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doi:10.1016/S0167-9236(01)00141-5 
Copyright © 2002 Published by Elsevier Science B.V. All rights reserved.
Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing
Available online 13 January 2002.
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Abstract
Decision support systems (DSS) are becoming increasingly more critical to the daily operation of organizations. Data warehousing, an integral part of this, provides an infrastructure that enables businesses to extract, cleanse, and store vast amounts of data. The basic purpose of a data warehouse is to empower the knowledge workers with information that allows them to make decisions based on a solid foundation of fact. However, only a fraction of the needed information exists on computers; the vast majority of a firm's intellectual assets exist as knowledge in the minds of its employees. What is needed is a new generation of knowledge-enabled systems that provides the infrastructure needed to capture, cleanse, store, organize, leverage, and disseminate not only data and information but also the knowledge of the firm. The purpose of this paper is to propose, as an extension to the data warehouse model, a knowledge warehouse (KW) architecture that will not only facilitate the capturing and coding of knowledge but also enhance the retrieval and sharing of knowledge across the organization. The knowledge warehouse proposed here suggests a different direction for DSS in the next decade. This new direction is based on an expanded purpose of DSS. That is, the purpose of DSS in knowledge improvement. This expanded purpose of DSS also suggests that the effectiveness of a DSS will, in the future, be measured based on how well it promotes and enhances knowledge, how well it improves the mental model(s) and understanding of the decision maker(s) and thereby how well it improves his/her decision making.
Author Keywords: Knowledge warehouse; Data warehouse; Knowledge management; Decision support systems; Data mining; Intelligent analysis; Model analysis
Article Outline
- 1. Introduction
- 2. Knowledge management
- 3. DSS, IT, and AI support of knowledge management
- 3.1. Sharing tacit knowledge
- 3.2. Converting tacit knowledge to explicit knowledge
- 3.3. Knowledge leveraging: converting explicit knowledge to new knowledge
- 3.4. Learning new knowledge: converting explicit knowledge to implicit knowledge
- 4. Goals and requirements for knowledge warehousing
- 4.1. Knowledge storage and retrieval
- 4.2. Analysis task management
- 4.3. Feedback and storage of new knowledge
- 5. Knowledge warehouse architecture
- 5.1. Knowledge acquisition module
- 5.2. Feedback loops
- 5.3. Knowledge extraction, transformation and loading module
- 5.4. Knowledge warehouse storage module
- 5.5. Knowledge analysis workbench
- 5.6. Communication manager
- 6. Development and implementation of the knowledge warehouse architecture
- 7. Roadmap for future DSS research
- 8. Summary and conclusions
- Exhibit A
- References
- Vitae
