A group decision making procedure for selecting data warehouse systems

Authors:
Santoso Wibowo;Hepu Deng;Xibao Zhang
Affiliations:
Faculty of Business Informatics, Central Queensland University, Melbourne, Victoria, Australia;School of Business IT & Logistics, RMIT University, Melbourne, Victoria, Australia;Department of International Economics and Trade, Qingdao University, Qingdao, China
Venue:
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Year:
2012

Citing 6
Cited 0

Interval valued intuitionistic fuzzy sets

Fuzzy Sets and Systems
Critical factors influencing the adoption of data warehouse technology: a study of the banking industry in Taiwan

Decision Support Systems
A fuzzy-based decision-making procedure for data warehouse system selection

Expert Systems with Applications: An International Journal
An empirical investigation of the factors affecting data warehousing success

MIS Quarterly
Intelligent decision support for effectively evaluating and selecting ships under uncertainty in marine transportation

Expert Systems with Applications: An International Journal
Multiattribute decision making based on interval-valued intuitionistic fuzzy values

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Evaluating and selecting the most suitable data warehouse system for development is complex and challenging. To effectively solve this problem, this paper presents a group decision making procedure for evaluating and selecting data warehouse systems. The subjectiveness and imprecision of the decision making process are adequately modeled by the use of interval-valued based intuitionistic fuzzy numbers. The concept of ideal solutions is adopted for determining the overall performance of each alternative data warehouse system across all the selection criteria on which the decision is made. An example is presented for demonstrating the applicability of the proposed procedure for solving real world data warehouse system selection problems.