文档介绍：Decision Support Systems 33 (2002) 143–161
ate/dsw
Knowledge warehouse: an architectural integration of
knowledge management, decision support, artificial
intelligence and data warehousing
Hamid R. Nemati a,*, David M. Steiger b,1, Lakshmi S. Iyer c,2, Richard T. Herschel d,3
aBryan School of Business and Economics, University of North Carolina at Greensboro, 440 Bryan Building, Greensboro, NC 27412, USA
bThe Maine Business School, University of Maine, 5723 Donald P. Corbett Business Building, Orono, ME 04469-5723, USA
cBryan School of Business and Economics, University of North Carolina at Greensboro, 482 Bryan Building, Greensboro, NC 27412, USA
dErivan K. Haub School of Business, St. Joseph’s University, 5600 City Avenue, Philadelphia, PA 19131-1395, USA
Abstract
Decision support systems (DSS) are ing increasingly more critical to the daily operation anizations. 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 puters; 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 anization. The knowledge warehouse proposed
here suggests a different direction for DSS in the next decade. This new direction