Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing

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 DS 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.

Asset Management System Processes: Implementation of Sensor and Artificial Intelligence

“…It suggests "artificial intelligence is to amplify the cognitive capabilities of the decision maker in converting tacit knowledge into explicit knowledge, integrating this explicit knowledge by analyzing it to detect new patterns and relations, and understanding the new knowledge by providing analogs and explanations [10]. " The implementation of artificial intelligence (AI) will help create an explicit knowledge of how a decision maker is making the end decision, and then create an algorithm in which a computer based system can make the same, or better, quality decisions without the intervention of a "tacit knowledge" user.…”

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“…8, No. 5;2015 Organizational knowledge-creation can be viewed as an upward spiral process, starting at the individual level moving up to the collective level, and then the organizational level. Sometimes it reaches the inter-organizational level (Nonaka, 1997).…”

“…It is the sum of what is known (tacit and explicit knowledge), and it resides in the intelligence and the competence of people (Nemati, Steiger, Iyer, & Herschel, 2002). It is an interdisciplinary concept of exact and inexact sciences (Sharara, 1997).…”

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