Network Issue

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

Hamid R. Nematia, , David M. Steiger, b, 1, Lakshmi S. Iyer, c, 2 and Richard T. Herschel, d, 3

a Bryan School of Business and Economics, University of North Carolina at Greensboro, 440 Bryan Building, Greensboro, NC 27412, USA

b The Maine Business School, University of Maine, 5723 Donald P. Corbett Business Building, Orono, ME 04469-5723, USA

c Bryan School of Business and Economics, University of North Carolina at Greensboro, 482 Bryan Building, Greensboro, NC 27412, USA

d Erivan K. Haub School of Business, St. Joseph's University, 5600 City Avenue, Philadelphia, PA 19131-1395, USA

Available online 13 January 2002.

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...