preparatory phase, (ii) requirement analysis phase, (iii) data warehouse development phase, (iv) operational data store development phase, (v) data mart development phase, (vi) metastore development phase, and (vii) maintenance phase. A metastore system is Proposed to help develop metadata interactively. An illustrative example is investigated to demonstrate the usefulness of IDM.

• Proceedings of the Korea Database Society Conference
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• 1999.10a
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• pp.113-118
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• 1999

본 연구는 통합병원정보시스템 (Integrated Hospital Information System) 에서 의료용 데이터 웨어하우스 (Medical Data Warehouse) 부분의 보안 프로토타입 시스템을 메타데이터 기반으로 설계하고 구현하는 데 주안점을 두었다. 특히, 의료용 데이터 웨어하우스 중에서도 임상 데이터 웨어하우스 (Clinical Data Warehouse) 에 초점을 두었으며 이에 대한 프로토타입은 ㅈ 병원에 적용되어서 개발되었다.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.383-392
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• 1999

오늘날의 의사결정을 지원하는 시스탬에 있어서 데이터 웨어하우스가 널리 활용되고 있다. 이러한 데이터웨어 하우스를 개발하는데 있어서 메타 데이터가 필수적인 요소로 활용되고 있다. 한편. 메타 데이터 연합 (Meta Data Coalition)에서 제정한 Metadata Interchange Specification (MDIS)는 이러한 메타 데이터의 표준으로 널리 활용되고 있다. 본 연구는 이러한 표준을 기반으로 한 메타 데이터 스키마를 제시하고 있다. 실제적인 개발에서 이러한 표준은 핵심웨어하우스 계층, 적용 계층, 사용자 탐험 계층 및 비즈니스 계층으로 구성되어 있다. 이러한 메타 데이터 스키마를 적용한 실제 시스템의 프로토타입을 본 연구에서 구현해 보았다.

• Information Systems Review
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• v.2 no.1
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• pp.51-69
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• 2000

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.383-392
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• 1999

오늘날의 의사결정을 지원하는 시스템에 있어서 데이터 웨어하우스가 널리 활용되고 있다. 이러한 데이터웨어 하우스를 개발하는데 있어서 메타 데이터가 필수적인 요소로 활용되고 있다. 한편, 메타 데이터 연합(Meta Data Coalition)에서 제정한 Metadata Interchange Specification (MDIS)는 이러한 메타 데이터의 표준으로 널리 활용되고 있다. 본 연구는 이러한 표준을 기반으로 한 메타 데이터 스키마를 제시하고 있다. 실제적인 개발에서 이러한 표준은 핵심웨어하우스 계층, 적용 계층, 사용자 탐험 계층 비즈니스 계층으로 구성되어 있다. 이러한 메타 데이터 스키마를 적용한 실제 시스템의 프로토타입을 본 연구에서 구현해 보았다.

• Journal of Information Technology Services
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• v.8 no.1
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• pp.1-9
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• 2009

It is essential for companies to manage massive data for dealing with large volume of transactions and customers' needs. To this end, the companies have operated data warehouse with many complex tools for data gathering and reporting to the end-users. However, the data from the heterogeneous tools at the various sources cannot be exchanged because of the different interfaces. Therefore, the data cannot be controlled with integrated manner, and furthermore the companies do not focus the quality of data resulting in the data quality problem. Thus, this study suggests how to manage massive data with a metadata. In particular, we investigate current status of metadata management, its appliance, and perspectives. The contribution of this research is to apply the metadata management system to the real world and to suggest its management procedure.